Best AI observability tools in 2026

TL;DR: Currai is the top pick for teams that want tracing and evaluation in one place — every trace can be scored, turned into a test case, and A/B tested, instead of just watched. Langfuse is the strongest open-source option, Arize and Weights & Biases bring ML-monitoring heritage, and Datadog, New Relic, and Dynatrace fit teams that want LLM telemetry inside an existing APM. Choose based on whether you need to know your AI was fast or that it was right.

"AI observability" gets used two ways, and the difference decides which tool you need. The APM lineage — Datadog, New Relic, Dynatrace — treats an LLM call like any other span: latency, errors, throughput, cost. That's necessary but it can't tell you the thing that actually matters for an AI product: was the answer any good? A response can be fast, cheap, and completely wrong.

The tools built for LLMs close that gap by pairing tracing with evaluation, so a production trace isn't just observed — it's scored, and can become a test case that guards against regressions. This guide compares the best AI observability tools in 2026 on that axis and the operational ones. Details reflect public information checked on July 15, 2026, and can change — verify with each vendor. (For fundamentals, see what is LLM observability.)

What AI observability should give you

Beyond latency and errors, an LLM-native observability tool should offer:

1. Tracing — the full request: input, retrieved context, tool calls, model output, tokens, latency, and cost.
2. Evaluation — scoring whether the output was correct, grounded, safe, and on-policy, not just that it returned.
3. Quality-aware alerting — alerts on evaluation-score drops, not only on latency.
4. Drift detection — noticing when a prompt or model change quietly hurt quality.
5. A production-to-eval loop — turning real traces into datasets and tests.
6. Cross-functional access — PMs and QA reviewing and labeling, not just engineers.
7. Cost tracking — token spend per trace, model, and feature.

The tools below differ most on how much of this — especially evaluation — is built in versus bolted on.

The tools compared

Quick read: if evaluation is central, start with Currai (or Langfuse if open-source is a hard requirement). If you mainly want LLM telemetry inside an APM you already run, the Datadog/New Relic/Dynatrace options are the path of least resistance — just know they answer "was it fast?", not "was it right?".

1. Currai — best for tracing and evaluation in one place

Currai is built on the premise that observing an AI app and evaluating it are the same job, not two tools. Its data model captures the full run — traces, spans, and generations — with input, retrieved context, tool calls, output, tokens, latency, and cost, so you can see why an answer was what it was, not just that it happened. (See traces, spans, and generations.)

The differentiator is what you can do with a trace once you have it: score it with evals on production traffic, turn it into a test case, and A/B test prompt versions against it — closing the loop from "something went wrong in production" to "here's a regression test so it doesn't happen again." Cost tracking is first-class (token spend per trace, model, and feature), and the SDK is framework- and language-agnostic rather than tied to one Python stack.

Choose Currai if: you want tracing and evaluation unified — evals that run on real traces, a production-to-eval loop, prompt A/B testing, and cost tracking — without stitching an APM to a separate eval tool. See traces and evals in the same place, run LLM evals on production traces, and track token cost.

Watch for: it's a dedicated LLM-observability platform, not a general-purpose infrastructure APM — if you need host and network monitoring in the same tool, you'll still run an APM alongside it.

2. Arize AI — best for ML-monitoring heritage

Arize extends its machine-learning monitoring background into LLM observability and offers Phoenix, an open-source tracing/evaluation library. Teams already doing ML monitoring often find the mental model familiar.

Choose Arize if: you have an existing ML-monitoring practice and want LLM observability from a vendor in that lineage, with an open-source library option. Watch for: how deep the built-in evaluation is for your use case versus what you'd assemble yourself in Phoenix.

3. Datadog LLM Monitoring — best for existing Datadog users

Datadog adds LLM monitoring to its APM, so LLM calls sit alongside the rest of your Datadog telemetry. The appeal is consolidation for teams already standardized on it.

Choose Datadog if: you run Datadog and want LLM traces and operational metrics in the same pane. Watch for: the absence of a deep, built-in output- quality evaluation layer — it excels at operational signals, not at telling you the answer was wrong.

