12 Best AI Observability Tools & Platforms (2026)

The label "AI observability" now covers products that do very different jobs. Some were built for LLM workloads and capture every step of an agent run. Some are routing layers that added analytics. Some are established APM platforms that added an AI tab to dashboards designed for microservices. All of them sell under the same phrase, and the differences only surface when something breaks.

The market is also consolidating. ClickHouse acquired Langfuse in January 2026. Mintlify acquired Helicone in March and moved it to maintenance mode. Choosing an AI observability platform now means choosing its roadmap, and several of those roadmaps changed hands this year.

In this article, we'll cover the 12 best AI observability tools and platforms that you can use to monitor agents in production in 2026.

What to look for in an AI observability tool

What an AI observability tool can report is bounded by where it sits in your stack. A proxy receives HTTP requests, so it can report tokens, latency, and cost per call, and it never sees the reasoning between two calls. An APM agent instruments the service, so it reports spans and error rates, and the completion text is a payload it was not built to read. SDK and OpenTelemetry instrumentation runs inside the application, so it can capture the whole run including tool arguments and retrieved documents.

Those positions determine the ceiling. Everything below is a question about how close a given platform gets to its own ceiling, and which ceiling you need:

• Trace granularity for agent runs - The unit that fails in an agent system is the run, not the request. A support agent that calls a retrieval tool, reasons over the result, calls a second tool, and drafts a reply produces one visible outcome and a dozen internal steps. Request-level logging shows a successful transaction that produced a bad result. The full tree shows which step introduced the error.

• Output-quality evaluation, not just telemetry - An LLM call can return HTTP 200 in 400ms and hallucinate a refund policy. Latency, error rate, and token count are all fine, and the output is wrong. Evaluation scores responses on faithfulness, relevance, and safety. The distinction that matters is whether it runs on live production traffic or only on offline datasets assembled by hand.

• Cost and token attribution - A single monthly invoice from OpenAI provides nothing actionable. Attribution by model, feature, customer, and prompt version identifies which change doubled spend and when. This is the metric most likely to move without anyone noticing, because nothing breaks. Spend climbs while every dashboard stays green.

• Alerting on quality, not only errors - Alerting inherited from infrastructure monitoring fires on HTTP status and latency thresholds, which are the failures LLM systems are least likely to produce. Monitors on cost, token volume, and evaluation scores matter more, scoped narrowly enough to be useful. An org-wide threshold pages constantly and gets muted within a week.

• OpenTelemetry support and trace portability - OpenTelemetry has GenAI semantic conventions for LLM spans, so instrumentation written once can export to more than one backend. That is the difference between switching vendors in an afternoon and reinstrumenting a codebase. Proprietary SDKs carry the highest lock-in risk, OTel-native instrumentation the lowest.

• Self-hosting and data residency - Prompts and completions routinely contain the data a compliance team cares about: health records, financial detail, personal identifiers. Check the license carefully. Several platforms describe themselves as open source while gating self-hosting, or the compliance features that make self-hosting worthwhile, behind an enterprise tier.

• Gateway and routing - Routing, fallbacks, and caching are usually a separate purchase from observability, which means the record of which model served a request sits apart from the record of how that request performed. Whether that matters depends on whether you run multiple providers. If you do, the seam between the two tools becomes the thing you debug.

Two of those seven are worth weighting above the rest. OpenTelemetry support determines how expensive it is to be wrong about this decision later, which matters because most teams are choosing before they know what their production traffic looks like. Evaluation on live traffic determines whether the platform can surface the failure mode that motivated the purchase in the first place, which is almost never a latency spike.

How these tools were evaluated

Each platform was assessed on seven dimensions: trace depth for multi-step agent runs, evaluation capability on production traffic, cost attribution granularity, alerting beyond operational metrics, OpenTelemetry support, self-hosting and licensing, and gateway functionality.

AI observability tools compared

The 12 best AI observability tools and platforms

1. Respan

Type: LLM observability platform with built-in gateway · Price: Free tier, paid tiers by volume · Self-host: Enterprise

Respan runs tracing, evaluation, prompt management, and model routing on one platform. Route, observe, and evaluate every LLM call. A trace carries the prompt version that produced it and the routing decision that served it, which removes the correlation step that eats most of the time in a multi-tool setup.

