11 Best AI Observability Tools in 2027

The best AI observability tools show you the decisions your production AI agents make as they answer support tickets, query databases, and open pull requests. Production AI agent monitoring traces every step an agent takes, from the first model call to the last tool call. AI observability tools are harder to compare than application performance monitoring (APM) vendors, because each one was built for a different job. Some trace multi-step reasoning or score large language model (LLM) hallucinations, while others attribute token spend to a session or stop a prompt injection mid-request.

This guide covers the capabilities that separate purpose-built AI observability from repurposed APM, includes a side-by-side comparison of 11 tools, and profiles each one.

What to Look for in an AI Observability Tool

The right AI observability tool tells you whether an LLM answer was correct, safe, and worth its cost. Traditional monitoring stops at confirming that the endpoint responded. Among teams running agents in production, 89 percent have observability in place, yet only 52 percent have evals.

The capabilities below separate purpose-built tools from repurposed APM:

• Full agent traces: Plan steps, tool calls, retrievals, and handoffs should link into one trace, which is the core of agentic AI observability.
• Output evaluation: Evaluation metrics should cover LLM hallucinations, relevance, toxicity, personally identifiable information (PII), and custom LLM-as-judge rules.
• Token cost tracking: Cost tracking should break down spend by session, user, and agent, so looping agents stand out.
• Real-time guardrails: Inline prompt injection detection should block or rewrite an attack before the response reaches the user.
• OpenTelemetry generative AI conventions: OpenTelemetry (OTel) still marks the generative AI (GenAI) conventions, gen_ai.*, as Development and has moved them to a dedicated GitHub repository, so ask which version each vendor ingests.
• Privacy controls and data residency: Look for PII masking and control over where traces are stored.
• Self-hosted versus managed service: With software as a service (SaaS), check whether the vendor hosts your data or stores it in your cloud bucket. For self-hosting, MIT, Apache 2.0, Elastic License 2.0 (ELv2), and Enterprise-only terms carry different obligations.
• Retention for baselines: A short retention window limits how far back evaluators can compare, which weakens model drift detection.

Each tool below covers a different subset of these capabilities, so the capability list above works as your first filter before pricing.

The 11 Best AI Observability Tools

The table below compares 11 AI observability tools on best fit, deployment, agent tracing, evaluation, pricing model, and license, using vendor-page data checked in November 2026. Commercial terms change by package, contract, region, and usage, so confirm current pricing during procurement. In the table, "Enterprise" marks each vendor's top paid tier.

Figures in the profiles come from vendor docs, pricing pages, and changelogs, and the profiles label vendor-reported benchmarks as such.

1. Coralogix AI Center

Coralogix AI Center is Coralogix's AI observability layer, a cross-stack observability platform built for platform and site reliability engineering (SRE) teams running LLM workloads alongside infrastructure. AI Center runs on the same platform as your logs, metrics, traces, and security data, and it ingests OpenTelemetry GenAI spans.

The open-source LLM Tracekit instruments LangGraph, LangChain, and the OpenAI Agents SDK, and Session Explorer traces each user journey with flagged messages in context. Olly, Coralogix's autonomous observability agent, runs root-cause analysis across logs, metrics, and traces when an AI incident traces back to infrastructure.

Pros

• The AI Center evaluation engine runs prebuilt evaluators for hallucination, PII exposure, off-topic responses, and toxicity, plus custom evaluators for your own quality and security rules.
• AI Guardrails block or rewrite unsafe prompts and responses in real time, and AI security posture management (AI-SPM) scores the posture of the AI workloads that AI Discovery maps across your repos.
• Cost tracking attributes spend from token usage up to whole AI workloads and sets spending limits per agent. Code agent observability tracks token usage and cost for Claude Code, the Codex CLI, and Cursor.
• Streama© in-stream processing lands AI traffic in your own S3 bucket in open Parquet with unlimited retention, which gives evaluators a long baseline for trend analysis.

Cons

• Processed data lands in your own object storage bucket, so your team provisions and manages that bucket, a modest setup step that fully hosted platforms don't require.
• Pricing follows the volume and type of data ingested, so teams moving from per-host or per-user models need a data-volume forecast rather than a seat count.
• Engineers with years of muscle memory in another vendor's query language face a retraining cost with DataPrime, even though Coralogix also supports Lucene and Prometheus Query Language (PromQL).

Pricing

Coralogix pricing is based on ingestion, with logs at $0.42/GB, traces at $0.16/GB, and no per-host, per-user, or per-query charges. Every plan includes full feature access and unlimited users and hosts, so AI Center, evaluators, and guardrails don't sit behind a higher tier.

AI evaluation workloads bill separately in tokens at $1.50 per million tokens, measured monthly and token use grows with the number of evaluators applied. A 14-day free trial includes an eight-unit quota with no credit card required.

