This guide breaks down the top AI voice agent platforms and helps you understand how they compare across speed, pricing, latency, and ease of deployment. If you’re exploring tools that can automate phone calls or handle real-time customer conversations, this article gives you a simple overview of what AI voice agents are, why teams use them, and what to consider when choosing the right platform.
Top 4 AI voice agent platform shortlist
Retell AI: Best AI voice agent platform for teams that need real-time, low latency phone agents with transparent per-minute pricing and flexible telephony integrations.
PolyAI: Best for large enterprises that require multilingual, high containment voice assistants that plug directly into existing contact center infrastructures.
Bland AI: Best for organizations needing hyper-scalable, security-focused voice automation capable of supporting massive inbound and outbound call volumes.
Voiceflow: Best for teams that prioritize rapid prototyping, collaborative design, and building conversational flows across both voice and chat channels.
What is an AI voice agent?
An AI voice agent is software that uses speech recognition and generative AI to handle live phone conversations. It listens, understands natural language, takes action through backend systems, and responds with natural-sounding speech. Most tools combine real-time STT, an LLM, workflow logic, and TTS to automate support, sales, and operational calls.
Why teams use AI voice agents
Even with strong innovation across the space, buyers consistently explore different platforms for several practical reasons:
Pricing clarity varies widely: Some vendors offer transparent usage-based pricing, while others require enterprise scoping. If your team needs predictable modeling, opaque pricing can slow alignment and budget approvals.
Implementation models differ: Certain platforms depend heavily on partners, SIs, or external vendors for setup. That can benefit complex deployments but introduces extra coordination and recurring services that some teams prefer to own in-house.
Not all tools fit smaller teams: Many AI voice platforms are built for large contact centers, while others prioritize fast, self-serve iteration. If you’re optimizing for speed rather than depth, enterprise-oriented platforms may feel heavy.
Customization sometimes incurs add-on costs: Adjustments to models, voice configurations, or routing logic may require custom development fees depending on the vendor. This can reshape true cost of ownership for more advanced use cases.
Limited public reviews for some players: Many emerging voice platforms have few third-party reviews, making benchmarking difficult for teams who rely on external validation during procurement.
Stack dependencies vary: Some platforms are tightly integrated with a specific cloud provider or LLM ecosystem. Teams seeking multi-cloud options or model-agnostic flexibility often prefer vendors that document broader support.
Integration complexity can differ dramatically: Connecting voice agents to CRMs, telephony systems, data warehouses, or marketing tools ranges from plug-and-play to custom-build depending on the vendor. This often affects rollout timelines.
Initial setup and operator training: Deploying and maintaining an AI voice agent platform may involve an early learning curve, especially for organizations new to conversational design or multi-step automation.
How to evaluate AI voice agent platforms
Choosing the right AI voice agent platform comes down to how well it matches your latency needs, integration requirements, budget, and team workflow. While most tools share similar core components, they differ significantly in performance, flexibility, pricing clarity, and how quickly you can get them into production.
Use the criteria below to benchmark each platform before making a decision.
Key Evaluation Criteria
Latency: Sub-second responsiveness is essential for natural conversations.
Voice Quality: Human-like, stable speech with interruption handling and emotional cues.
Pricing Transparency: Clear pricing makes procurement and forecasting easier.
Ease of Deployment: How long it takes to go from zero to a working agent.
Control & Customization: Ability to adjust logic, voice, models, and routing without vendor dependence.
Integrations: Native support for CRMs, telephony, data systems, and APIs.
Scalability: Whether the platform can reliably handle your expected call volume.
Compliance: Requirements like SOC 2, HIPAA, or GDPR depending on your industry.
Observability: Access to transcripts, analytics, summaries, and agent performance metrics.
Support Model: Vendor-led, partner-led, or self-serve workflows.
AI Voice Agent Platform Evaluation Matrix
Criteria
Why It Matters
What to Look For
Latency
Determines how natural and interruption-friendly conversations feel.
Sub-second response times; stable performance under load.
Voice Quality
Impacts caller trust and overall user experience.
Realistic TTS, emotion handling, and barge-in support.
Pricing Transparency
Helps teams model costs and avoid surprises during scale.
Clear per-minute or per-conversation pricing; no hidden fees.
Ease of Deployment
Influences speed to value and iteration cycles.
No-code builders, templates, and simple telephony setup.
Customization
Enables tuning behavior to your workflows and brand voice.
Edit logic, voices, models, routing rules without vendor reliance.
Integrations
Connects agents to the systems they need to take action.
Native CRM/telephony connectors; flexible API actions.
Scalability
Ensures reliability during peak call periods.
Proof of large-scale calling; infrastructure built for concurrency.
Compliance
Required for regulated industries like healthcare and finance.
SOC2, HIPAA, GDPR, data residency, encryption guarantees.
Affects how quickly you can fix issues or ship updates.
Responsive vendor support or true self-serve flexibility.
Top 10 AI voice agent platforms in 2025
1. Retell AI
Retell AI is widely recognized as one of the strongest platforms for building real-time AI voice agents. Whether you care most about advanced features, responsiveness, or cost efficiency, Retell offers a balanced mix of all three. The platform is built to help teams automate live phone conversations with natural-sounding voice interactions, predictive intelligence, and adaptive workflows that adjust to customer needs in real time.
Retell provides a developer-first environment with an intuitive drag-and-drop builder for designing, deploying, and managing voice agents quickly. It supports leading LLMs, multilingual voices, real-time logic, and smooth integrations with telephony providers like Twilio.
Because Retell handles multiple languages and diverse voice models, it’s a strong fit for global teams that need broad audience coverage and consistent customer experiences.
To simplify the evaluation process, Retell also includes pre-made templates for use cases such as lead qualification, appointment scheduling, routing, and customer support.
Key Benefits
Retell stands out for its speed, flexibility, and usability, making it an attractive choice for teams wanting to launch voice agents without long onboarding cycles or heavy engineering lift.
Transparent pricing: Retell uses a clear, usage-based billing model. You pay for what you use and nothing else. No enterprise-only pricing walls or opaque minimums — just predictable per-minute and per-model costs.
Built for voice-first performance: Retell’s architecture is purpose-built for live phone calls. Sub-second latency, natural turn-taking, and interruption handling create conversations that feel genuinely human.
Fast deployment with no partner requirements: You can design, test, and release agents in a matter of hours. There’s no need for external implementation partners, making it ideal for teams experimenting with AI receptionists, outbound automation, or call-handling pilots.
Real-time analytics: Every interaction is automatically transcribed, summarized, and evaluated for sentiment and performance. These insights help teams iterate quickly and improve call quality — functionality that many general-purpose platforms lack.
Flexible integrations: Retell connects directly with telephony systems like Twilio or SIP trunks, and plugs into CRMs like Salesforce and HubSpot. This makes it easy to slot into your existing stack without re-architecting workflows.
Model and voice control: Mix models from OpenAI, Anthropic, and others, and choose from premium voice providers like ElevenLabs or Play.ht. Each agent can use the model and voice best suited for the use case.
Low maintenance, scalable infrastructure: Retell automatically scales across high-volume calling without complex configuration or multiple infrastructure layers.
Compliance-ready: Retell meets SOC 2, HIPAA, and GDPR requirements, making it suitable for organizations in regulated sectors.
Cons
Requires some comfort with configuring LLMs and prompts to get the best results.
Telephony and LLM usage can still add up at very high volumes, so you need basic cost monitoring.
Pricing
Retell is one of the most affordable enterprise-grade voice AI platforms. Usage-based pricing starts around $0.07 per connected minute, with enterprise discounts dropping to roughly $0.05 per minute at scale. You only pay for active call time — not idle minutes.