The observability layer tracks requests, tokens, errors, latency, and cost broken down by model, user, and API key, with LLM and tool steps rendered as a single trace tree. Monitors watch error rate, cost, latency, or tokens and alert Slack, email, or a webhook on breach, scoped by model, project, environment, or user. Instrumentation is the Respan SDK or OTLP. The AI gateway routes to 250+ models with failover, response caching, and per-key spend limits. Respan is SOC 2, ISO 27001, GDPR, and HIPAA compliant, with a BAA available.

Pricing: A free tier covers evaluation and small production workloads. Paid tiers scale with volume, and self-hosting is available on Enterprise.

Best fit: teams running agents in production who would rather debug one system than four.

2. Langfuse

Type: Open-source LLM observability platform · Price: Free tier, from $29/mo · Self-host: Yes

Langfuse is a tracing backbone with prompt management attached. The MIT-licensed core is usable self-hosted rather than a limited demo build, OpenTelemetry support is solid, and the trace UI handles multi-step runs without falling over. Callback handlers cover OpenAI SDK, LangChain, LlamaIndex, LiteLLM, Vercel AI SDK, Haystack, and Mastra.

Where it falls short: Langfuse logs traces and does not score them. There are no built-in metrics for faithfulness, relevance, or hallucination, so quality monitoring means writing LLM-as-judge logic or paying for a second tool. There is no native alerting on quality degradation either, which means the platform will record a regression accurately and tell nobody. Self-hosting in production is Postgres plus ClickHouse plus application containers, so "free" is a licensing statement rather than a cost statement. Native SDKs are Python and TypeScript only. ClickHouse acquired Langfuse in January 2026 and roadmap direction under new ownership is not yet clear.

Pricing: Self-hosting the MIT core is free in licensing and costs whatever the infrastructure costs. Managed cloud has a free tier, paid plans from around $29 per month, and Enterprise from roughly $2,499 per year.

Best fit: engineering teams with data residency requirements and the appetite to build their own evaluation layer. See Langfuse alternatives.

3. LangSmith

Type: Observability and evaluation platform · Price: Free tier, from $39/seat/mo · Self-host: Enterprise only

LangSmith ships from the LangChain team, and traces render the full execution tree including tool selections, retrieved documents, and model parameters. Annotation queues route specific traces to domain experts for labelling, and that output feeds evaluation datasets.

Where it falls short: the value is concentrated inside one ecosystem. LangSmith works with any framework through a traceable wrapper, but trace depth outside LangChain and LangGraph is noticeably thinner, which makes it a bet on a framework as much as a platform. Built-in evaluation metrics are limited, so LLM-as-judge scoring is mostly custom work. Self-hosting is Enterprise only. Seat pricing at $39 per month discourages exactly the cross-functional access that makes annotation queues valuable, and base trace retention is short enough that reviewing an incident weeks later may find nothing there.

Pricing: A free developer tier covers evaluation. Paid plans start around $39 per seat per month, with Enterprise custom. Trace volume meters separately from seats.

Best fit: teams whose stack is LangChain today and will still be LangChain in two years. See LangSmith alternatives.

4. Arize

Type: ML and LLM observability platform · Price: Phoenix free, AX from $50/mo · Self-host: Yes (Phoenix)

Arize extended a classical ML observability product into LLM workloads. Phoenix, the open-source library, runs in a Jupyter notebook, locally, or via Docker with no external dependencies, which makes it viable during development rather than only after deploy. Instrumentation uses OpenInference, built on OpenTelemetry, covering LlamaIndex, LangChain, Haystack, DSPy, and smolagents.

Where it falls short: the LLM evaluation layer is the shallow part of an otherwise deep product, because the platform was built for model monitoring first and extended second. Custom evaluators are supported, built-in research-backed metrics for faithfulness and hallucination are not. Prompt versioning is limited. The UX assumes an engineer, which rules out the domain experts who usually know whether an output was actually wrong. Retention on lower commercial tiers is short, and cost tracking leans on token counts rather than dollar amounts.

