Best LLM Observability & Evals 2026

Langfuse is the default recommendation for the same reason pgvector is the default vector store: it is good enough at everything, cheap, and doesn't lock you in. The core is MIT-licensed and free to self-host, framework coverage is the broadest in the category, and you get tracing, prompt management, scores, and session threading in one tool.

Cloud pricing runs Hobby free (50k units/month), Core $29/mo, Pro $199/mo (both with 100k units included), and Enterprise at $2,499/mo. The thing to understand is the billing unit: a single trace, with all its observations and scores, consumes many units — so estimate from your own volume rather than from trace counts. Overage is $8 per 100k units, graduating down to $7 past 1M and $6 past 50M. Self-hosting is free, with a $500/mo enterprise license for the advanced tier.

Where it's weaker: evals are functional but manual — you author datasets and annotation workflows yourself rather than getting the guided experiment machinery Braintrust ships.

Best for: Most teams, and anyone with data-sovereignty or GDPR constraints. Skip if: your entire workflow is dataset-and-regression-gate centered.

If you build on LangChain or LangGraph, LangSmith is close to zero-effort: the trace tree maps onto your chain or graph structure automatically, with no instrumentation to write. Its Insights feature clusters similar traces so you can spot a failure pattern rather than reading a thousand individual runs.

Pricing is seat-based: Developer is free with 1 seat and 5,000 base traces/month; Plus is $39/seat/month with 10,000 base traces, then pay-as-you-go. That model is fine for a small team and gets expensive as headcount grows — five engineers is $195/month before you've traced anything unusual. Self-hosting and hybrid deployment exist only on Enterprise.

Best for: LangChain and LangGraph teams who value integration over portability. Skip if: you're not on LangChain — the main advantage evaporates and you're paying per seat for a closed platform.

Braintrust inverts the usual design. Most tools are tracing platforms with evals bolted on; Braintrust is an evaluation platform where scores are native to the trace view. If your quality loop revolves around datasets, experiments, and regression gates that block a deploy, that architectural difference shows up in daily use rather than in a feature list.

It's also the premium option. Starter is free with 1 GB of processed data, 10,000 scores, and 14-day retention; Pro is $249/month with 5 GB, 50,000 scores, and 30-day retention, with overage at $3/GB and $1.50 per 1,000 scores. Billing on two axes — data volume and score count — means aggressive LLM-as-judge evaluation is what drives your bill, not traffic.

On durability: Braintrust raised an $80M Series B at an $800M valuation in February 2026 led by Iconiq, and counts Notion, Replit, Cloudflare, Ramp, and Dropbox as users. For a closed-source platform you're building a quality process around, that matters.

Best for: Teams with defined quality criteria who gate releases on eval scores. Skip if: you mainly need to debug traces, or you require self-hosting — there isn't one.

Phoenix is the most genuinely free option here — Apache 2.0 with no feature gating, no usage limits, no required API key, and no phone-home telemetry. You can run it in a local Python process while debugging and scale the same tool to Docker or Kubernetes in production.

Its real edge is standards. Phoenix is built on OpenTelemetry plus OpenInference, giving you a shared span vocabulary across LangChain, LlamaIndex, the OpenAI SDK, LiteLLM, and your own code. As the category consolidates on OTEL, that's the least lock-in available: your instrumentation stays portable if you later move to a commercial platform. It ships RAG-specific metrics and embedding analysis too, which is useful when debugging retrieval quality specifically.

Best for: Local debugging, air-gapped or regulated environments, and teams that want zero vendor dependency. Skip if: you want a managed service and a support contract — that's Arize AX, the paid sibling.

Helicone earned its reputation on the easiest integration in the category: change your base URL and you're logging every request, with cost and latency dashboards a minute later. It's Apache 2.0 and self-hostable at every tier, with Hobby free (10,000 requests/month, 1 GB, 7-day retention), Pro at $79/month, and Team at $799/month — the cheapest paid entry point of the group.

The complication is strategic, not technical. Helicone was acquired by Mintlify in March 2026 and now runs in maintenance mode: security patches, new model support, bug fixes, and performance work continue, but feature development has stopped. Combined with its proxy-based design — which captures requests well but not full agent trace trees — it's a questionable foundation for a new project heading toward agent evals.

Best for: Existing deployments, and quick cost/latency visibility on a simple LLM app. Skip if: you're starting fresh — Langfuse gives you the same open-source freedom on a product that's still moving.