9 Best AI Observability Tools for 2026

If the number you care about most is revenue, see how Amplitude Agent Analytics connects agent quality to user behavior, so you can analyze it like any other product data.

AI observability is the practice of tracing, measuring, and evaluating AI systems in production so you can see how they behave, what they cost, and whether their outputs hold up. For LLM apps and agents, that means capturing prompts, responses, tool calls, latency, token spend, and quality scores and, ideally, connecting them to what users do next.

Traditional monitoring wasn't built for this. A February 2026 Gartner report on AI evaluation and observability platforms puts it plainly: "The nondeterminism in GenAI and agentic AI applications makes it difficult to measure and improve reliability and earn user trust." A hallucination still returns a clean response, so uptime alone tells you little.

One related term often gets mixed in by mistake. AI visibility tracks how your brand shows up in ChatGPT or Google AI answers, which is a marketing problem rather than an observability one.

We judged every tool against seven criteria, each tied to a question your team will face once an agent is live. Outcome linkage carries the most weight, because this list is written for the teams who own what an agent is supposed to move.

Tracing depth. Can you follow a single request through every prompt, retrieval call, tool call, and response? Span-level detail is what lets engineers find where an agent went off course.

Evals. Does the tool score output quality with automated checks, LLM-as-judge scoring, or human review? The category listing on Gartner Peer Insights for AI evaluation and observability platforms describes tools that "create a positive feedback loop by feeding observability data (logs, metrics, traces) back to evals."

Latency and cost. Can you see token spend and response time per request, model, and prompt version? Spend can climb quietly when prompts grow.

Guardrails. Can the tool flag or block unsafe, off-policy, or incorrect outputs in production, not just report them later?

Integrations. How do you send data in? Proxies are fastest to set up, SDKs capture the most detail, and OpenTelemetry keeps you portable. The OpenTelemetry GenAI semantic conventions give the category a vendor-neutral way to describe this data.

Deployment. Open source and self-hosted, managed SaaS, or a hybrid? Data residency rules often decide this for you.

Outcome linkage. Does the tool connect quality scores to user behavior, such as funnels, cohorts, retention, and revenue? It's also the criterion closest to the "unclear business value" Gartner cites in its cancellation forecast.

We started from the vendors buyers see most often in this category, including those on Gartner Peer Insights, and kept tools built for production LLM and agent monitoring. We then reviewed each against the seven criteria above, using each vendor's published documentation and pricing as of October 2026.

No tool leads on all seven. Amplitude ranks first on the weighted criterion, outcome linkage, not because it out-traces the specialists. The other eight are grouped by primary strength: tracing and evals (LangSmith, Langfuse, Arize), then eval-first and guardrail tools (Braintrust, Galileo), then infrastructure, proxy, and test-suite tools (Datadog, Helicone, Confident AI).

1. Amplitude (Agent Analytics)

Agent Analytics turns agent activity into Amplitude events: every user message, tool call, agent response, and session end lands next to the funnels, cohorts, and retention curves your team already uses in Product Analytics.

Outcome linkage is where it leads the list. You can segment users by conversation quality and compare their conversion, retention, and revenue, and Amplitude automatically evaluates every closed session against a built-in set of signals, with no setup. From any session, View Replay opens Session Replay at the moment the conversation started, so you can see what the user did around it.

Getting data in is flexible. The Agent Analytics FAQ lists the Amplitude AI SDK, the HTTP API, OpenTelemetry, warehouse import, and integrations. With OpenTelemetry, you route the GenAI spans you already emit to Amplitude as a second destination, with no re-instrumentation.

Where it falls short: Agent Analytics isn't a replacement for a dedicated tracer. The same FAQ positions it alongside tools like LangSmith, Langfuse, Braintrust, and Datadog, noting that most teams run all three layers: observability, offline evals, and Agent Analytics.

G2 rates the Amplitude platform 4.5 out of 5 across 2,997 reviews. A software engineer at a mid-market company says, "The thing I appreciate most is how quickly I can get from a question to an actual answer." A product manager adds that "session replay has been genuinely useful for spotting exactly where users drop off in a flow."

Pricing: Freemium. Free includes 2M events per month and 5,000 monthly agent sessions; Plus starts at $0 (first 2M events per month free) and scales up to 70M events, per Amplitude's pricing page (as of October 2026).

Best for: Product and growth teams that own the metric an agent is supposed to move.

2. LangSmith (LangChain)

LangSmith, from the LangChain team, is where many engineers start when an agent gets complicated. It records each step of a run, so you can replay where reasoning broke.

Tracing depth is its strongest criterion, and evals sit right beside the traces. Production traces become datasets that feed evals, which guide the next change. Token cost and latency show per run.

It runs as a cloud service on its standard plans, with hybrid and self-hosted deployment reserved for Enterprise. Data comes in through Python and TypeScript SDKs or OpenTelemetry. There's no native output blocking, though its LLM Gateway can redact PII and secrets. Tying a trace to retention means exporting data.