Number rental starts at $2 per month for local numbers and $5 per month for toll-free numbers. The company also provides a $10 testing credit and limited concurrent calls for early pilots.
Customer Feedback: “Retell AI has completely transformed the way we manage automated calls, with impressive voice quality and understanding.”
Recommended For
Teams that want a highly flexible, transparent, and scalable voice AI agent system tailored to real-time phone interactions. Especially well-suited for product teams, call centers, and outbound sales groups looking for predictable pricing and fast iteration cycles.
Build your first Retell agent in minutes for free.
2. PolyAI
PolyAI focuses on multilingual customer support, call containment, and AI answering services for companies that serve global audiences. It plugs into existing contact center stacks and CRMs, and offers deep analytics, configurable voice experiences, and relatively fast time to go live.
Key Benefits
Pre-trained domain assistants: PolyAI provides ready-made assistants for use cases like authentication, billing questions, order lookups, reservations, and routing. These can be customized, but they give you a strong starting point without heavy training.
High call containment from day one: In production environments, PolyAI has reported call containment rates above 80 percent, with up to 87 percent of calls successfully handled early in deployments, while still allowing smooth handoff to human agents when needed.
Human-like voice and language handling: The system is tuned for natural speech. Callers can talk freely, interrupt, change topics, and blend phrases while the assistant maintains context and continues the conversation smoothly.
Multilingual and globally ready: PolyAI supports a wide range of languages and accents, which makes it a strong option for companies with international customers and regional markets.
Enterprise certifications and regulated use cases: PolyAI highlights certifications such as SOC 2 and is designed to be deployed in regulated spaces like healthcare and financial services.
Strong CRM and contact center integrations: It integrates with major contact center and CRM platforms so teams can keep their existing stack and still add advanced voice automation.
Cons
High minimum contract size and custom pricing make it inaccessible for many smaller teams.
Heavier implementation and customization cycles compared to more self serve tools.
Strongest value shows up in large, complex contact center environments rather than small pilots.
Pricing
PolyAI uses custom, usage-based enterprise pricing with relatively high starting thresholds, typically beginning around 150,000 dollars per year. Exact rates depend on volume and configuration and are not listed publicly.
Review: "There are many options for AI currently in the market. PolyAI impressed us by providing a product that could be launched in a short amount of time without risking quality".
Recommended For
Large enterprises and contact centers that want a fully managed, highly customized voice AI solution with strong multilingual capabilities.
3. Bland AI
Bland AI is focused on highly realistic voice interactions and strict security and governance. It handles large-scale inbound and outbound calling, SMS, and broader omnichannel workflows, which makes it well suited for enterprise telemarketing, notifications, and transactional calls.
The company highlights its ability to scale to as many as one million concurrent calls, which appeals to organizations that require significant resiliency and throughput.
Key Benefits
Extreme scalability: Bland is engineered for very high call volumes, supporting up to one million concurrent calls, which is far beyond what many typical setups can accommodate.
Granular conversational control: The Conversational Pathways feature allows teams to design detailed dialog flows that mix scripted and generative responses, giving more control over what the agent can and cannot say.
Proprietary voice and model stack: Bland runs its own speech and reasoning models rather than depending entirely on third parties, giving it greater control over latency, quality, and reliability.
Multi-region deployment and data control: Customers can choose data processing regions to support GDPR and local privacy rules. This is especially attractive to industries like healthcare and finance that have strict compliance needs.
Built-in omnichannel capabilities: Bland supports voice, SMS, and chat from the same platform, enabling scenarios such as order tracking, follow-up messaging, and inventory updates across channels.
Cons
No public pricing and clear enterprise focus can be a barrier for mid market or smaller teams.
Setup and customization typically require more engineering involvement.
Designed for extreme scale, which can feel like overkill for simpler or lower volume use cases.
Pricing
Pricing is not published. Bland positions itself for large enterprise deals, with costs that reflect its customisation and scale.
Product Hunt Rating: 3 / 5 (10 reviews)
Best For
Large enterprises that have tight requirements around privacy, governance, and brand voice, and that operate at very high call volumes.
4. Voiceflow
Voiceflow is a no-code platform for designing conversational flows across voice and chat. It is particularly strong for prototyping, collaboration, and iterating on agent experiences.
Key Benefits
Rapid prototyping and iteration: The drag-and-drop builder and modular components allow teams to create and refine agents in hours, which is much faster than large, enterprise-first platforms.
Collaboration-centric design: Voiceflow includes real-time collaboration, shared workspaces, commenting, and role-based permissions so designers, product teams, and engineers can work together smoothly.
Technology agnostic: You can plug in any LLM, API, backend system, or data source, which reduces vendor lock-in and gives you flexibility as the AI landscape shifts.
Support for voice and chat: From a single interface, you can build agents that respond over both voice and text. This simplifies management of multichannel experiences and helps keep behavior consistent.
Enterprise security features: Despite its emphasis on design, Voiceflow offers SOC 2 and ISO 27001 compliance, along with permissions and guardrails for enterprise teams.
Cons
Out of the box, it is more of a design and orchestration layer than a full telephony stack.
Costs can climb quickly at higher usage or with larger teams of editors.
You still need to wire in underlying LLMs, calling infrastructure, and evaluation if you want production grade agents.
Pricing
Voiceflow provides a free tier for basic usage. The Pro plan begins at 60 dollars per editor per month for up to 20 agents. The Business plan at 150 dollars per editor per month unlocks unlimited agents, with enterprise pricing available on request.
Review: "Good platform if you have less than 5,000 chats per month, otherwise extremely expensive".
Best For
Startups, design teams, and innovation groups that value speed of experimentation and cross-team collaboration more than extremely high call concurrency.
5. Sierra AI
Sierra AI builds advanced customer service agents that are trained to align closely with a company’s brand identity and policies. The focus is on agents that can reason, act, and communicate in a way that feels true to the brand.
Key Benefits
Action-oriented agents: Sierra agents integrate with backend systems like CRMs, subscription tools, and order platforms so they can perform tasks such as updating records or processing returns, not just respond with information.
Multi-model architecture: Sierra uses a constellation of models, including options like OpenAI, Anthropic, and Meta. This multi-model setup is designed to boost reliability, reduce hallucinations, and provide fallbacks.
Voice plus omnichannel: Sierra added voice capabilities in 2024, so the same agents can now handle natural phone conversations with interruptions and realistic cadence while also working across other channels.
Guardrails and governance: Strong controls for policy enforcement, data access, and auditing are central to the platform. You can trace and manage how decisions are made, which is crucial for long-term compliance.
Brand-level tuning: Teams can shape tone, vocabulary, and context handling so the agent sounds and behaves like the brand, which is especially important for consumer-facing companies.
Cons
High starting price puts it firmly in the enterprise bucket.
Setup can be complex and requires cross functional alignment across data, policy, and brand.
Reported bugs and rough edges compared to some more focused voice players.
Pricing
Pricing for Sierra generally starts around 150,000 dollars per year, with final costs set based on agent complexity and interaction volume. The idea is to deliver sophisticated, brand-aligned automation at a lower total cost of ownership than some legacy enterprise platforms.
Review: “User friendly, fast and many supported languages. Very complex setup process and more bugs then competitors”.
Recommended For
Customer-focused brands in areas like telecom and financial services where consistent tone, compliance, and policy adherence are essential.
6. Replicant
Replicant is an enterprise automation platform designed for contact centers and support-heavy organizations. Its Thinking Machine is built to resolve Tier 1 customer calls independently, escalate when appropriate, and integrate with backend systems.