Pricing: Phoenix is free and self-hosted. Managed AX has a free tier with one user and a capped trace allowance, paid plans from around $50 per month, and custom Enterprise.

Best fit: organizations running both classical ML and LLM workloads that would rather not add a second vendor.

5. Braintrust

Type: Evaluation-first platform · Price: Free tier, $249/month for Pro · Self-host: Enterprise only

Braintrust treats evaluation as the product. Datasets, scoring functions, comparison reports, and regression testing are all first-class, and experiment comparison across prompt versions is more rigorous than anything else on this list.

Where it falls short: tracing exists to serve the eval workflow, so it is less developed than in tools built for production debugging. There is no gateway, which means routing and cost control are a separate purchase and a separate system to correlate against. Self-hosting is Enterprise only. Enterprise pricing is not disclosed, and the tier escalation tends to arrive earlier than teams plan for, because eval volume grows with the test suite rather than with traffic. Buying Braintrust to debug a 2am incident is buying the wrong tool.

Pricing: A free developer tier covers early evaluation. Pro is $249/month.

Best fit: teams where eval discipline is the constraint. See Braintrust alternatives or the Respan vs Braintrust comparison.

6. Helicone

Type: LLM gateway with observability · Price: Free tier, Pro $79/mo, Team $799/mo · Self-host: Yes

Helicone instruments by proxy. Change the base URL and tracing, caching, and cost analytics arrive without touching application code, which for years was the fastest path from zero to observability available.

Where it falls short: Mintlify acquired Helicone on 3 March 2026 and the platform is in maintenance mode, defined by the team as security updates, bug fixes, performance fixes, and new model support. Feature development has ended and the founders have said they will help customers migrate. Anything published this year describing Helicone as actively developed has not been updated since. Separately from the acquisition, proxy instrumentation is request-shaped: it sees calls passing through it, not the reasoning or tool steps between them, so agent chains are largely invisible. The proxy also sits in the critical path, which means its failure mode is your failure mode. Governance features like granular RBAC and audit trails are thin.

Pricing: Hobby is free with 10,000 requests per month and one seat. Pro is $79 per month with unlimited seats, alerts, and reports. Team is $799 per month and adds SOC 2 and HIPAA. Enterprise is custom with on-prem deployment.

Best fit: existing deployments, with a migration plan. Respan maintains a detailed breakdown of Helicone alternatives.

7. Kong AI Gateway

Type: API gateway with AI traffic support · Price: $500/month per control plane · Self-host: Yes

Kong extended its API gateway to AI traffic, and the governance surface is broad: PII sanitization, semantic prompt guards, semantic caching, per-user and per-model token quotas, and chargeback across internal teams. Version 3.14 added Agent Gateway for agent-to-agent traffic alongside LLM and MCP.

Where it falls short: Kong emits telemetry, it does not analyse it. OpenTelemetry span attributes and Prometheus metrics go to Konnect dashboards or an existing stack, which means Kong is a data source in an observability setup rather than the observability platform itself, and you still need somewhere for the data to land. There is no output-quality evaluation of any kind. Visibility is request-shaped, so an agent run appears as a sequence of unrelated calls. At $500 per month per control plane, plus the operational overhead of running a full API gateway, it is an expensive way to acquire AI telemetry if traffic governance is not already a requirement.

Pricing: $500/month per control plane.

Best fit: platform teams governing AI traffic across many internal consumers, paired with something that handles trace and eval depth.

8. Galileo

Type: Enterprise evaluation and monitoring platform · Price: Free tier, $150/month for Pro · Self-host: No

Galileo targets enterprises where quality and compliance requirements dominate: hallucination detection, eval automation at scale, and audit workflows.

Where it falls short: the product is built around governance and audit workflows, so the developer experience is thinner than platforms designed for engineers debugging production. No self-hosting, which rules it out for teams with data residency requirements. Ownership is also in question: reporting suggests Cisco acquired Galileo in 2026 and folded it into Splunk Observability. This could not be confirmed from Cisco, Splunk, or Galileo directly, so treat it as unverified. If accurate, an independent platform has become a component of a much larger suite with its own roadmap and its own pricing conversation.