Who is Coralogix AI Center best for?

Platform and SRE teams that want agent sessions, evaluations, and guardrails in the same place as their logs, metrics, traces, and security data, rather than in a separate AI tool.

2. Braintrust

Braintrust is an evaluation platform for teams that want production traces and evaluation in one workflow. Its online scoring runs the same scorers on live logs as it does in offline experiments. Traces capture tool calls and separate LLM errors from tool errors.

Pros

• Sampling rates on online scoring keep LLM-as-judge cost under control.
• October 2026 changelog entries added Model Context Protocol (MCP) server and Cursor session tracing.
• Traces record cached and reasoning tokens alongside estimated cost.

Cons

• On-premises or hosted deployment requires the Enterprise plan.
• Retention runs for 14 days on Starter and 30 days on Pro.
• Starter and Pro set monthly allowances for processed data and scores.

Pricing

The Starter plan costs $0 per month and includes 1 GB of processed data, 10,000 scores, 14-day retention, and $10 in monthly model credits. Pro runs $249 per month with five GB, 50,000 scores, and 30-day retention. On-premises or hosted deployment sits in the Enterprise plan.

Who is Braintrust best for?

Teams that gate deploys on eval scores and are comfortable running on SaaS.

3. Arize Phoenix

Arize Phoenix is a free, self-hosted tracing and evaluation tool for teams whose traces can't leave their network, with fully air-gapped operation. Phoenix builds on OpenTelemetry through OpenInference instrumentation and converts incoming gen_ai.* spans to OpenInference automatically. Arize also lists a managed cloud option.

Pros

• Pre-built evaluators cover Faithfulness, Hallucination, Correctness, and Retrieval Relevance for retrieval-augmented generation (RAG).
• The retrieval view surfaces clusters of poorly rated queries, plus a query-density drift signal that points to missing knowledge-base coverage.
• The self-hosted version has no feature gates.

Cons

• Phoenix's server ships under ELv2, a source-available license that bars offering Phoenix as a hosted service.
• Running Phoenix in production means operating Docker or the Helm chart with PostgreSQL yourself.
• Managed monitoring, alerting, and the embedding-drift guides are part of the commercial Arize AX product.

Pricing

Phoenix is open source and free to self-host. Arize AX, the commercial product, has a free SaaS tier with 25,000 trace spans per month and 15-day retention, and AX Pro costs $50 per month with 50,000 spans, 10 GB of ingestion, and 30-day retention. AX Enterprise pricing is custom and adds a self-hosted option.

Who is Arize Phoenix best for?

Teams that need air-gapped tracing and built-in RAG evaluators and can take on ELv2 terms and the operations work of self-hosting.

4. Langfuse

Langfuse is an open-source LLM engineering platform for teams that want to self-host, and it runs the same codebase on Langfuse Cloud and on your own Kubernetes cluster. ClickHouse acquired Langfuse in January 2026, and both companies stated that the MIT license and self-hosting path would stay unchanged. Evaluation covers LLM-as-judge scoring online and offline.

Pros

• The MIT-licensed core includes observability, prompt management, evaluation, and datasets with no usage limits.
• One codebase serves both Langfuse Cloud and self-hosted deployments.
• Langfuse Cloud offers a free Hobby tier.

Cons

• Project-level role-based access control (RBAC), protected prompt labels, data retention policies, audit logs, and server-side data masking require a license key.
• The MIT license excludes the ee folders.
• Self-hosting puts the Kubernetes deployment and its upgrades on your team.

Pricing

The MIT-licensed core is free to self-host. Langfuse Cloud's free Hobby tier includes 50,000 billing units per month, 30 days of data access, and two users. Core costs $29 per month with 100,000 units and 90 days of data access. Pro costs $199 per month with three years of data access, and Enterprise starts at $2,499 per month.

Who is Langfuse best for?

Teams that need an MIT-licensed core they can run themselves, with enterprise controls available through a license key.

5. Galileo AI, Now Splunk Agent Observability

Galileo, now Splunk Agent Observability after Cisco's acquisition, is built for teams that score hallucinations across high-volume production traffic. Its evaluation stack centers on Luna-2, small language models built for low-cost evaluation, including hallucination detection. Deployment runs in the cloud or self-hosted.

Pros

• In Galileo's published benchmarks, Luna-2 scores outputs at a fraction of the latency and cost of an LLM-as-a-judge setup.
• Agentic metrics such as tool selection quality, action completion, and tool errors run on the same Luna-2 evaluators.
• Self-hosted deployment keeps scoring inside your own environment.

Cons

• Spans bill annually, so you commit for a year.
• Paid pricing requires a quote.
• The free edition's limits aren't stated.

Pricing

Pricing starts at $100 per month per 1.2 million spans, billed annually, and includes 500 million Luna tokens and five Luna fine-tuning runs per year. Additional Luna tokens and extended retention are quoted separately.

