12 AI observability and evaluation tools for agents, compared

Your agent returned a fast, clean, error-free response. Your APM dashboard is all green.

The answer was still wrong.

That's the gap AI observability and evaluation tools are meant to close. There are a lot of them now, and they don't all do the same job. Here's a practical comparison of 12, with pricing and trade-offs, so you can pick the right one for where your agent is today.

Disclosure: I work at BotGauge, which is on this list. I've tried to keep the trade-offs honest, including ours. Pricing was checked in October 2026, so confirm with each vendor before you buy.

Three different jobs

• Evaluation scores outputs against criteria you define.
• Observability records traces, cost, latency, and quality on live traffic.
• Red teaming attacks the agent to find failures nobody has written a test for yet.

Most tools are strong at one or two. Very few cover all three, so knowing which job you need done is half the decision.

What to look for

• Evaluation depth: scoring for faithfulness, relevance, safety, and goal completion, using metrics, LLM judges, or your own code checks. A trace without a score tells you what happened, not whether it was good.
• Agent-level tracing: every prompt, tool call, retrieval, and branch, not just the final answer. Multi-step agents fail in the middle.
• Failure discovery: test sets only cover what someone thought of. This matters most for agents that move money or change records.
• Quality alerts: alerts when faithfulness or safety scores drop, not just on latency and errors.
• A production-to-test loop: failures found in production should become regression tests that run in CI.
• Team fit: SSO, access control, predictable pricing, and whether PMs and QA can use it without an engineer.

The comparison

Tool	Best for	Open source	Entry price
BotGauge	Red teaming through monitoring, for agents that take actions	Not stated	Not published
Braintrust	Evals, tracing, and CI gates in one platform	No	Free; Pro $249/mo
Confident AI	Quality checks PMs and QA can run	Metrics library (DeepEval)	Free; Starter $200/mo
LangSmith	LangChain and LangGraph teams	No	Free; Plus $39/seat/mo
Langfuse	Self-hosted tracing and prompt management	Yes (core)	Free; from $29/mo
Arize Phoenix	Notebook-first tracing	Yes	Free; cloud from $50/mo
Galileo	Cheap evaluators and runtime guardrails	No	Free; Pro from $100/mo
LangWatch	Multi-agent and voice simulation	Yes	Free; from €29/seat/mo
Pydantic Logfire	LLM and app traces in one place	Partial	Free; Team from $49/mo
Latitude	Turning failures into fix PRs	Yes	Free; Pro $99/mo
Datadog LLM Obs.	Teams already on Datadog	No	Usage-based
Fiddler	Regulated enterprises	No	Custom

Quick takes

BotGauge runs adaptive red-team campaigns against your agent, then turns findings into evals that run on every release, with tracing and monitoring after launch. Watch out for: the red teaming product is waitlist-based right now, pricing isn't public, and there's no LLM gateway or token-cost analytics.

Braintrust turns a production trace into a test case in one click, has 25+ built-in scorers, and a GitHub Action that can block a PR on a quality regression. Watch out for: self-hosting is Enterprise only, and $249/mo is steep for small teams.

Confident AI scores every trace with 50+ metrics, alerts through Slack or PagerDuty, and lets non-engineers run evals. It also has a red teaming module. Watch out for: GB-based billing takes a while to forecast.

LangSmith shows the full execution tree of an agent run, with annotation queues for expert review. Watch out for: depth drops outside LangChain, and per-seat pricing adds up.

Langfuse is open-source tracing, prompt versioning, and cost tracking you can host yourself. Watch out for: you mostly bring your own evaluators.

Arize Phoenix runs locally in a notebook or Docker, with clustering that surfaces groups of degrading traces. Watch out for: the interface is less polished than commercial tools.

Galileo uses small, fast evaluator models that cost far less than an LLM judge, and can turn evals into runtime guardrails. Watch out for: a younger platform with a smaller community.

LangWatch is open source, visualizes multi-agent runs, and can turn a production failure into a CI simulation. Watch out for: not built for infrastructure-level monitoring.

Pydantic Logfire puts HTTP requests, agent loops, model calls, and database queries in one OpenTelemetry trace, queryable with SQL. Watch out for: evals lean on custom rubrics more than preset metrics.

Latitude clusters recurring failures into tracked issues and can hand them to your coding agent to open a fix PR. Watch out for: no LLM gateway, and it's built for engineers more than PMs.

Datadog LLM Observability puts LLM spans right next to your existing APM traces and logs. Watch out for: evaluation is secondary, and usage pricing can be hard to predict.

Fiddler covers classic ML and LLMs with fast guardrails, drift detection, and audit trails. Watch out for: custom pricing and an enterprise-sized learning curve.

The pattern: eval sets only measure what someone thought to test, and production traces only teach you about failures that already reached users. If your agent just answers questions from a knowledge base, an eval and tracing platform is often enough. If it takes real actions, you'll want something that hunts for failures before launch too.

The full write-up, with longer reviews and an FAQ, is in comparison of AI observability and evaluation tools.

What are you using to monitor your agents in production, and what do you wish it did better? Share in the comments.