Best LLM Observability Platforms for Production AI Agents, Ranked

Langfuse is an open-source LLM engineering platform covering tracing, prompt management, datasets, and evaluation, with an MIT-licensed core that ships native SDKs for Python and JavaScript and connectors for LangChain, LlamaIndex, OpenAI SDK, Vercel AI SDK, and 50+ other frameworks. Cloud pricing is usage-based on units (traces, observations, and scores) with unlimited users on every paid tier, starting at $29/month on Core for 100k units with overage at $8 per 100k units. The trade-off is operational: self-hosting on the MIT version is unusually viable, but the data layer migrated to ClickHouse in v3, and operating ClickHouse at production scale is real ops work.

LangSmith is LangChain's observability and evaluation platform, with native tracing of LangChain and LangGraph applications, annotation queues, LLM-as-judge evaluators, and dataset management for regression testing. The Plus plan is $39 per seat per month with 10,000 base traces included, overages at $0.50 per 1,000 traces (some pricing sources cite $2.50 per 1,000), and 400-day retention. The trade-offs are framework lock-in and self-hosting: the @traceable decorator works with any stack, but the deepest integration is with LangChain and LangGraph, and self-hosting is gated behind the Enterprise tier, which puts cloud-hosted trace data on the Plus plan.

Braintrust is built around evaluation rather than tracing alone, with custom scorers, LLM-as-judge, playground experiments against production traces, and a native GitHub Action that runs evals on every pull request and can block merges that drop quality below a threshold. The free Starter plan includes 1M trace spans, 10k evaluation scores, and unlimited users; Pro is $249/month with an additional GB-and-score overage, and Enterprise is custom. Braintrust's customer list includes Notion, Stripe, Vercel, Airtable, Instacart, Zapier, Ramp, Dropbox, Cloudflare, and BILL, and the company raised an $80 million Series B in February 2026 at an $800 million valuation. The trade-offs are the jump from free to $249 with no mid-tier, and self-hosting only on Enterprise.

Phoenix is Arize AI's open-source observability and evaluation platform, built on OpenTelemetry and OpenInference instrumentation, with out-of-the-box support for OpenAI Agents SDK, Claude Agent SDK, LangGraph, Vercel AI SDK, Mastra, CrewAI, LlamaIndex, DSPy, and the major LLM providers. It runs locally, in a Docker container, on Kubernetes via Helm, or as two free cloud instances at app.phoenix.arize.com. The full enterprise platform (Arize AX) is custom-priced for production-scale teams. Phoenix is the strongest pure open-source choice when trace data has to stay on your infrastructure and you can carry the deployment work; it's weaker than evaluation-first platforms on built-in eval depth for LLM-specific use cases like faithfulness and conversational coherence.

Datadog LLM Observability adds LLM tracing, automated cost calculation across 800+ models, evaluators, and Sensitive Data Scanner-based PII redaction to the existing Datadog APM platform, with auto-instrumentation for OpenAI, Anthropic, Bedrock, and LangChain calls and free LLM-span ingestion below 40,000 spans per month. Pro starts at $160/month for 100,000 LLM spans, with retention add-ons billed per 10,000 spans and only LLM spans metered (tool, embedding, retrieval, and agent spans are free). The trade-offs are dedicated AI quality tooling and overall observability cost: there are no built-in evaluation metrics for faithfulness, relevance, or safety in the way evaluation-first platforms ship them, and Datadog's broader pricing model is its own line item to budget.