12 LLM Observability Tools Compared in 2026

Once an LLM application is in production, "it worked in my testing" stops being useful information. You need to see what actually got sent, what came back, what it cost, how long it took, and whether a chain of tool calls broke somewhere in the middle. That's the job of an LLM observability tool: recording what happened in enough detail that you can debug it after the fact. This post covers 12 real, currently maintained tools, what each one actually captures, and how it's deployed, plus where observability's job ends and a different kind of tool takes over.

What LLM observability actually means

Langfuse's own documentation describes the core mechanic plainly:

Application tracing records the complete lifecycle of a request as it flows through your system.

That's the shared foundation across every tool on this list: instrument your application, capture spans for each LLM call, retrieval step, and tool invocation, and store the result as a trace you can inspect later. On top of tracing, most of these products add evaluation (scoring whether an output was any good, often using another LLM as a judge), cost and latency tracking, and datasets built from real production traffic for testing prompt or model changes before shipping them.

The 12 tools

Langfuse. Open source and self-hostable, with a hosted option; as of 2026 it's part of ClickHouse. Captures traces natively understanding LLM-specific concepts like token usage and prompt/completion pairs, supports LLM-as-a-judge and code-based evaluators, and includes prompt version management and datasets for structured testing. (Langfuse docs)

Helicone. Open source (Apache 2.0), deployable as a proxy in front of most providers for unified logging of prompts, cost, latency, and errors with minimal code change. Worth knowing before you commit: Mintlify acquired Helicone in March 2026 and moved the hosted product into maintenance mode, meaning security patches and new-model support continue but active feature development has stopped; the self-hosted version remains fully open source. (TrueFoundry, "Helicone Pricing 2026"; AIToolPick, "Helicone Pricing 2026")

Arize Phoenix. Open source, runs locally with a single function call or self-hosted in your own cloud, no account required for the local version. Captures distributed traces via OpenTelemetry and OpenInference auto-instrumentation, and runs over 50 research-backed evaluation metrics covering hallucination detection, relevance, and faithfulness. (Arize, Phoenix product page)

Datadog LLM Observability. Hosted, part of the existing Datadog APM platform. Automatically traces and annotates LLM calls with latency, errors, and token usage, plus cost estimation, evaluations, and sensitive data scanning, aimed at teams already standardized on Datadog for the rest of their stack. (Datadog, LLM Observability)

LangSmith. Hosted, built by the LangChain team but framework-agnostic. Traces individual LLM and agent steps, supports offline evaluation against datasets and online LLM-as-judge scoring, and prices trace volume separately from seats: a free tier, a Plus tier at $39 per seat per month, and usage-based charges of $0.50 per 1,000 base traces after the free allotment. (Inference.net, "LangSmith Pricing Explained")

W&B Weave. Hosted, from Weights & Biases. A single decorator around a function call captures inputs, outputs, cost, and latency automatically, and Weave Guardrails adds pre-built scorers for toxicity, bias, PII, and hallucination alongside quality metrics like coherence and relevance. (Weights & Biases, W&B Weave)

Braintrust. Hosted, positioned around three pillars: observability, evals, and automation. Captures real-time traces of prompts, responses, and tool calls, runs LLM-as-judge and custom code scorers against datasets, and turns production failures into test cases for CI/CD. Pricing includes a free Starter tier and a Pro tier at $249 per month. (Voiceflow, "What Is Braintrust AI?"; Cekura, "Braintrust Pricing in 2026")

PromptLayer. Hosted, built specifically around prompt engineering workflows. Captures timestamps, latency, token usage, and success/failure per request, with prompt version control that lets you annotate changes and compare outputs across versions, plus OpenTelemetry support for broader tracing. (PromptLayer, Observability)

Comet Opik. Open source (Apache 2.0), self-hostable in full including tracing, evaluation, and production monitoring, with a hosted option adding team management. Captures full trace trees for multi-step agents and tool calls, and supports online evaluation that scores traces in real time using an LLM as a judge. (Comet, Opik product page)

Traceloop (OpenLLMetry). OpenLLMetry itself is open source instrumentation built on OpenTelemetry, with Traceloop offering a hosted platform on top. Because it targets the open OTLP standard rather than a proprietary SDK, the same traces can be routed to Datadog, Honeycomb, or a self-hosted collector just by changing the destination, which makes it a reasonable choice if you don't want to commit to one vendor's trace format. (GitHub, traceloop/openllmetry)

New Relic AI Monitoring. Hosted, extending New Relic's existing APM platform to LLM and agent workloads. Covers over 50 integrations with LLMs, ML libraries, and frameworks, with an Agent Service Map for multi-agent systems and monitoring of cost, tool utilization, and quality issues like bias and hallucination. New Relic has reported 30% quarter-over-quarter growth in AI Monitoring adoption. (New Relic, AI monitoring docs)

Honeycomb. Hosted, extending its existing distributed-tracing product to LLM and agent traffic via OpenTelemetry's gen_ai semantic conventions. Lets you follow every prompt, model call, and tool handoff in one view with token cost and failures inline, and its Canvas feature lets you query telemetry in natural language rather than writing a query language by hand. (Honeycomb, "What Is LLM Observability and Monitoring?")