Key Benefits
Resolution-first philosophy: Replicant aims to solve customer issues end-to-end instead of just routing or deflecting calls, which can significantly reduce the load on live agents.
Voice plus other channels: The platform supports voice, chat, and SMS automation, enabling consistent experiences across multiple touchpoints.
Hands-on implementation support: Customers frequently call out Replicant’s responsiveness and collaborative approach during deployment as a major strength.
Enterprise scale: Replicant’s technology has been deployed in real contact centers with high call volumes and complex workflows, demonstrating proven scalability.
Analytics and insights built in: The platform provides call summaries, trends, and performance metrics so teams can continuously improve automation outcomes.
Cons
No transparent pricing and an enterprise sales motion can slow down experimentation.
Implementation typically requires a formal project rather than a lightweight self serve trial.
Strong focus on support and contact center workloads, less suited for small, experimental teams.
Pricing
Replicant does not share standard pricing publicly. Engagements are structured as enterprise contracts, tailored to call volume, complexity, and required integrations.
Review: "The team is quick to reply if there are any technical concerns and is open to feedback. They usually respond within an hour when a ticket is sent in".
Recommended For
Large contact centers that want to automate a substantial portion of inbound volume with a partner that has deep experience in voice automation.
7. ElevenLabs
ElevenLabs is best known for its high quality text-to-speech and voice cloning technology, and has recently moved into conversational AI agents. Its tools can take textual or spoken input, ground it in your data, and deliver highly natural voice responses.
While it is not a full telephony stack on its own, it is a strong option for brands that already have an audio focus and want cutting-edge voice quality.
Key Benefits
Highly realistic voice synthesis: ElevenLabs produces voices that are very close to human, capturing subtle tone and rhythm so audio feels natural rather than synthetic.
Advanced voice cloning: It can clone voices using relatively small audio samples, allowing companies to create distinctive voice identities for their brand or products.
Multilingual and cross-language support: ElevenLabs supports many languages and can handle dubbing or translation while preserving the original voice’s character.
Flexible voice design controls: Teams can adjust attributes such as accent, age, gender, and style and fine tune parameters to align with their brand tone.
Cons
Not a full voice agent or telephony platform on its own, you still need other tools for call flows and routing.
Credit based pricing can be hard to forecast at scale without careful monitoring.
Compliance posture is more limited than platforms built specifically for regulated, enterprise environments.
Pricing
ElevenLabs operates on a credit-based system. You purchase credits that can be used for TTS, agents, and other capabilities, and buy more as needed.
Example tiers include:
Free: 10,000 credits per month (roughly 10 minutes of high quality TTS or about 15 minutes of agent time)
Starter: 5 dollars per month for 30,000 credits
Higher tiers for creators, pros, business, and enterprise with increasing credit allowances, priority, and SLAs
Total cost depends on how many minutes of audio and agents you run and what quality levels you select.
Recommended For
Teams that heavily care about voice quality, expressiveness, and branding, such as those working on podcasts, narration, gaming, or voice-first apps. For full telephony routing and complex call workflows, it is usually paired with other platforms.
8. Synthflow AI
Synthflow AI is a scalable voice AI platform that combines a no-code workflow builder, real-time personalization, and strong CRM integrations. It supports HIPAA compliance, inbound routing, and multi-tenant setups, which makes it especially attractive for agencies and teams running multiple clients on a single system.
Key Benefits
Straightforward pricing: Synthflow AI uses a clear pay-as-you-go structure at around 0.08 dollars per minute, which makes budgeting easier than platforms that hide costs behind enterprise quotes.
Fast time to value: The visual builder and pre-built templates allow teams to launch voice agents in weeks without heavy implementation projects, ideal for AI appointment setters or call routing assistants.
Low latency and smooth calls: Synthflow AI targets sub-500 ms response times so conversations feel natural and do not suffer from awkward pauses.
Voice cloning and multilingual support: You can clone voices, adjust tone, and support more than 50 languages so brands can offer localized, on-brand experiences in different regions.
Bring-your-own-carrier: Synthflow AI connects with Twilio, SIP trunks, and existing phone systems so you can keep your current telephony stack and layer AI on top.
Advanced generative features: The platform uses generative AI for dynamic, personalized interactions instead of rigid IVR menus.
Cons
Feature set is optimized for common workflows; very complex enterprise scenarios may hit limits.
No code builders can hide complexity, making debugging harder once flows get large.
Built in opinionated patterns that may not match every team’s preferred architecture.
Pricing
Starter: 29 dollars per month for 5,000 minutes and 1 agent
Growth: 99 dollars per month for 20,000 minutes and unlimited agents
Scale: 249 dollars per month for 60,000 minutes
Custom enterprise pricing is available for higher volumes
Review: “What I like best about Synthflow is that it doesn’t bury you in technical complexity. You don’t need to be a coder or spend weeks wiring together APIs just to get a usable AI voice agent”.
Recommended For
Marketing teams, agencies, and enterprises that need compliant automation, robust inbound voice flows, and deep integrations without a large engineering team.
Ada.cx provides AI agents that automate customer service across voice, chat, and email so support teams can handle more volume without expanding headcount at the same pace. The platform is built AI-first rather than around rigid, scripted bots, which helps it deal with more open-ended customer questions.
Key Benefits
Unified AI Agent across channels: Ada runs voice, chat, and email under one AI layer, so you do not have to manage separate tools for each channel.
Knowledge-driven generative answers: By connecting to your knowledge base and documentation, Ada can generate responses on the fly, reducing manual intent training and speeding up deployment.
Natural, low-latency voice calls: Ada’s voice agents handle interruptions, pauses, and open questions, which makes interactions feel closer to speaking with a human.
Pay-for-resolution pricing: Instead of charging only by minutes or traffic, Ada offers pricing tied to resolved conversations, aligning cost with value.
Enterprise compliance and scale: Ada supports SOC 2, HIPAA, and GDPR and is designed to support high-volume, regulated environments.
Review: “Ada helped our small support team contain the most easy-to-resolve customer inquiries, freeing-up more time for agents to go through our backlog.”
Pricing
Ada uses performance-based pricing, usually tied to successful resolutions or interaction volume. Final pricing depends on monthly conversations, integrations, and channels, but most enterprise plans start in the low six figures per year.
Recommended For
Brands that prioritize high quality customer experience at scale, especially in e-commerce, fintech, and telecom, where multilingual support and fast automation setup matter.
Decagon.ai offers a unified AI engine that resolves customer issues across chat, voice, email, SMS, and custom channels. Its core concept is Agent Operating Procedures (AOPs), which are natural-language instructions that compile into logic, giving teams a faster and more flexible way to define agent behavior.
Key Benefits
Agent Operating Procedures (AOPs): You describe business logic in plain language and Decagon compiles it into code. Non-technical teams can adjust behavior quickly while engineers maintain control over guardrails.
Unified omnichannel plus voice: Decagon supports digital channels and voice through Decagon Voice, so you do not need one stack for chat and a separate one for telephony.
Custom voice and brand tuning: Integration with ElevenLabs allows for very natural, brand-aligned voices with control over tone and pronunciation.
Deep action and integration layer: Agents can call APIs, trigger workflows, and connect to tools like Stripe for billing or refunds, so they can actually complete tasks instead of only answering questions.
Observability and decision traceability: Every decision path is logged, versioned, and auditable, which simplifies debugging and ongoing improvement.
Rapid deployment and strong ROI: AOPs and a model-agnostic architecture help teams go live faster than building dialog flows from scratch, and case studies cite support cost reductions of up to 65 percent.
Pricing
Decagon positions pricing around value with two main models:
Per conversation: A fixed fee per interaction, whether fully resolved or not
Per resolution: You pay only when the agent resolves the issue without human escalation
Because Decagon targets enterprises with larger volumes, pricing is custom. Public references put many deployments between roughly 95,000 and 590,900 dollars per year, depending on volume and complexity.
