Braintrust vs Arize Phoenix
Detailed side-by-side comparison to help you choose the right tool
Braintrust
🔴DeveloperAI evaluation
AI evals, prompt iteration and observability platform
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FreeArize Phoenix
🔴DeveloperBusiness Analytics
Open-source LLM observability and evaluation platform built on OpenTelemetry. Self-host for free with comprehensive tracing, experimentation, and quality assessment for AI applications.
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FreeFeature Comparison
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💡 Our Take
Choose Braintrust if you want a managed SaaS platform with automated prompt optimization and a polished evaluation workflow — minimal setup and the Loop agent are the wins. Choose Arize Phoenix if you need open-source ML observability with deep support for embeddings, RAG debugging, and on-prem deployment for compliance reasons. Phoenix is stronger for ML researchers and RAG-heavy applications; Braintrust is better for product teams shipping LLM features fast.
Braintrust - Pros & Cons
Pros
- ✓Strong fit for production AI teams because traces, datasets and experiments live in one workflow
- ✓Starter is $0/month with 1 GB processed data, 10k scores and 14-day retention
- ✓Pro is $249/month with 5 GB processed data, 50k scores, 30-day retention and priority support
- ✓Framework agnostic with Python, TypeScript, Go, Ruby and C# SDKs
Cons
- ✗The value shows up after you have real traffic or evaluation datasets; it may be overkill for prototypes
- ✗Data and score overages require attention on high-volume products
- ✗Enterprise deployment choices need procurement and security review
Arize Phoenix - Pros & Cons
Pros
- ✓Fully open source and free to self-host, with no seat-based pricing, trace volume caps, or feature gating — a major advantage over LangSmith and other commercial competitors.
- ✓Built on OpenTelemetry and OpenInference standards, so instrumentation is portable and traces can be exported to other OTel backends without vendor lock-in.
- ✓Broad framework coverage with auto-instrumentation for LangChain, LlamaIndex, CrewAI, Haystack, DSPy, OpenAI, Anthropic, Bedrock, LiteLLM, and more — minimal code changes required to start tracing.
- ✓Comprehensive built-in evaluators (hallucination, relevance, toxicity, QA correctness, RAG metrics) plus a flexible framework for writing custom LLM-as-a-judge evals.
- ✓Backed by Arize AI, a well-resourced company with a commercial enterprise product, giving the open-source project sustained engineering investment and frequent releases.
- ✓Strong support for RAG debugging and agent tracing, including embedding visualization, UMAP clustering, and step-by-step inspection of tool calls and retrieval steps.
Cons
- ✗Self-hosting requires operational effort — running Postgres, managing storage growth from high-volume traces, and handling upgrades are non-trivial for small teams without DevOps capacity.
- ✗UI and workflows have a steeper learning curve than polished SaaS alternatives like LangSmith, especially for users new to OpenTelemetry concepts like spans and traces.
- ✗Rapid release cadence occasionally introduces breaking changes to SDKs, integrations, or UI, requiring teams to pin versions and test carefully before upgrading.
- ✗Documentation, while extensive, can lag behind the latest features, and some advanced workflows (custom evaluators, dataset versioning, annotation APIs) require reading source code or GitHub issues.
- ✗Enterprise features like SSO, RBAC, audit logging, and SLAs are reserved for the paid Arize AX platform rather than the open-source Phoenix core.
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