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Vellum vs LiteLLM

VellumLiteLLM

Bottom line: Vellum for teams wanting an all-in-one build-evaluate-deploy platform; LiteLLM for engineering teams juggling multiple LLM providers.

Build, evaluate, and deploy LLM apps and agents with confidence.

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Open-source AI gateway to call 100+ LLM APIs in one format

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Votes00
PricingFreemiumFreemium
CategoryLlm ObservabilityCoding
Tags
llmopsai-agentsprompt-engineeringevaluationworkflow
llm-gatewayopen-sourceapi-proxymodel-routingllmops
Best for
  • Teams wanting an all-in-one build-evaluate-deploy platform
  • Cross-functional groups with technical and non-technical members
  • Regulated sectors needing VPC or HIPAA options
  • Engineering teams juggling multiple LLM providers
  • Platform teams building an internal AI gateway
  • Startups wanting free multi-model routing
Pros
  • Combines prompt, workflow, evaluation, and deployment in one platform
  • Visual workflow builder lowers the barrier for cross-functional collaborators
  • Solid evaluation and testing tooling for pre-production quality gates
  • Multi-provider model support avoids lock-in
  • Free tier lets teams trial without a credit card
  • Free, actively maintained open-source core with a large community
  • Supports 100+ providers through one OpenAI-compatible interface
  • Built-in cost tracking, budgets, and virtual keys
  • Load balancing, retries, and fallbacks for reliability
  • Can be fully self-hosted for data control
Cons
  • Pricing is opaque and sales-led, making budgeting hard
  • Not open source, and self-hosting is locked to Enterprise
  • Free tier is quite limited in credits, documents, and seats
  • Smaller company and funding than infrastructure incumbents
  • Frequent repositioning can mean shifting product focus
  • Self-hosting means you own deployment, scaling, and maintenance
  • Advanced governance (SSO, RBAC, audit logs) requires the paid Enterprise tier
  • Enterprise pricing is negotiated and not fully transparent
  • Acting as a proxy adds an operational hop to debug when issues arise
  • Feature breadth can make initial configuration complex

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