Review: "The biggest upside of using Decagon isn't simply the assumption of repetitive day-to-day tasks that would normally be done manually, but that Decagon allows us to evaluate data on a much deeper level."
Recommended For
Enterprises that want highly customizable, transparent, and outcome-focused automation, especially in fintech, telecom, and SaaS with heavy support demand.
Fastest setup, most flexible stack, strong analytics
PolyAI
Large enterprises, multilingual support
High performance, tuned for call containment
Custom enterprise quotes only
Weeks to deploy
Strong contact center & CRM integrations
Human-like, multilingual voices
SOC 2, regulated industry ready
Call containment >80%, domain-trained agents
Bland AI
Enterprises needing scale + governance
Extremely robust (up to 1M concurrent)
No public pricing
Weeks to months
API-first; custom infrastructure
Hyper-realistic proprietary voices
GDPR, HIPAA-friendly
Massive concurrency, strong control & security
Voiceflow
Design teams, prototyping, collaboration
Good for testing, varies in production
Clear plan-based pricing
Hours to days
Any LLM, any API (most flexible design tool)
Varies, strong for design not telephony
SOC 2, ISO 27001
Best flow builder & collaboration features
Sierra AI
Brands needing tone control & governance
Strong, multi-model fallback
Enterprise-only
Weeks to deploy
Deep backend integration & policy controls
Natural, consistent brand-aligned
Enterprise-grade governance
Best for brand tone alignment & compliance
Replicant
Large contact centers, high-volume support
Enterprise-grade performance
No public pricing
Enterprise deployment cycle
Strong CCaaS + CRM integrations
High quality but contact-center tuned
Enterprise-level safety & controls
Resolution-first engine, strong support model
ElevenLabs
Voice quality, branding, audio products
High performance TTS
Clear credit-based pricing
Minutes to integrate
API-first voice engine
Best-in-class TTS & cloning
Not a full compliance stack
Unmatched voice realism
Synthflow AI
Agencies, marketing teams, SMB automation
Sub-500 ms
⭐ Very transparent (plan + usage)
Weeks (fast, no-code)
Twilio, SIP, CRMs
Good quality, multilingual
HIPAA support
Affordable at scale, strong builder
Ada.cx
Enterprise CX teams, omnichannel automation
Strong, consistent
Enterprise-only, usage-based
Weeks
CRM, CX, support stack
Natural, intent-aware
SOC 2, HIPAA, GDPR
Pay-for-resolution model, strong KB grounding
Decagon.ai
Enterprises needing deep action-taking AI
Strong, model-agnostic
Custom (resolution or conversation based)
Weeks
APIs, Stripe, backend workflows
ElevenLabs-powered voices
Enterprise governance
AOPs, deep integrations, strong ROI claims
Saving sales time with AI voice agents
I started looking into AI voice agents because our sales team was drowning in pointless first-touch calls — missed follow-ups, no-shows, bad leads, you name it. We tried a couple of platforms and they all sounded like robots reading from a script. Slow, awkward, and definitely not something I’d trust with a prospect.
Out of frustration, I rebuilt our entire first-touch workflow using Retell AI for the calls and Vellum for the logic. The first test call shocked me. Retell actually sounded human and replied instantly. And with Vellum, I could change the pitch flow, objection handling, qualification steps, everything, without waiting on an engineer.
Within a week, that setup was qualifying leads, booking meetings, and handling all the “just checking in” calls our reps hated. And it did it without burning prospects with weird pauses or scripted answers.
Retell handled the live conversation. Vellum handled the brain. Together, it finally felt like an AI agent I could trust to talk to prospects without embarrassing us.
The biggest takeaway from comparing these platforms is that AI voice agents are only as good as their fit for your actual use case. Some excel at real time phone performance, others at multilingual support or strict governance, and some focus on fast iteration and clear pricing.
If you want the right outcome, ignore the hype and focus on the basics: latency, pricing clarity, integration flexibility, and how quickly you can make changes without relying on a vendor. Pick a platform that matches the calls you need to run, run a small pilot, listen to the calls, and iterate. That simple approach consistently outperforms choosing the platform with the longest feature list.
1. How is an AI voice agent different from a traditional IVR system?
Traditional IVRs route callers through fixed menus and keypad options. An AI voice agent lets callers speak naturally, understands intent, accesses backend systems, and can resolve requests end to end instead of just forwarding calls to a queue.
2. What kinds of use cases are AI voice agents actually good at today?
They are strongest at high volume, repeatable workflows like lead qualification, appointment scheduling, order status, basic troubleshooting, payment reminders, and routing. More complex or sensitive issues are usually better handed off to human agents.
3. How should I think about latency when choosing a platform?
Anything over a second of delay between a caller finishing a sentence and the agent responding will feel awkward. When evaluating platforms, test real calls and listen for overlap handling, interruptions, and how quickly the agent recovers after someone talks over it.
4. Can AI voice agents integrate with my existing telephony and CRM stack?
Most modern platforms support SIP, Twilio, or native carrier integrations and can connect to CRMs like Salesforce or HubSpot through APIs. Before you choose a vendor, confirm how they handle caller ID, contact syncing, and logging calls or transcripts into your existing systems.
5. How do I keep AI voice agents from “hallucinating” or giving wrong answers?
You reduce hallucinations by grounding the agent in your own data, enforcing clear guardrails, and testing prompts against real calls. Platforms that let you control prompts, tools, and retrieval logic directly make it easier to debug and correct behavior over time.
6. What are the main security and compliance questions I should ask vendors?
Ask where data is stored, how long call recordings and transcripts are retained, whether you get data residency options, and which certifications they hold (SOC 2, HIPAA, GDPR). You should also understand how access is controlled and how to delete or export data if you churn.
7. How do I measure if an AI voice agent deployment is successful?
Track containment rate, resolution rate, transfer rate to humans, customer satisfaction, average handle time, and cost per resolved interaction. Listen to a sample of calls weekly and pair the data with qualitative review so you can see why metrics are moving.
8. How big does my team need to be to manage an AI voice agent in production?
You do not need a huge team, but you do need clear owners. In most cases one person on ops or product, one engineer for integrations, and one stakeholder from support or sales is enough to run pilots and keep things healthy once the flows are stable.
9. Can AI voice agents handle sales calls, or are they only for support?
They can do both, but the responsibilities are different. For sales, agents are best at qualification, follow ups, and scheduling, not closing. A good pattern is to let the AI handle first touch, gather context, and then hand off warm, qualified conversations to human reps.
10. How should I run a pilot before committing to a platform long term?
Pick one narrow use case, define success metrics up front, and cap the call volume. Run the agent against real traffic for a few weeks, review transcripts daily, and iterate on prompts and flows. If you cannot ship changes quickly during the pilot, that is a red flag.
11. What is the advantage of pairing a voice platform like Retell with a workflow layer like Vellum?
A voice platform gives you low latency calling, telephony, and audio quality. A workflow layer like Vellum controls prompts, tools, and evaluation. Together you can experiment on the “brain” of the agent while keeping the voice and telephony stable, which makes it much easier to improve performance without ripping out your entire stack.
Quick overview
This guide breaks down the top AI voice agent platforms and helps you understand how they compare across speed, pricing, latency, and ease of deployment. If you’re exploring tools that can automate phone calls or handle real-time customer conversations, this article gives you a simple overview of what AI voice agents are, why teams use them, and what to consider when choosing the right platform.