Product managers will struggle here. The interface centers on runs, not users, so there's no cohort view of agent quality.

G2 rates LangSmith 4.4 out of 5 across 78 reviews. A senior software engineer says, "What I like most about LangSmith is how much easier it makes debugging AI applications."

Pricing: Free Developer plan with 1 seat and 5,000 base traces a month; Plus costs $39 per seat a month with 10,000 base traces, per LangChain pricing (as of October 2026).

Best for: Engineering teams debugging multi-step agents.

3. Langfuse

Langfuse is one of the most widely used open-source options for LLM tracing, released under the MIT license. It suits teams that would rather run their observability stack than rent it.

Deployment is where it stands apart. You can self-host it with Docker, Kubernetes, or Terraform to meet data residency rules, or use Langfuse Cloud. Tracing and evals cover the core of LLM observability, with latency and cost tracked per request. Data arrives through Python and JS/TS SDKs, native OpenTelemetry support, or gateway integrations such as LiteLLM.

Guardrails aren't native: Langfuse's docs pair it with separate LLM security libraries for runtime protection. Outcome linkage isn't native either, so business questions need a warehouse join. Self-hosting also means ownership. Your team runs, patches, and scales it, and that work grows with traffic.

Langfuse has too few reviews on G2 and Capterra for a reliable rating yet.

Pricing: Open source and free to self-host. Langfuse Cloud's free Hobby plan includes 50,000 units a month, 30 days of data access, and 2 users; Core starts at $29 a month, per Langfuse pricing (as of October 2026).

Best for: Data-sensitive orgs that want open-source tracing on their own infrastructure.

4. Arize (Phoenix and AX)

Arize spans classic ML monitoring and LLM observability. Phoenix is its source-available tool under the Elastic License 2.0, and Arize AX is the commercial platform.

Tracing and evals are strong in both, and Phoenix offers a free, self-hostable start. Teams running recommendation models alongside LLM apps can watch both in one vendor. LLM workloads get latency and cost tracking by default.

Both products accept OpenTelemetry and OpenInference data through Python and TypeScript. Arize AX adds Guards, which can block or correct outputs at run time. It doesn't connect quality to retention or revenue on its own. All that breadth has a cost: two products can feel heavy if you only run one LLM feature.

G2 rates Arize AX 4.4 out of 5 across 76 reviews. A technical project manager says, "Arize AI has made it much easier to monitor model performance in production and catch issues like data or concept drift before they significantly impact output quality."

Pricing: Phoenix is free to self-host. Arize AX Free includes 25,000 spans a month with 15-day retention; AX Pro starts at $50 a month, per Arize pricing (as of October 2026).

Best for: ML platform groups running traditional models and LLM apps side by side.

5. Braintrust

Braintrust puts evals first and builds observability around them. It's one of the more flexible tools on deployment and integration.

Scoring is the core of the product, with production traces feeding the same evals you run before release. It connects through Python and TypeScript SDKs, OpenTelemetry, or its gateway, and runs as SaaS, with a self-hosted data plane available on Enterprise. Each scored trace also shows its response time and token spend.

Runtime guardrails aren't native, and outcome linkage stops at the model, so product metrics live elsewhere. Expect a heavier start too. An eval-first workflow asks you to define datasets and scoring up front, which takes more effort than passive logging.

G2 rates Braintrust 4.3 out of 5 across 58 reviews. A developer says, "I like how Braintrust makes it easier to evaluate and monitor AI applications in one place."

Pricing: Free Starter plan with 1 GB of processed data, 10,000 scores, and unlimited users; Pro costs $249 a month, per Braintrust pricing (as of October 2026).

Best for: Engineering orgs that gate every release on eval results.

6. Galileo

Galileo focuses on agent observability in production, with guardrails at the center. It's built for teams that want to catch bad outputs, not just log them.

Guardrails lead its scorecard. Galileo has replaced its earlier Protect feature with Agent Control, which blocks harmful content, prompt injection, and PII leakage, and evals and agent tracing support it so you can see why an output was flagged. Response time and spend are covered at a basic level. Runtime protection at scale relies on Galileo's Luna-2 evaluators on its enterprise tier, or on custom code-based metrics.

It deploys as SaaS, in a virtual private cloud, or on-premises, and connects through Python and TypeScript SDKs or OpenTelemetry. Outcome linkage to product metrics isn't a focus. Not every feature needs this much protection, either. For a low-risk internal tool, a guardrail-centered platform may be more than you'll use.

We couldn't match Galileo to a reliable G2 or Capterra listing, so we haven't included a rating.

Pricing: Galileo's pricing page now points to Splunk Agent Observability, which starts at $100 a month per 1.2 million spans, billed annually, per Splunk pricing (as of October 2026).

Best for: Regulated or customer-facing products with strict output-safety needs.