Pricing: Free tier, $150/month for Pro.

Best fit: teams whose primary need is hallucination detection and audit-ready quality reporting, subject to confirming current ownership.

9. Fiddler

Type: Model monitoring and explainability platform · Price: $0.002 per trace · Self-host: Enterprise

Fiddler monitors drift and quality and adds explainability on top: local and global explanations for model behavior, embedding clustering to surface patterns in outputs, and runtime guardrails for safety policy. In regulated settings where "the model decided" is not an acceptable answer, that explainability layer is the reason to look at it.

Where it falls short: the explainability heritage is classical ML, and the LLM-specific tooling is correspondingly less mature than platforms built for LLM workloads first. Self-hosting is Enterprise. The feature set is aimed at governance and risk teams, which means the developer workflow around tracing and debugging is not where the product invests. Per-trace pricing also behaves badly for agent workloads, since a single agent run can generate dozens of traces and the bill scales with architectural complexity rather than user volume.

Pricing: $0.002 per trace.

Best fit: financial services, healthcare, and other settings where model decisions need defending to an auditor.

10. Datadog LLM Observability

Type: APM platform with an LLM module · Price: $160/month· Self-host: No

Datadog LLM Observability correlates LLM spans with the rest of your APM traces. When an agent slows down because a downstream database started timing out, both halves appear in one view without any additional wiring. Agentless deployment via environment variables works in serverless environments.

Where it falls short: this is a module on a platform built for infrastructure, and it inherits that platform's assumptions. There are no evaluation metrics for faithfulness, relevance, or safety, so output quality is not measured at all. Alerts fire on latency and errors, which are the failure modes LLM systems produce least often. Cost scales with trace volume, and every other Datadog product bills separately on top, so the $160 is a floor rather than a total. It is built for SREs rather than for whoever owns whether the answers are correct.

Pricing: $160/month.

Best fit: teams already standardized on Datadog whose LLM workloads are simple, or as a secondary destination fed by OTel from a dedicated tool.

11. New Relic AI Monitoring

Type: APM platform with AI monitoring · Price: Upon request · Self-host: No

New Relic added AI telemetry to its APM platform: latency, throughput, token usage, and cost per model call, inside dashboards and alerting workflows an existing team already knows. For organizations already paying for New Relic, the procurement cost is zero.

Where it falls short: the limitations are Datadog's, with less depth. No output-quality evaluation, no annotation workflow, no dataset curation, no agent-specific debugging. Pricing for AI Monitoring is not published, so budgeting requires a sales conversation, and LLM traces carry prompt and completion payloads that are large compared with typical APM spans, which makes ingest costs harder to forecast than the rest of the platform. This is operational telemetry for AI workloads rather than an AI observability platform, and it is worth being clear about that before it becomes the reason nobody catches a quality regression.

Pricing: Not published for AI Monitoring. The platform free tier covers 100GB of monthly data ingest and one full user, and beyond that New Relic quotes on request.

Best fit: existing New Relic customers who want basic AI telemetry without adding a vendor.

12. Dynatrace

Type: APM platform with AI observability · Price: From $7/mo per host · Self-host: No

Dynatrace applies its Davis causation engine to AI workloads, correlating anomalies across metrics, logs, and traces to identify root cause with little manual configuration. OneAgent covers hosts and containers, OpenTelemetry ingest handles custom AI telemetry, and Dynatrace documents integration with AI gateways including Kong.

Where it falls short: Davis reasons about infrastructure causation, and infrastructure causation is not why LLM outputs go wrong. Nothing in the platform evaluates whether a completion was faithful to its retrieved context, and no amount of automated root cause analysis will surface a hallucination. There is no self-hosting. Per-host hourly billing is predictable for stable estates and less so for workloads that scale with traffic. Adopting Dynatrace for AI observability alone would be an expensive way to get token counts.

Pricing: Billed hourly against consumed resources and quoted monthly per host: around $7 for Basic, $29 for Infrastructure Monitoring, and $58 for Full-Stack Monitoring. Synthetics and real-user monitoring bill separately. Volume discounts apply at scale.

