Top LLM Observability and Evaluation Tools (2026)

Buying an observability tool is the easy half of this problem. The hard half is deciding what “good” means for your system, and no tool will do that for you. I have walked into AI projects with beautiful trace dashboards and no answer to the question “is it working better than last month?” The traces were all there. Nobody had ever labelled a single one.

So this comparison comes with a warning attached. The five platforms below differ in ways that matter, and I will go through them. But the gap between any two of them is far smaller than the gap between a team that maintains an evaluation set and a team that does not. If you only have budget for one decision this quarter, make it the second one.

Why LLM Systems Need Different Instrumentation

Conventional services fail loudly. LLM systems fail quietly, and your existing monitoring is built entirely around the first kind of failure.

When a normal API breaks, it throws a 500, latency spikes, an error rate crosses a threshold and something pages someone. Every tool in a standard APM stack is designed around that signal. An LLM system that has gone badly wrong returns HTTP 200, in good time, with a fluent and confident answer that happens to be false. There is no error to catch. Your dashboards stay green while your users lose trust.

That is the whole case for LLM-specific observability, and it has a consequence that shapes what you should look for: the useful unit of observation is the step, not the request. You need to see the retrieved context, not just that retrieval happened. You need the tool call arguments, not just that a tool was called. A request-level trace tells you an agent took nine seconds. A step-level trace tells you it searched the wrong index twice and then invented a plausible answer to cover for it.

The Five, Compared

The columns are chosen to reflect what actually differs in practice. Note that hosting and openness matter more than they might appear: traces contain your prompts, your retrieved documents and often your customers’ data, which makes where they live a compliance question rather than a preference.

Langfuse’s documentation is a fair reference point for what this category now includes as standard: prompt and response capture, token usage, latency, retrieval and tool steps, LLM-as-a-judge scoring, datasets and experiments, and dashboards over cost and quality. If a tool you are evaluating is missing several of those, it is a tracing tool rather than an evaluation platform.

Score Retrieval and Generation Separately

This is the single most valuable habit in this article, and it costs nothing but discipline. Most teams score the final answer, get a number, and then argue about prompts. That number cannot tell them what to fix.

A RAG answer can be wrong in two fundamentally different ways. Either the retrieval step never surfaced the right passage, in which case the model was asked to answer from context that did not contain the answer - and no prompt change will rescue that. Or retrieval worked, the correct passage was sitting in the context window, and the model still got it wrong, which is a genuine generation problem. The fixes have nothing in common. One is about chunking, hybrid search and reranking; the other is about prompting, model choice and output constraints.

So measure them apart. Ask first whether the correct passage appeared in the top results, as a question about retrieval alone with no model involved. Only once that number is healthy is it meaningful to ask whether the answer was right. I have watched teams spend a month on prompt engineering for a system whose retrieval was surfacing the correct document about half the time - the prompts were never the problem, and the end-to-end score could not have told them that.

The mechanics of the retrieval half - hybrid search, structure-aware chunking, reranking - are in RAG architecture in production, and the store-level decisions in vector databases for enterprise RAG.

Your Evaluation Set Is the Asset

Traces are cheap and abundant. Within a week of instrumenting anything you will have more traces than you can read. What is scarce, and what compounds, is a curated set of cases with known-correct answers.

Build it out of real failures. Every time someone reports a bad answer, that case goes into the set with the answer it should have given. Within a few months you have a regression suite that encodes everything your system has ever got wrong, and you can tell in minutes whether a change helped or quietly broke something that used to work. That artefact is worth more than any dashboard, and unlike the dashboard it is entirely yours.

Two practical notes. LLM-as-a-judge scoring is useful and not sufficient. It scales, which human labelling does not, but it agrees with human judgement unevenly and drifts when the judge model changes. Use it for breadth and keep a smaller human-labelled core as ground truth. And check you can export the set. A platform that holds your labelled data in a format you cannot take with you has made your most durable asset into a reason you cannot leave.

Make OpenTelemetry Support a Requirement

There is a standards story here that mirrors what MCP did for tool integration, and it is worth understanding before you commit.

OpenTelemetry maintains GenAI semantic conventions - a vendor-neutral definition of the spans, metrics and events a GenAI client should emit, with conventions for the Model Context Protocol and for specific providers alongside them. Where a platform speaks OTel, your instrumentation is a standard rather than a vendor SDK, and moving between tools stops meaning re-instrumenting your application.