7. Datadog Agent Observability

Datadog Agent Observability, formerly LLM Observability, adds LLM traces to the infrastructure monitoring many enterprises already run. Its appeal is one place for app, infra, and model telemetry.

For existing customers, integration is the draw, since LLM traces sit next to the services and hosts they depend on. Data comes in through Python, Node.js, and Java SDKs, OpenTelemetry, or an HTTP API. Latency, errors, and cost are well covered, and tracing follows requests across your stack.

Evals are present but less central than in eval-first tools, and runtime blocking comes from a separate product, AI Guard. It runs as SaaS within your Datadog account, and outcome linkage to user behavior isn't native. It lives in an operations tool, so product teams rarely open it, and its value drops if you don't already use Datadog.

G2 rates the Datadog platform 4.4 out of 5 across 735 reviews. A DevOps manager at an enterprise company says, "The trace views are invaluable when there's an outage."

Pricing: Free for up to 40,000 LLM spans a month with 15-day retention; Pro starts at $160 a month (billed annually) for 100,000 LLM spans, per Datadog pricing (as of October 2026).

Best for: Enterprises already standardized on Datadog.

8. Helicone

Helicone is a proxy-based, open-source tool under the Apache 2.0 license and among the quickest to instrument on this list. You route model calls through its AI Gateway, and data starts flowing.

Setup takes minutes. Request logs, latency, and cost data arrive with little code, which makes it useful for spotting spend problems early. You can also self-host it with Docker or Kubernetes, or log asynchronously instead of proxying if you'd rather not add a hop. Because it sits in the request path, it also handles caching, rate limits, and fallbacks between model providers.

Helicone's LLM Security feature can block detected prompt threats and return an error instead. Evals and deep agent tracing are thinner than in LangSmith or Braintrust, and outcome linkage isn't offered. The proxy model cuts both ways: it adds a hop to every request, and it shows individual calls more clearly than multi-step reasoning.

Helicone has too few reviews on G2 for a reliable rating yet.

Pricing: Free to self-host under Apache 2.0. The hosted Hobby plan includes 10,000 requests a month with 7-day retention; Pro costs $79 a month, per Helicone pricing (as of October 2026).

Best for: Small teams that want cost and latency visibility this week.

9. Confident AI (DeepEval)

Confident AI takes a test-suite approach to LLM quality. Its open-source DeepEval framework, under the Apache 2.0 license, lets engineers write evals the way they write unit tests.

Evals come first, with tests that can run before every release. The platform adds production monitoring on top, so the same checks apply to live traffic. Tracing and cost tracking support that quality focus.

The platform runs as SaaS, with self-hosting on Enterprise plans, and ingests traces through the DeepEval Python SDK and OpenTelemetry. Runtime guardrails aren't part of the hosted platform; its open-source DeepTeam library offers input and output guards. Outcome linkage isn't part of the design, and teams wanting infra telemetry or user-level analysis will need other tools alongside it.

G2 rates Confident AI 4.6 out of 5 across 5 reviews, a small sample. A back-end developer says, "I really like the great open source CLI tool that Confident AI provides to run and develop my evals."

Pricing: DeepEval is open source and free. The platform's free plan includes 2 seats and 1 project; Starter costs $200 a month, per Confident AI pricing (as of October 2026).

Best for: Developers who treat prompts and agents like code under test.

Pick the tool that answers the question your team gets asked most. The criteria above point to clear matches:

• Deep debugging: LangSmith or Arize, when engineers need span-level detail on complex agents.
• Open-source self-hosting: Langfuse, Helicone, DeepEval, or source-available Arize Phoenix, when data control matters most.
• Eval-first quality: Braintrust or Confident AI, when releases depend on test results.
• Production guardrails: Galileo, when bad outputs carry real risk.
• Enterprise infrastructure: Datadog, when your stack already runs there.
• Product outcomes: Amplitude Agent Analytics, when leadership asks whether the agent grows the business.

Pairing a tracer with an outcome layer is common.

Product teams pick Amplitude because agent quality and user behavior share one data foundation. Amplitude brings Product Analytics, Agent Analytics, Session Replay, and Feature Experimentation into one platform.

That shared foundation shortens the path from signal to fix. CashBook, a Y Combinator-backed finance app for small businesses in India, wired its AI agent to Agent Analytics and let an automated judge grade every session. That turned a hunch into a named defect: a 16% error rate in one internal tool. It also showed why quality matters beyond the agent. Eighty-six percent of sessions passed the response-quality check, yet by week four, users whose sessions failed retained at 63.9%, against 79.2% for everyone else.

From there, Feature Experimentation lets you test prompt and model changes with quality as the metric, and Amplitude AI Agents can run automated diagnostics on what changed and why.

The specialist tools on this list keep your models fast, safe, and correct. Amplitude answers the next question: did that correct answer help someone sign up, stay, or pay?

The next wave of AI observability won't stop at the model. It will feed what users do back into prompts, experiments, and roadmaps, so the product improves along with the agent.