Top 4 AI voice agent platform shortlist
Retell AI: Best AI voice agent platform for teams that need real-time, low latency phone agents with transparent per-minute pricing and flexible telephony integrations.
PolyAI: Best for large enterprises that require multilingual, high containment voice assistants that plug directly into existing contact center infrastructures.
Bland AI: Best for organizations needing hyper-scalable, security-focused voice automation capable of supporting massive inbound and outbound call volumes.
Voiceflow: Best for teams that prioritize rapid prototyping, collaborative design, and building conversational flows across both voice and chat channels.
What is an AI voice agent?
An AI voice agent is software that uses speech recognition and generative AI to handle live phone conversations. It listens, understands natural language, takes action through backend systems, and responds with natural-sounding speech. Most tools combine real-time STT, an LLM, workflow logic, and TTS to automate support, sales, and operational calls.
Why teams use AI voice agents
Even with strong innovation across the space, buyers consistently explore different platforms for several practical reasons:
Pricing clarity varies widely: Some vendors offer transparent usage-based pricing, while others require enterprise scoping. If your team needs predictable modeling, opaque pricing can slow alignment and budget approvals.
Implementation models differ: Certain platforms depend heavily on partners, SIs, or external vendors for setup. That can benefit complex deployments but introduces extra coordination and recurring services that some teams prefer to own in-house.
Not all tools fit smaller teams: Many AI voice platforms are built for large contact centers, while others prioritize fast, self-serve iteration. If you’re optimizing for speed rather than depth, enterprise-oriented platforms may feel heavy.
Customization sometimes incurs add-on costs: Adjustments to models, voice configurations, or routing logic may require custom development fees depending on the vendor. This can reshape true cost of ownership for more advanced use cases.
Limited public reviews for some players: Many emerging voice platforms have few third-party reviews, making benchmarking difficult for teams who rely on external validation during procurement.
Stack dependencies vary: Some platforms are tightly integrated with a specific cloud provider or LLM ecosystem. Teams seeking multi-cloud options or model-agnostic flexibility often prefer vendors that document broader support.
Integration complexity can differ dramatically: Connecting voice agents to CRMs, telephony systems, data warehouses, or marketing tools ranges from plug-and-play to custom-build depending on the vendor. This often affects rollout timelines.
Initial setup and operator training: Deploying and maintaining an AI voice agent platform may involve an early learning curve, especially for organizations new to conversational design or multi-step automation.
How to evaluate AI voice agent platforms
Choosing the right AI voice agent platform comes down to how well it matches your latency needs, integration requirements, budget, and team workflow. While most tools share similar core components, they differ significantly in performance, flexibility, pricing clarity, and how quickly you can get them into production.
Use the criteria below to benchmark each platform before making a decision.
Key Evaluation Criteria
Latency: Sub-second responsiveness is essential for natural conversations.
Voice Quality: Human-like, stable speech with interruption handling and emotional cues.
Pricing Transparency: Clear pricing makes procurement and forecasting easier.
Ease of Deployment: How long it takes to go from zero to a working agent.
Control & Customization: Ability to adjust logic, voice, models, and routing without vendor dependence.
Integrations: Native support for CRMs, telephony, data systems, and APIs.
Scalability: Whether the platform can reliably handle your expected call volume.
Compliance: Requirements like SOC 2, HIPAA, or GDPR depending on your industry.
Observability: Access to transcripts, analytics, summaries, and agent performance metrics.
Support Model: Vendor-led, partner-led, or self-serve workflows.
AI Voice Agent Platform Evaluation Matrix
Criteria
Why It Matters
What to Look For
Latency
Determines how natural and interruption-friendly conversations feel.
Sub-second response times; stable performance under load.
Voice Quality
Impacts caller trust and overall user experience.
Realistic TTS, emotion handling, and barge-in support.
Pricing Transparency
Helps teams model costs and avoid surprises during scale.
Clear per-minute or per-conversation pricing; no hidden fees.
Ease of Deployment
Influences speed to value and iteration cycles.
No-code builders, templates, and simple telephony setup.
Customization
Enables tuning behavior to your workflows and brand voice.
Edit logic, voices, models, routing rules without vendor reliance.
Integrations
Connects agents to the systems they need to take action.
Native CRM/telephony connectors; flexible API actions.
Scalability
Ensures reliability during peak call periods.
Proof of large-scale calling; infrastructure built for concurrency.
Compliance
Required for regulated industries like healthcare and finance.
SOC2, HIPAA, GDPR, data residency, encryption guarantees.
Affects how quickly you can fix issues or ship updates.
Responsive vendor support or true self-serve flexibility.
Top 10 AI voice agent platforms in 2025
1. Retell AI
Retell AI is widely recognized as one of the strongest platforms for building real-time AI voice agents. Whether you care most about advanced features, responsiveness, or cost efficiency, Retell offers a balanced mix of all three. The platform is built to help teams automate live phone conversations with natural-sounding voice interactions, predictive intelligence, and adaptive workflows that adjust to customer needs in real time.
Retell provides a developer-first environment with an intuitive drag-and-drop builder for designing, deploying, and managing voice agents quickly. It supports leading LLMs, multilingual voices, real-time logic, and smooth integrations with telephony providers like Twilio.
Because Retell handles multiple languages and diverse voice models, it’s a strong fit for global teams that need broad audience coverage and consistent customer experiences.
To simplify the evaluation process, Retell also includes pre-made templates for use cases such as lead qualification, appointment scheduling, routing, and customer support.
Key Benefits
Retell stands out for its speed, flexibility, and usability, making it an attractive choice for teams wanting to launch voice agents without long onboarding cycles or heavy engineering lift.
Transparent pricing: Retell uses a clear, usage-based billing model. You pay for what you use and nothing else. No enterprise-only pricing walls or opaque minimums — just predictable per-minute and per-model costs.
Built for voice-first performance: Retell’s architecture is purpose-built for live phone calls. Sub-second latency, natural turn-taking, and interruption handling create conversations that feel genuinely human.
Fast deployment with no partner requirements: You can design, test, and release agents in a matter of hours. There’s no need for external implementation partners, making it ideal for teams experimenting with AI receptionists, outbound automation, or call-handling pilots.
Real-time analytics: Every interaction is automatically transcribed, summarized, and evaluated for sentiment and performance. These insights help teams iterate quickly and improve call quality — functionality that many general-purpose platforms lack.
Flexible integrations: Retell connects directly with telephony systems like Twilio or SIP trunks, and plugs into CRMs like Salesforce and HubSpot. This makes it easy to slot into your existing stack without re-architecting workflows.
Model and voice control: Mix models from OpenAI, Anthropic, and others, and choose from premium voice providers like ElevenLabs or Play.ht. Each agent can use the model and voice best suited for the use case.
Low maintenance, scalable infrastructure: Retell automatically scales across high-volume calling without complex configuration or multiple infrastructure layers.
Compliance-ready: Retell meets SOC 2, HIPAA, and GDPR requirements, making it suitable for organizations in regulated sectors.
Cons
Requires some comfort with configuring LLMs and prompts to get the best results.
Telephony and LLM usage can still add up at very high volumes, so you need basic cost monitoring.
Pricing
Retell is one of the most affordable enterprise-grade voice AI platforms. Usage-based pricing starts around $0.07 per connected minute, with enterprise discounts dropping to roughly $0.05 per minute at scale. You only pay for active call time — not idle minutes.
Number rental starts at $2 per month for local numbers and $5 per month for toll-free numbers. The company also provides a $10 testing credit and limited concurrent calls for early pilots.
Customer Feedback: “Retell AI has completely transformed the way we manage automated calls, with impressive voice quality and understanding.”