Best fit: large estates already running Dynatrace where AI workloads are one component of a much bigger system.

How to choose an AI observability tool

Most teams compare features first and discover the constraints later, which wastes an evaluation cycle. Reverse it. Constraints eliminate whole categories in a single pass, and what survives is usually a shortlist of two or three rather than twelve.

• Start with what disqualifies, not what appeals. Data residency, required certifications, and deployment model rule out options before any feature enters the conversation. If traces cannot leave your infrastructure, roughly half the market is gone immediately, and the remaining decision is a different one.

• Name the failure you are trying to catch. Slow responses and error spikes are an infrastructure problem, and whatever already monitors your services probably handles them. Wrong answers that returned successfully are a different problem, and catching those requires scoring output on live traffic rather than watching status codes.

• Check where the instrumentation sits before trusting the feature list. A proxy cannot report reasoning it never receives, and a service agent cannot evaluate a completion it treats as an opaque payload. Match the position to the depth you need, then compare products within that group.

• Decide whether routing belongs in scope. Running one provider makes this irrelevant. Running several means the record of which model served a request and the record of how it performed will either live together or be correlated by hand during every incident.

• Weight portability higher than it feels worth today. Most teams choose before they know what their production traffic looks like, which means being wrong is likely and the cost of being wrong is the real variable. OpenTelemetry instrumentation makes a change of mind cheap. A proprietary SDK makes it a project.

• Work out who needs to read the output. If quality decisions require an engineer to write a script, engineering becomes the queue for every question about whether the product is working. Tools vary enormously here and it rarely appears on a comparison table.

• Model cost at the volume you expect, not the volume you have. Per-seat, per-trace, per-GB, and per-request pricing diverge sharply at scale, and the cheapest option at pilot volume is frequently the most expensive one a year later.

Whichever way that lands, run a pilot on real production traffic before committing. Instrumentation effort, trace fidelity, and cost at volume are all difficult to judge from a pricing page, and every platform here looks equally capable in a demo. Most teams end up on a platform built for LLM workloads specifically, and for anyone already there, the vendor-by-vendor breakdown of LLM observability tools goes deeper on each platform than this comparison does.

FAQ

What are AI observability tools?

Platforms that capture, monitor, and evaluate the behavior of AI systems in production. They differ from application monitoring by recording agent-shaped telemetry, full traces of multi-step runs including tool calls and retrieved context, and by scoring output quality rather than only operational metrics.

How is AI observability different from APM?

APM monitors infrastructure: latency, error rates, throughput, resource use. AI observability monitors what the system produced. A model can return a 200 response in 50ms and still hallucinate. Evaluating that requires scoring response content, which APM was never built to do.

What is the difference between AI observability and LLM observability?

In practice the terms are used interchangeably, though AI observability is the broader label and gets applied to model monitoring and data pipeline monitoring too. For teams shipping LLM applications, the relevant tools are the ones covered here rather than the data-pipeline platforms that also use the phrase.

Which AI observability tools are open source?

Langfuse (MIT core), Arize Phoenix (Elastic License 2.0), and Helicone (Apache 2.0) all have open-source components. Kong has open-source elements alongside commercial features. Open source generally means owning the trace data and building the evaluation layer in-house.

What should I monitor first?

Cost per feature, latency at p95, and error rate give a baseline in an afternoon. Add evaluation on production traffic once normal looks recognizable. Teams that start with evaluation before baseline telemetry usually end up scoring traffic they cannot yet interpret.

Does an AI gateway replace an AI observability platform?

Not on its own. A gateway sees requests passing through it, which yields cost, latency, and model usage. It does not see the reasoning steps inside an agent run, and it does not evaluate output quality. Platforms that provide both remove the seam between them.

Build on traces you can act on

An agent completes twelve steps, returns a wrong answer, and every span is green. That is the failure most monitoring cannot express and the reason this category exists.

Respan gives you the full agent run, evaluation on live traffic, cost attribution down to the feature, and monitors that fire on quality rather than status codes, with the gateway that served the request on the same platform as the trace that recorded it.

Start for free or book a demo.