I would make OTel support a hard requirement, with two honest caveats. The conventions are still stabilising, so treat it as a portability hedge rather than a promise of frictionless migration. And coverage is more settled for plain model calls than for agent and evaluation spans, which is exactly where you will want it most as agentic systems spread. Ask a vendor what they emit today rather than what the roadmap says.

What Actually Belongs on the Dashboard

Most LLM dashboards measure what is easy - tokens, latency, cost, request volume. Those are worth having and none of them tell you whether the system is working. A short list I would want in place before calling a system observable:

• Retrieval hit rate. How often the correct passage appears in the top results, measured without the model. The leading indicator of answer quality.
• Grounding rate. What proportion of answers are actually supported by retrieved context. Ungrounded answers are hallucinations whether or not they happen to be true.
• Regression against the eval set. Run it on every meaningful change. This is the number that tells you whether last week’s improvement broke something.
• Tool failure and retry rates for agentic systems. Silent tool failures are the most common cause of an agent confidently doing nothing useful, and they rarely surface as errors.
• Human escalation rate. How often someone had to step in. The most honest quality metric you have, and the one users implicitly compute themselves.

Cost and latency belong on there too. They are just not evidence of quality, and it is remarkable how often a dashboard full of them is treated as though it were. If you want to see what measuring against public benchmarks rather than self-reported numbers looks like in practice, my proof page is the version of this I hold my own work to.

Frequently Asked Questions

What is LLM observability?

LLM observability is the practice of recording what an AI system actually did on each request - the prompt sent, the context retrieved, the tools called, the model response, token usage and latency - in a form you can query afterwards. It differs from conventional application monitoring in what counts as a failure. A traditional service fails loudly with an error code; an LLM system usually fails quietly, returning a fluent, confident and wrong answer with a 200 status. Nothing in your existing APM stack will flag that, which is why LLM systems need tracing at the step level rather than the request level.

Why should retrieval and generation be evaluated separately?

Because they fail for different reasons and have different fixes, and a single end-to-end quality score cannot tell you which one broke. If the retrieval step never surfaced the right passage, no amount of prompt engineering will recover the answer - the information was not in the context window. If retrieval was correct and the answer is still wrong, the problem is genuinely in generation. Teams that score only the final answer routinely spend weeks tuning prompts against what is a retrieval failure. Measure retrieval quality on its own - did the correct passage appear in the top results - before you look at the answer at all.

Which LLM observability tool should I choose?

Langfuse if you want open source and self-hosting, which also matters when traces contain regulated data you cannot send to a vendor. LangSmith if your stack is already LangChain or LangGraph and you want the deepest native integration. Arize Phoenix if you want OpenTelemetry-native tracing and ML-grade rigour. Braintrust if evaluation is the centre of your workflow rather than an add-on. Datadog if your organisation already runs Datadog and one pane of glass is worth more than best-of-breed. The differences are real but smaller than the difference between having a labelled evaluation set and not having one.

Does LLM observability support OpenTelemetry?

Increasingly, and it is worth making a buying requirement. OpenTelemetry maintains GenAI semantic conventions defining spans, metrics and events for GenAI clients, along with conventions for the Model Context Protocol and provider-specific ones. Standardised traces mean your instrumentation is not rewritten when you change vendors, and your history stays queryable. Two honest caveats: the conventions are still stabilising, so treat OTel support as a portability hedge rather than a guarantee of drop-in migration, and coverage of agent and evaluation spans is less settled than plain model calls.

Instrument Second, Define Good First

If you are starting from nothing, the order that works is: decide what a correct answer looks like for your use case, collect thirty real cases where the system got it wrong, label them, and only then pick a platform to run that set inside. Teams that instrument first usually end up with excellent visibility into a system nobody can say is improving.

The tools in this comparison are all good. Any of them will trace an agentic system well enough to debug it, and the choice between them is mostly about hosting, existing stack and whether evaluation sits at the centre of your workflow or beside it. That decision deserves an afternoon. What deserves the rest of the quarter is the eval set - and if you want help putting that discipline in place around a system already in production, that is a good part of what I do.