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Braintrust vs LiteLLM

BraintrustLiteLLM

Bottom line: Braintrust for teams making evaluation a first-class part of development; LiteLLM for engineering teams juggling multiple LLM providers.

Eval-first evaluation and observability platform for AI applications.

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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
llm-evaluationobservabilityprompt-engineeringci-cdllmops
llm-gatewayopen-sourceapi-proxymodel-routingllmops
Best for
  • Teams making evaluation a first-class part of development
  • Organizations wanting CI/CD quality gates for AI
  • Product and AI engineering teams iterating on prompts
  • Engineering teams juggling multiple LLM providers
  • Platform teams building an internal AI gateway
  • Startups wanting free multi-model routing
Pros
  • Deep, eval-first feature set with scorers, experiments, and statistical rigor
  • Strong CI/CD integration to catch regressions before deploy
  • Usage-based pricing with unlimited users, so no per-seat penalty
  • Powerful playground for prompt iteration and comparison
  • Tight loop between production logs and evaluation datasets
  • 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
  • Not open source; source-available and hosted model
  • Self-hosting locked to the Enterprise tier and a license key
  • Big price jump from free to $249 per month
  • SSO, RBAC, and self-host are gated behind Enterprise
  • Data- and score-based billing makes costs less predictable at volume
  • 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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