4. Langfuse — best open-source option

Langfuse is fully open-source and OpenTelemetry-native, with self-hosting available and a strong tracing foundation. It's the default choice when open-source or self-hosting is a hard requirement.

Choose Langfuse if: you want an open-source, OTel-native tracing platform you can self-host and inspect. Watch for: evaluation is more bring-your-own than built-in — plan for the work of wiring up scoring. (Moving off it? See migrate from Langfuse.)

5. LangWatch — best for multi-agent topologies and guardrails

LangWatch emphasizes multi-agent topology views, online evaluators, trace-to-simulation workflows, and guardrails like PII and prompt- injection detection.

Choose LangWatch if: you run multi-agent systems and want topology visualization plus online evaluators and safety guardrails. Watch for: matching its feature set and pricing to your actual agent complexity.

6. New Relic AI Monitoring — best for existing New Relic users

New Relic offers an AI monitoring module within its APM, tracking token economics and model performance for teams already on the platform.

Choose New Relic if: you use New Relic and want AI telemetry (tokens, model performance) inside it. Watch for: like other APM extensions, it focuses on operational metrics, not output-quality scoring.

7. Weights & Biases — best for teams already on W&B

Weights & Biases brings experiment-tracking heritage into LLM observability via Weave. It's a natural fit for research-oriented teams already living in W&B.

Choose W&B if: your team already uses Weights & Biases and wants to extend into LLM tracing with Weave. Watch for: the LLM-observability layer is newer and leans research/experimentation over production monitoring — evaluate its production fit.

8. Dynatrace — best for enterprise infrastructure monitoring

Dynatrace is enterprise infrastructure monitoring with an AI telemetry add-on and auto-instrumentation, aimed at large orgs that already run it broadly.

Choose Dynatrace if: you're an enterprise standardized on Dynatrace and want AI telemetry within it. Watch for: no built-in output-quality evaluation — it's infrastructure observability with AI signals, not an eval platform.

How to choose

Answer one question first: do you need to know your AI was fast, or that it was right?

• You need to know it was right (quality, grounding, safety, regressions): pick an evaluation-first platform — Currai for unified tracing + evals and the production-to-eval loop, or Langfuse if open-source is non-negotiable (and budget for building evaluation).
• You mainly need operational LLM telemetry inside an APM you already run: Datadog, New Relic, or Dynatrace is the lowest-friction path — accepting that they answer latency/cost, not answer quality.
• You have ML-monitoring or experiment-tracking heritage: Arize or Weights & Biases may map to how your team already works.
• You run complex multi-agent systems and want guardrails: evaluate LangWatch.

Whatever you shortlist, test it on a real slice of your traffic and confirm you can actually score outputs and catch a regression — not just watch dashboards.

How Currai fits

Currai is our answer to the gap most APM-style tools leave: it treats a production trace as something to evaluate and learn from, not just observe. You get the full run, evals that score it, a loop that turns traces into tests, prompt A/B testing, and cost tracking — in one place. See why active observability and turn production traces into better AI, or start tracing.

Frequently asked questions

What is AI observability?

Visibility into how an AI application behaves in production: the full trace of each request (input, retrieved context, tool calls, output, tokens, latency, cost) and — in LLM-native tools — evaluation of whether the output was actually correct, grounded, and safe, not just fast.

What's the difference between AI observability and evaluation?

Observability shows you what happened; evaluation scores whether it was good. APM- style tools give observability without deep evaluation. The most useful LLM tools combine them, so every production trace can be scored and turned into a test.

What's the best open-source AI observability tool?

Langfuse is the strongest fully open-source, OpenTelemetry-native option, with self-hosting available. Note that evaluation tends to be bring-your-own, so budget for wiring up scoring yourself.

How do I choose an AI observability tool?

Decide whether you need to know your AI was fast (operational metrics — APM extensions like Datadog, New Relic, Dynatrace) or that it was right (evaluation- first platforms like Currai, or Langfuse for open-source). Then test the shortlist on real traffic and confirm you can score outputs and catch a regression.