Top LLM Observability and Evaluation Platforms in 2026: Langfuse, LangSmith, Braintrust, Arize, and More Compared

LLM apps break in ways old software never really had to. The same prompt can come back differently. A retrieval step can grab the wrong document while every HTTP check still looks fine. An agent can burn through tool calls and tokens, then hand back a polished mistake. That is why observability for LLMs is no longer a side project in 2026. It’s infrastructure.

The category has split into four camps. AI-native platforms like Langfuse, LangSmith, Braintrust, Arize, and Opik treat the trace itself as the main artifact. Evaluation tools such as Arize Phoenix, DeepEval, MLflow, and RAGAS focus on scoring outputs. Gateways like Helicone, Portkey, and LiteLLM sit in front of model providers. And APM vendors such as Datadog, New Relic, and Dynatrace bolt LLM signals onto existing monitoring stacks.

OpenTelemetry is the thread tying all of this together. Its GenAI semantic conventions define vendor-neutral gen_ai.* span attributes for model calls, token usage, agent steps, and tool executions. Google Cloud, AWS, Azure, and Datadog are already using them, and coding agents are moving that way too. GitHub Copilot’s agent telemetry exposes gen_ai.* span trees, Claude Code offers opt-in tracing, and Codex supports native OpenTelemetry export. If a buyer wants portability, OTel is not optional anymore.

Langfuse stands out on the open-source end. It captures nested traces across retrieval, embedding, and agent actions, and its March 2026 observations-centric data model brought a claimed 10x+ dashboard performance jump. LangSmith leans into LangChain and LangGraph users with deep traces, online evals, and a unified cost view. Braintrust puts evaluation at the center, with versioned datasets, CI gates, and an AI agent that helps build scorers and datasets. Arize goes deepest on eval rigor, while MLflow appeals to teams that want trace ownership and no enterprise paywalls.

Helicone is the simplest entry point: route traffic through it and you get cost, token, and latency dashboards fast. But that’s the point. It’s a gateway first, not a full agent-forensics lab. The broader pattern is clear: teams are no longer asking whether to observe LLM systems. They’re deciding how much pain they want before they do.