Braintrust vs Phoenix (2026)
Overview
Braintrust is an LLM evaluation and observability platform. Compare its evals, tracing, pricing, pros and cons, and alternatives.
Phoenix is an open-source LLM observability and evaluation platform from Arize AI. It supports OpenTelemetry-based tracing across LLM and agent applications, with built-in evaluators, dataset management, and prompt playgrounds. Phoenix can be self-hosted with Docker or run via the Arize-hosted cloud version.
Key features
Core capabilities each platform advertises.
- Experiments comparing prompts and models
- Code-based and LLM-as-judge scorers
- Datasets built from production traces
- Production logging and tracing
- OpenTelemetry-based LLM and agent tracing
- Built-in evaluators for hallucination and relevance
- Dataset and experiment management
- Prompt playgrounds and versioning
- Self-hosted Docker deployment or Arize cloud
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Unlimited users on every plan, including free
- Production traces, datasets, and experiments connect in one eval workflow
- Framework-agnostic SDKs across multiple languages
- Loop agent drafts scorers and datasets from your own data
Cons
- Processed data and scores bill separately, and both grow with eval volume
- 14-day retention on Starter and 30 days on Pro
- Gateway is in public preview with no SLA
- Self-hosting and custom data export are Enterprise only
Pros
- Open-source with active development by Arize
- OpenTelemetry-native (no proprietary trace format lock-in)
- Strong evaluator library out of the box
- Both self-hosted and managed cloud options available
- Upgrade path to full Arize enterprise platform
Cons
- Smaller community than Langfuse for open-source-first teams
- Cloud version's enterprise pricing is contact-sales only
- UI and feature set tilt toward ML engineers more than application developers
Braintrust or Phoenix — which should you choose?
Choose Braintrust if you wantChoose if you want
- Iterating on prompts with production data
- Running evaluations across model versions
- Building golden datasets from production traffic
- Human review of LLM outputs
Choose Phoenix if you wantChoose if you want
- OpenTelemetry-native LLM observability
- Hallucination detection for RAG
- Experiment tracking and golden-set evaluation
- Prompt iteration and comparison
- Self-hosted tracing for compliance-sensitive teams
Compare Braintrust and Phoenix on your own traffic
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