Recommended For
Teams that want a highly flexible, transparent, and scalable voice AI agent system tailored to real-time phone interactions. Especially well-suited for product teams, call centers, and outbound sales groups looking for predictable pricing and fast iteration cycles.
Build your first Retell agent in minutes for free.
2. PolyAI
PolyAI focuses on multilingual customer support, call containment, and AI answering services for companies that serve global audiences. It plugs into existing contact center stacks and CRMs, and offers deep analytics, configurable voice experiences, and relatively fast time to go live.
Key Benefits
Pre-trained domain assistants: PolyAI provides ready-made assistants for use cases like authentication, billing questions, order lookups, reservations, and routing. These can be customized, but they give you a strong starting point without heavy training.
High call containment from day one: In production environments, PolyAI has reported call containment rates above 80 percent, with up to 87 percent of calls successfully handled early in deployments, while still allowing smooth handoff to human agents when needed.
Human-like voice and language handling: The system is tuned for natural speech. Callers can talk freely, interrupt, change topics, and blend phrases while the assistant maintains context and continues the conversation smoothly.
Multilingual and globally ready: PolyAI supports a wide range of languages and accents, which makes it a strong option for companies with international customers and regional markets.
Enterprise certifications and regulated use cases: PolyAI highlights certifications such as SOC 2 and is designed to be deployed in regulated spaces like healthcare and financial services.
Strong CRM and contact center integrations: It integrates with major contact center and CRM platforms so teams can keep their existing stack and still add advanced voice automation.
Cons
High minimum contract size and custom pricing make it inaccessible for many smaller teams.
Heavier implementation and customization cycles compared to more self serve tools.
Strongest value shows up in large, complex contact center environments rather than small pilots.
Pricing
PolyAI uses custom, usage-based enterprise pricing with relatively high starting thresholds, typically beginning around 150,000 dollars per year. Exact rates depend on volume and configuration and are not listed publicly.
Review: "There are many options for AI currently in the market. PolyAI impressed us by providing a product that could be launched in a short amount of time without risking quality".
Recommended For
Large enterprises and contact centers that want a fully managed, highly customized voice AI solution with strong multilingual capabilities.
3. Bland AI
Bland AI is focused on highly realistic voice interactions and strict security and governance. It handles large-scale inbound and outbound calling, SMS, and broader omnichannel workflows, which makes it well suited for enterprise telemarketing, notifications, and transactional calls.
The company highlights its ability to scale to as many as one million concurrent calls, which appeals to organizations that require significant resiliency and throughput.
Key Benefits
Extreme scalability: Bland is engineered for very high call volumes, supporting up to one million concurrent calls, which is far beyond what many typical setups can accommodate.
Granular conversational control: The Conversational Pathways feature allows teams to design detailed dialog flows that mix scripted and generative responses, giving more control over what the agent can and cannot say.
Proprietary voice and model stack: Bland runs its own speech and reasoning models rather than depending entirely on third parties, giving it greater control over latency, quality, and reliability.
Multi-region deployment and data control: Customers can choose data processing regions to support GDPR and local privacy rules. This is especially attractive to industries like healthcare and finance that have strict compliance needs.
Built-in omnichannel capabilities: Bland supports voice, SMS, and chat from the same platform, enabling scenarios such as order tracking, follow-up messaging, and inventory updates across channels.
Cons
No public pricing and clear enterprise focus can be a barrier for mid market or smaller teams.
Setup and customization typically require more engineering involvement.
Designed for extreme scale, which can feel like overkill for simpler or lower volume use cases.
Pricing
Pricing is not published. Bland positions itself for large enterprise deals, with costs that reflect its customisation and scale.
Product Hunt Rating: 3 / 5 (10 reviews)
Best For
Large enterprises that have tight requirements around privacy, governance, and brand voice, and that operate at very high call volumes.
4. Voiceflow
Voiceflow is a no-code platform for designing conversational flows across voice and chat. It is particularly strong for prototyping, collaboration, and iterating on agent experiences.
Key Benefits
Rapid prototyping and iteration: The drag-and-drop builder and modular components allow teams to create and refine agents in hours, which is much faster than large, enterprise-first platforms.
Collaboration-centric design: Voiceflow includes real-time collaboration, shared workspaces, commenting, and role-based permissions so designers, product teams, and engineers can work together smoothly.
Technology agnostic: You can plug in any LLM, API, backend system, or data source, which reduces vendor lock-in and gives you flexibility as the AI landscape shifts.
Support for voice and chat: From a single interface, you can build agents that respond over both voice and text. This simplifies management of multichannel experiences and helps keep behavior consistent.
Enterprise security features: Despite its emphasis on design, Voiceflow offers SOC 2 and ISO 27001 compliance, along with permissions and guardrails for enterprise teams.
Cons
Out of the box, it is more of a design and orchestration layer than a full telephony stack.
Costs can climb quickly at higher usage or with larger teams of editors.
You still need to wire in underlying LLMs, calling infrastructure, and evaluation if you want production grade agents.
Pricing
Voiceflow provides a free tier for basic usage. The Pro plan begins at 60 dollars per editor per month for up to 20 agents. The Business plan at 150 dollars per editor per month unlocks unlimited agents, with enterprise pricing available on request.
Review: "Good platform if you have less than 5,000 chats per month, otherwise extremely expensive".
Best For
Startups, design teams, and innovation groups that value speed of experimentation and cross-team collaboration more than extremely high call concurrency.
5. Sierra AI
Sierra AI builds advanced customer service agents that are trained to align closely with a company’s brand identity and policies. The focus is on agents that can reason, act, and communicate in a way that feels true to the brand.
Key Benefits
Action-oriented agents: Sierra agents integrate with backend systems like CRMs, subscription tools, and order platforms so they can perform tasks such as updating records or processing returns, not just respond with information.
Multi-model architecture: Sierra uses a constellation of models, including options like OpenAI, Anthropic, and Meta. This multi-model setup is designed to boost reliability, reduce hallucinations, and provide fallbacks.
Voice plus omnichannel: Sierra added voice capabilities in 2024, so the same agents can now handle natural phone conversations with interruptions and realistic cadence while also working across other channels.
Guardrails and governance: Strong controls for policy enforcement, data access, and auditing are central to the platform. You can trace and manage how decisions are made, which is crucial for long-term compliance.
Brand-level tuning: Teams can shape tone, vocabulary, and context handling so the agent sounds and behaves like the brand, which is especially important for consumer-facing companies.
Cons
High starting price puts it firmly in the enterprise bucket.
Setup can be complex and requires cross functional alignment across data, policy, and brand.
Reported bugs and rough edges compared to some more focused voice players.
Pricing
Pricing for Sierra generally starts around 150,000 dollars per year, with final costs set based on agent complexity and interaction volume. The idea is to deliver sophisticated, brand-aligned automation at a lower total cost of ownership than some legacy enterprise platforms.
Review: “User friendly, fast and many supported languages. Very complex setup process and more bugs then competitors”.
Recommended For
Customer-focused brands in areas like telecom and financial services where consistent tone, compliance, and policy adherence are essential.
6. Replicant
Replicant is an enterprise automation platform designed for contact centers and support-heavy organizations. Its Thinking Machine is built to resolve Tier 1 customer calls independently, escalate when appropriate, and integrate with backend systems.
Key Benefits
Resolution-first philosophy: Replicant aims to solve customer issues end-to-end instead of just routing or deflecting calls, which can significantly reduce the load on live agents.
Voice plus other channels: The platform supports voice, chat, and SMS automation, enabling consistent experiences across multiple touchpoints.
Hands-on implementation support: Customers frequently call out Replicant’s responsiveness and collaborative approach during deployment as a major strength.
