The dev platform for production LLM apps
Bring features powered by Large Language Models to production with tools for prompt engineering, semantic search, model versioning, quantitative testing, and performance monitoring. Compatible across all major LLM providers.
Rapid prompt engineering
Perform side-by-side comparisons of multiple prompts, parameters, models, and even model providers across a variety of test cases.
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Cross-Provider Support
Compare how the same prompt performs using any of the major LLM providers.
Test Cases & Quantitative Evaluation
Evaluate against your bank of test cases and a variety of metrics.
History Tracking & Collaboration
Each permutation you try is saved to your history and has a unique url so that you can revisit or share with others.
All of the Models
All major open and closed source providers (OpenAI, Anthropic, Google, Mistral, Llama-2).
Collaborate with your team
Take turns editing prompts and testing models with first-class collaboration tools.
Bring Your Own Model
Upload your custom models to Vellum directly in the App UI and test against other models.
Use your data to personalize experiences
Language models learn from the internet and might not give accurate facts about your company or customers, often making things up. Vellum Search fixes this.
Fully-managed semantic search
Good semantic search allows you to reliably retrieve relevant data specific to your company and use it as context in LLM calls. Home-grown systems are easy to prototype, but often fall short once in production.
Shared Workspace
PMs, Engineers and Domain Experts collaborate on building AI features at the same time.
Tightly integrated with the entire AI stack
Great search means nothing if it doesn't power a great end-user experience. Vellum Search is tightly integrated with the rest of Vellum's AI stack so you can quickly iterate on the full experience holistically.
Powerful defaults, highly configurable
Default chunking, embedding models, and search settings, with advanced customizations.
Make changes with confidence using Deployments
Vellum's framework for testing, versioning, and monitoring changes help you iterate with confidence.
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Simple API interface
Vellum's provider-agnostic, high-reliability, low-latency API allows you to make changes to the prompt without making any code changes.
Quantitative testing & version control
Build up banks of test cases and quantitatively evaluate changes to prompts at scale. Every update is version-controlled and can be easily reverted.
Replay recent requests
Spot check the "before" and "after" to confirm that outputs look reasonable.
Future Feature Support by Default
Use the latest features coming from model providers via a consistent API interface designed to integrate new updates by default.
Iterate on and deploy prompt chains with Workflows
Quickly prototype, deploy, version, and monitor complex chains of LLM calls and the business logic that tie them together.
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Vellum’s low-code workflow builder UI allows you to efficiently test hypotheses and iterate on your chains.
Production-grade observability
Vellum logs all Workflow invocations as well as valuable debugging info for each step in the chain. Debugging and troubleshooting problematic chains has never been easier.
Bring the Whole Team
Take turns editing the chain and the business logic in an easy handover process.
Managed Orchestration
Workflows can be deployed through Vellum and invoked via a simple streaming API. No need to manage complex infrastructure for schedulers or event-driven execution.
Evaluate the Quality of LLM prompts at scale
Use a bank of test cases to evaluate and identify the best prompt/model combination over a wide range of scenarios.
Measure performance for any use-case.
Use custom metrics to evaluate the performance of a prompt/model combination or a Workflow.
Custom Evaluators
Regex match, semantic similarity, JSON validity/schema match or use external endpoint to evaluate your output.
LLM-Based Evaluation
Use a Vellum Workflow as an evaluator for another Prompt/Workflow.
Log End-User Feedback
Identify areas where your AI app needs improvement, collect user feedback and add it to your evaluation dataset it to continue refining your prompts.
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