Choose an LLM observability stack

There's no single best tool - the right choice depends on constraints. Gather them, then map to a recommendation. Base recommendations on this repo's curated list (verified tools + licenses), not on hype.

Ask these constraints first

1. Deployment: SaaS OK, or must self-host / on-prem (data residency, regulated industry)?
2. Primary need: tracing/cost, evaluation (quality testing), or both? Prompt management too?
3. Existing stack: already on Datadog/Grafana/OTel? On LangChain? Using a gateway?
4. Budget/licensing: need a permissive OSS license (MIT/Apache), or is a commercial tier fine? (Note AGPL/Elastic-license implications for embedding.)
5. Code-change tolerance: want zero-code (proxy) or fine to add an SDK?
6. Team: engineers, or also non-technical PMs who need a UI?

Map constraints → recommendation

• Must self-host, permissive license, want everything → Langfuse (MIT core: tracing + evals + prompts) or Comet Opik (Apache-2.0). For eval-heavy local work, Arize Phoenix.
• Zero code changes, just want cost + logs → a gateway/proxy: Helicone (change base URL), LiteLLM or Portkey (also routing).
• Already on OTel / want vendor-neutral, future-proof → emit OpenTelemetry GenAI semantic conventions via OpenLLMetry or OpenInference; export to your existing backend.
• Deep in the LangChain ecosystem → LangSmith (tightest integration; SDK OSS, backend commercial).
• Enterprise APM already (Datadog/New Relic) → use their LLM Observability product to keep one pane of glass.
• Primary need is evaluation/testing, not dashboards → promptfoo (prompt/RAG + CI), DeepEval (pytest-style), Ragas (RAG metrics). Pair with a tracing tool for online scoring.
• Regulated / finance / must audit + guardrail → self-hosted tracing (Langfuse/Phoenix) + guardrails (Guardrails AI, LLM Guard for PII/prompt-injection) + strict prompt/PII redaction.

Common production shape

A gateway (cost + routing) + an evaluation framework (quality) + an OTel-native tracing backbone. This keeps cost, quality, and traces decoupled and swappable.

Deliver the recommendation

• Name a primary tool + a runner-up, each with a one-line why it fits these constraints.
• Call out license/self-hosting implications explicitly (especially AGPL / Elastic-license for embedding, and SaaS data-egress for regulated data).
• Link to the tool's row in this repo's README so they can compare stars/license.

Anti-pattern

Recommending the highest-star tool by default. LiteLLM has the most stars but is a gateway - it's the wrong answer for someone who asked for an evaluation framework. Match the tool category to the stated need.