Enterprise scale: Replicant’s technology has been deployed in real contact centers with high call volumes and complex workflows, demonstrating proven scalability.
Analytics and insights built in: The platform provides call summaries, trends, and performance metrics so teams can continuously improve automation outcomes.
Cons
No transparent pricing and an enterprise sales motion can slow down experimentation.
Implementation typically requires a formal project rather than a lightweight self serve trial.
Strong focus on support and contact center workloads, less suited for small, experimental teams.
Pricing
Replicant does not share standard pricing publicly. Engagements are structured as enterprise contracts, tailored to call volume, complexity, and required integrations.
Review: "The team is quick to reply if there are any technical concerns and is open to feedback. They usually respond within an hour when a ticket is sent in".
Recommended For
Large contact centers that want to automate a substantial portion of inbound volume with a partner that has deep experience in voice automation.
7. ElevenLabs
ElevenLabs is best known for its high quality text-to-speech and voice cloning technology, and has recently moved into conversational AI agents. Its tools can take textual or spoken input, ground it in your data, and deliver highly natural voice responses.
While it is not a full telephony stack on its own, it is a strong option for brands that already have an audio focus and want cutting-edge voice quality.
Key Benefits
Highly realistic voice synthesis: ElevenLabs produces voices that are very close to human, capturing subtle tone and rhythm so audio feels natural rather than synthetic.
Advanced voice cloning: It can clone voices using relatively small audio samples, allowing companies to create distinctive voice identities for their brand or products.
Multilingual and cross-language support: ElevenLabs supports many languages and can handle dubbing or translation while preserving the original voice’s character.
Flexible voice design controls: Teams can adjust attributes such as accent, age, gender, and style and fine tune parameters to align with their brand tone.
Cons
Not a full voice agent or telephony platform on its own, you still need other tools for call flows and routing.
Credit based pricing can be hard to forecast at scale without careful monitoring.
Compliance posture is more limited than platforms built specifically for regulated, enterprise environments.
Pricing
ElevenLabs operates on a credit-based system. You purchase credits that can be used for TTS, agents, and other capabilities, and buy more as needed.
Example tiers include:
Free: 10,000 credits per month (roughly 10 minutes of high quality TTS or about 15 minutes of agent time)
Starter: 5 dollars per month for 30,000 credits
Higher tiers for creators, pros, business, and enterprise with increasing credit allowances, priority, and SLAs
Total cost depends on how many minutes of audio and agents you run and what quality levels you select.
Recommended For
Teams that heavily care about voice quality, expressiveness, and branding, such as those working on podcasts, narration, gaming, or voice-first apps. For full telephony routing and complex call workflows, it is usually paired with other platforms.
8. Synthflow AI
Synthflow AI is a scalable voice AI platform that combines a no-code workflow builder, real-time personalization, and strong CRM integrations. It supports HIPAA compliance, inbound routing, and multi-tenant setups, which makes it especially attractive for agencies and teams running multiple clients on a single system.
Key Benefits
Straightforward pricing: Synthflow AI uses a clear pay-as-you-go structure at around 0.08 dollars per minute, which makes budgeting easier than platforms that hide costs behind enterprise quotes.
Fast time to value: The visual builder and pre-built templates allow teams to launch voice agents in weeks without heavy implementation projects, ideal for AI appointment setters or call routing assistants.
Low latency and smooth calls: Synthflow AI targets sub-500 ms response times so conversations feel natural and do not suffer from awkward pauses.
Voice cloning and multilingual support: You can clone voices, adjust tone, and support more than 50 languages so brands can offer localized, on-brand experiences in different regions.
Bring-your-own-carrier: Synthflow AI connects with Twilio, SIP trunks, and existing phone systems so you can keep your current telephony stack and layer AI on top.
Advanced generative features: The platform uses generative AI for dynamic, personalized interactions instead of rigid IVR menus.
Cons
Feature set is optimized for common workflows; very complex enterprise scenarios may hit limits.
No code builders can hide complexity, making debugging harder once flows get large.
Built in opinionated patterns that may not match every team’s preferred architecture.
Pricing
Starter: 29 dollars per month for 5,000 minutes and 1 agent
Growth: 99 dollars per month for 20,000 minutes and unlimited agents
Scale: 249 dollars per month for 60,000 minutes
Custom enterprise pricing is available for higher volumes
Review: “What I like best about Synthflow is that it doesn’t bury you in technical complexity. You don’t need to be a coder or spend weeks wiring together APIs just to get a usable AI voice agent”.
Recommended For
Marketing teams, agencies, and enterprises that need compliant automation, robust inbound voice flows, and deep integrations without a large engineering team.
Ada.cx provides AI agents that automate customer service across voice, chat, and email so support teams can handle more volume without expanding headcount at the same pace. The platform is built AI-first rather than around rigid, scripted bots, which helps it deal with more open-ended customer questions.
Key Benefits
Unified AI Agent across channels: Ada runs voice, chat, and email under one AI layer, so you do not have to manage separate tools for each channel.
Knowledge-driven generative answers: By connecting to your knowledge base and documentation, Ada can generate responses on the fly, reducing manual intent training and speeding up deployment.
Natural, low-latency voice calls: Ada’s voice agents handle interruptions, pauses, and open questions, which makes interactions feel closer to speaking with a human.
Pay-for-resolution pricing: Instead of charging only by minutes or traffic, Ada offers pricing tied to resolved conversations, aligning cost with value.
Enterprise compliance and scale: Ada supports SOC 2, HIPAA, and GDPR and is designed to support high-volume, regulated environments.
Review: “Ada helped our small support team contain the most easy-to-resolve customer inquiries, freeing-up more time for agents to go through our backlog.”
Pricing
Ada uses performance-based pricing, usually tied to successful resolutions or interaction volume. Final pricing depends on monthly conversations, integrations, and channels, but most enterprise plans start in the low six figures per year.
Recommended For
Brands that prioritize high quality customer experience at scale, especially in e-commerce, fintech, and telecom, where multilingual support and fast automation setup matter.
Decagon.ai offers a unified AI engine that resolves customer issues across chat, voice, email, SMS, and custom channels. Its core concept is Agent Operating Procedures (AOPs), which are natural-language instructions that compile into logic, giving teams a faster and more flexible way to define agent behavior.
Key Benefits
Agent Operating Procedures (AOPs): You describe business logic in plain language and Decagon compiles it into code. Non-technical teams can adjust behavior quickly while engineers maintain control over guardrails.
Unified omnichannel plus voice: Decagon supports digital channels and voice through Decagon Voice, so you do not need one stack for chat and a separate one for telephony.
Custom voice and brand tuning: Integration with ElevenLabs allows for very natural, brand-aligned voices with control over tone and pronunciation.
Deep action and integration layer: Agents can call APIs, trigger workflows, and connect to tools like Stripe for billing or refunds, so they can actually complete tasks instead of only answering questions.
Observability and decision traceability: Every decision path is logged, versioned, and auditable, which simplifies debugging and ongoing improvement.
Rapid deployment and strong ROI: AOPs and a model-agnostic architecture help teams go live faster than building dialog flows from scratch, and case studies cite support cost reductions of up to 65 percent.
Pricing
Decagon positions pricing around value with two main models:
Per conversation: A fixed fee per interaction, whether fully resolved or not
Per resolution: You pay only when the agent resolves the issue without human escalation
Because Decagon targets enterprises with larger volumes, pricing is custom. Public references put many deployments between roughly 95,000 and 590,900 dollars per year, depending on volume and complexity.
Review: "The biggest upside of using Decagon isn't simply the assumption of repetitive day-to-day tasks that would normally be done manually, but that Decagon allows us to evaluate data on a much deeper level."