How they compare on deployment and focus

Sources: vendor documentation and product pages linked in each entry above.

Observability versus guardrails: two different jobs

This is worth being direct about, because the two get conflated constantly. Observability records what happened: it gives you traces, costs, and evaluation scores you can look at after a request completes, and use to debug or improve the system over time. It does not, by itself, stop a bad answer from reaching a user. Every tool on this list is fundamentally a recording and analysis layer; even the ones with "evaluation" features are mostly scoring output after the fact, in a dashboard or a CI run, not blocking it inline before it ships.

Guardrails and verification are a different job: acting on a check's result inline, before the response goes out, and doing something (blocking, retrying, escalating to a different model) when it fails. llm11 sits in that second category, not the observability one. It runs a verification pass on every request, schema and groundedness checks always, heavier checks on requests that earn them, and escalates once to a stronger model when a check fails, all before the response reaches your application. The how-it-works page covers that inline mechanism and the receipt it produces per request, which is closer to a verification log than a full observability platform: it tells you what was checked and what happened as a result, not a complete trace of every span in a multi-step agent.

If cost is your primary concern rather than trace-level debugging, our piece on AI cost optimization covers where the tracking most of these tools do overlaps with actually reducing spend, and where it doesn't by itself. Several of these tools also compete directly on the evaluation side rather than pure tracing, which our Langfuse alternatives comparison covers in more depth if that's the specific decision in front of you.

Do you need a dedicated tool, or does your gateway already log enough?

Most gateways log requests and responses at a basic level: what was called, what it cost, how long it took. That's useful, but it's typically shallower than what a dedicated observability tool captures, especially for multi-step agents where you need to see the full tree of tool calls and sub-generations, not just the top-level request. If you're running a single model call per request, gateway-level logging is often enough. If you're running agents with several steps, retrieval, and branching logic, a dedicated tracing tool earns its place because that's specifically the shape of data it's built to capture.

Which one should you actually pick

Start with what you're trying to answer. If the question is "did this specific request work correctly," pick a tool with strong per-trace inspection and evaluation, Langfuse, Arize Phoenix, or Comet Opik are all open source enough to try without a procurement process. If the question is "what is my LLM traffic costing me and where," several of these tools track cost well but a router that lowers the cost directly, rather than just reporting it, addresses the underlying problem instead of just measuring it, which is the gap /compare is built to make legible against your actual traffic. If you're already standardized on Datadog, New Relic, or Honeycomb for the rest of your infrastructure, extending that same platform to LLM traffic avoids a second tool and a second dashboard to check.

Frequently asked questions

Is LLM observability the same as monitoring?

They overlap but aren't identical. Monitoring usually implies dashboards, alerts, and thresholds on metrics like latency or error rate. Observability is the broader capability that lets you ask arbitrary questions about what happened in a specific request after the fact, which is what tracing is built to support.

Do I need a paid tool, or is open source enough?

Several strong options, Langfuse, Arize Phoenix, Comet Opik, and Helicone's self-hosted version, are open source and free to run yourself. The tradeoff is the same one as any self-hosted software: you own patching, scaling, and uptime instead of a vendor doing it for you.

Can an observability tool stop a bad answer before it reaches a user?

Generally no. Most of these tools score or flag an answer after it has already been generated and, in most integrations, after it has already been returned to the caller. Stopping a bad answer before it ships is the job of an inline verification or guardrails layer, which is a different architecture from after-the-fact tracing.

Does adding observability slow down my application?

Well-instrumented tracing adds a small amount of overhead, since it usually runs asynchronously or in a background thread rather than blocking the response. The bigger latency cost in most LLM applications is the model call itself, not the instrumentation wrapped around it.

How many of these tools can I use together?

More than one, though most teams settle on a primary one to avoid duplicating trace storage and evaluation configuration. It's common to pair an open-source tracing tool with a platform-specific one, for example using OpenTelemetry-based instrumentation like Traceloop's so the same traces can be sent to more than one backend without re-instrumenting your code.