Recommended For
Enterprises that want highly customizable, transparent, and outcome-focused automation, especially in fintech, telecom, and SaaS with heavy support demand.
Fastest setup, most flexible stack, strong analytics
PolyAI
Large enterprises, multilingual support
High performance, tuned for call containment
Custom enterprise quotes only
Weeks to deploy
Strong contact center & CRM integrations
Human-like, multilingual voices
SOC 2, regulated industry ready
Call containment >80%, domain-trained agents
Bland AI
Enterprises needing scale + governance
Extremely robust (up to 1M concurrent)
No public pricing
Weeks to months
API-first; custom infrastructure
Hyper-realistic proprietary voices
GDPR, HIPAA-friendly
Massive concurrency, strong control & security
Voiceflow
Design teams, prototyping, collaboration
Good for testing, varies in production
Clear plan-based pricing
Hours to days
Any LLM, any API (most flexible design tool)
Varies, strong for design not telephony
SOC 2, ISO 27001
Best flow builder & collaboration features
Sierra AI
Brands needing tone control & governance
Strong, multi-model fallback
Enterprise-only
Weeks to deploy
Deep backend integration & policy controls
Natural, consistent brand-aligned
Enterprise-grade governance
Best for brand tone alignment & compliance
Replicant
Large contact centers, high-volume support
Enterprise-grade performance
No public pricing
Enterprise deployment cycle
Strong CCaaS + CRM integrations
High quality but contact-center tuned
Enterprise-level safety & controls
Resolution-first engine, strong support model
ElevenLabs
Voice quality, branding, audio products
High performance TTS
Clear credit-based pricing
Minutes to integrate
API-first voice engine
Best-in-class TTS & cloning
Not a full compliance stack
Unmatched voice realism
Synthflow AI
Agencies, marketing teams, SMB automation
Sub-500 ms
⭐ Very transparent (plan + usage)
Weeks (fast, no-code)
Twilio, SIP, CRMs
Good quality, multilingual
HIPAA support
Affordable at scale, strong builder
Ada.cx
Enterprise CX teams, omnichannel automation
Strong, consistent
Enterprise-only, usage-based
Weeks
CRM, CX, support stack
Natural, intent-aware
SOC 2, HIPAA, GDPR
Pay-for-resolution model, strong KB grounding
Decagon.ai
Enterprises needing deep action-taking AI
Strong, model-agnostic
Custom (resolution or conversation based)
Weeks
APIs, Stripe, backend workflows
ElevenLabs-powered voices
Enterprise governance
AOPs, deep integrations, strong ROI claims
Saving sales time with AI voice agents
I started looking into AI voice agents because our sales team was drowning in pointless first-touch calls — missed follow-ups, no-shows, bad leads, you name it. We tried a couple of platforms and they all sounded like robots reading from a script. Slow, awkward, and definitely not something I’d trust with a prospect.
Out of frustration, I rebuilt our entire first-touch workflow using Retell AI for the calls and Vellum for the logic. The first test call shocked me. Retell actually sounded human and replied instantly. And with Vellum, I could change the pitch flow, objection handling, qualification steps, everything, without waiting on an engineer.
Within a week, that setup was qualifying leads, booking meetings, and handling all the “just checking in” calls our reps hated. And it did it without burning prospects with weird pauses or scripted answers.
Retell handled the live conversation. Vellum handled the brain. Together, it finally felt like an AI agent I could trust to talk to prospects without embarrassing us.
The biggest takeaway from comparing these platforms is that AI voice agents are only as good as their fit for your actual use case. Some excel at real time phone performance, others at multilingual support or strict governance, and some focus on fast iteration and clear pricing.
If you want the right outcome, ignore the hype and focus on the basics: latency, pricing clarity, integration flexibility, and how quickly you can make changes without relying on a vendor. Pick a platform that matches the calls you need to run, run a small pilot, listen to the calls, and iterate. That simple approach consistently outperforms choosing the platform with the longest feature list.
1. How is an AI voice agent different from a traditional IVR system?
Traditional IVRs route callers through fixed menus and keypad options. An AI voice agent lets callers speak naturally, understands intent, accesses backend systems, and can resolve requests end to end instead of just forwarding calls to a queue.
2. What kinds of use cases are AI voice agents actually good at today?
They are strongest at high volume, repeatable workflows like lead qualification, appointment scheduling, order status, basic troubleshooting, payment reminders, and routing. More complex or sensitive issues are usually better handed off to human agents.
3. How should I think about latency when choosing a platform?
Anything over a second of delay between a caller finishing a sentence and the agent responding will feel awkward. When evaluating platforms, test real calls and listen for overlap handling, interruptions, and how quickly the agent recovers after someone talks over it.
4. Can AI voice agents integrate with my existing telephony and CRM stack?
Most modern platforms support SIP, Twilio, or native carrier integrations and can connect to CRMs like Salesforce or HubSpot through APIs. Before you choose a vendor, confirm how they handle caller ID, contact syncing, and logging calls or transcripts into your existing systems.
5. How do I keep AI voice agents from “hallucinating” or giving wrong answers?
You reduce hallucinations by grounding the agent in your own data, enforcing clear guardrails, and testing prompts against real calls. Platforms that let you control prompts, tools, and retrieval logic directly make it easier to debug and correct behavior over time.
6. What are the main security and compliance questions I should ask vendors?
Ask where data is stored, how long call recordings and transcripts are retained, whether you get data residency options, and which certifications they hold (SOC 2, HIPAA, GDPR). You should also understand how access is controlled and how to delete or export data if you churn.
7. How do I measure if an AI voice agent deployment is successful?
Track containment rate, resolution rate, transfer rate to humans, customer satisfaction, average handle time, and cost per resolved interaction. Listen to a sample of calls weekly and pair the data with qualitative review so you can see why metrics are moving.
8. How big does my team need to be to manage an AI voice agent in production?
You do not need a huge team, but you do need clear owners. In most cases one person on ops or product, one engineer for integrations, and one stakeholder from support or sales is enough to run pilots and keep things healthy once the flows are stable.
9. Can AI voice agents handle sales calls, or are they only for support?
They can do both, but the responsibilities are different. For sales, agents are best at qualification, follow ups, and scheduling, not closing. A good pattern is to let the AI handle first touch, gather context, and then hand off warm, qualified conversations to human reps.
10. How should I run a pilot before committing to a platform long term?
Pick one narrow use case, define success metrics up front, and cap the call volume. Run the agent against real traffic for a few weeks, review transcripts daily, and iterate on prompts and flows. If you cannot ship changes quickly during the pilot, that is a red flag.
11. What is the advantage of pairing a voice platform like Retell with a workflow layer like Vellum?
A voice platform gives you low latency calling, telephony, and audio quality. A workflow layer like Vellum controls prompts, tools, and evaluation. Together you can experiment on the “brain” of the agent while keeping the voice and telephony stable, which makes it much easier to improve performance without ripping out your entire stack.
ABOUT THE AUTHOR
Nicolas Zeeb
Technical Content Lead
Nick is Vellum’s technical content lead, writing about practical ways to use both voice and text-based agents at work. He has hands-on experience automating repetitive workflows so teams can focus on higher-value work.
ABOUT THE reviewer
Anita Kirkovska
Founding Growth Lead
An AI expert with a strong ML background, specializing in GenAI and LLM education. A former Fulbright scholar, she leads Growth and Education at Vellum, helping companies build and scale AI products. She conducts LLM evaluations and writes extensively on AI best practices, empowering business leaders to drive effective AI adoption.
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6+ months on engineering time saved
Learn how CourseMojo uses Vellum to enable their domain experts to collaborate on AI initiatives, reaching 10x of business growth without expanding the engineering team.
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