Who is Galileo best for?

Teams that score every production trace for hallucinations and can commit to annual span billing.

6. Datadog LLM Observability

Datadog LLM Observability, now branded Agent Observability, is built for teams already on Datadog APM. Its Python software development kit (SDK) auto-instruments calls to OpenAI, LangChain, LangGraph, Bedrock, and Anthropic. Spans following GenAI conventions v1.37 or later arrive without the SDK.

Pros

• AI spans sit in the same APM context as the rest of your Datadog data.
• Evaluation supports managed and custom LLM-as-judge scoring.
• Patterns clusters production traffic into hierarchical topics using your connected LLM provider account, with a Clusters view color-coded by evaluation scores.

Cons

• Deployment is SaaS only.
• Spans on GenAI convention versions older than v1.37 need the Datadog SDK.

Pricing

The Free tier costs $0 per month with up to 40,000 LLM spans and 15-day retention. Pro costs $160 per month with up to 100,000 LLM spans, and on-demand usage beyond the included spans is billed separately. Offline and online evaluations carry no separate product fee, though each LLM call in an evaluation counts as spans.

Who is Datadog LLM Observability best for?

Teams already standardized on Datadog that want AI spans beside their existing APM data and can run on SaaS.

7. Pydantic Logfire

Pydantic Logfire is an OpenTelemetry-based tracing platform for teams that want AI calls inside the same trace as the rest of the application, and it has been generally available since October 2024. With OpenTelemetry underneath, one trace can run from the web handler through agent, model, and tool calls to the database. The Agents view picks up any SDK emitting GenAI invoke_agent spans, including Pydantic AI.

Pros

• LLM panels show tokens and cost per model span.
• Logfire's MCP server lets Claude Code, Codex, or another coding agent query production telemetry and manage dashboards and alerts.
• Live evals score production traffic.

Cons

• Coverage is partial for Semantic Kernel and the TypeScript OpenAI Agents SDK.
• Self-hosting requires the Enterprise plan.
• The free Personal plan includes a fixed monthly record allowance.

Pricing

Personal is free with 10 million records per month, and ingestion pauses at that cap instead of billing you. Team costs $49 per month, and Growth costs $249 per month, each with 10 million records included and $2 per additional million. Self-hosting in your own Kubernetes cluster sits in the Enterprise plan, which has custom pricing.

Who is Pydantic Logfire best for?

Teams on Pydantic AI or another SDK that emits GenAI spans, as long as partial coverage for Semantic Kernel and the TypeScript OpenAI Agents SDK isn't a blocker.

8. Helicone

Helicone is a proxy-based logging tool for teams that want request-level tokens, latency, and cost with minimal setup. You swap your provider base URL for an endpoint such as oai.helicone.ai and add a Helicone-Auth header. An async integration mode keeps Helicone off your request path.

Pros

• Setup takes a base URL swap and one header.
• Proxy mode adds edge caching, rate limits, and retries.
• The Apache 2.0 main repository self-hosts with Docker Compose.

Cons

• Edge caching, rate limits, and retries work only in proxy mode, not in async mode.
• Helicone joined Mintlify on March 3, 2026 and entered maintenance mode.
• Helicone's site now states that it's no longer part of Mintlify, so confirm its development status before you commit.

Pricing

Helicone's free Hobby tier includes 10,000 requests and one GB of storage. Pro costs $79 per month and Team $799 per month, both with usage-based pricing.

Who is Helicone best for?

Teams with one LLM app that want logging in minutes and can accept a tool in maintenance mode.

9. LangSmith

LangSmith is an observability and evaluation platform for teams building on LangChain and LangGraph, and it also traces other frameworks and model providers. Agents built outside LangChain can still report through LangSmith's OpenTelemetry Protocol (OTLP) endpoint, without its SDK. Deployment runs in LangChain's cloud, in a hybrid setup with a self-hosted data plane, or fully self-hosted.

Pros

• Online evaluation runs reference-free LLM-as-judge evaluators on production traces, and offline evaluation compares versions against curated datasets.
• LangSmith Engine detects recurring issues across traces.
• Annotation queues collect human feedback on traces, and dashboards and alerts track quality over time.

Cons

• Hybrid and self-hosted deployment require the Enterprise plan.
• Base traces are retained for 14 days, and keeping a trace for 180 days costs an extra fee per extended trace.
• Paid plans bill per seat, so costs grow with team size and trace volume.

Pricing

The Developer plan is free for one seat with 5,000 base traces per month, then pay as you go. Plus costs $39 per seat per month with 10,000 base traces, then pay as you go. Enterprise pricing is custom and adds hybrid and self-hosted deployment.

Who is LangSmith best for?

Teams building on LangChain or LangGraph that want tracing and evaluation in one tool and can budget per seat.

10. Weights & Biases Weave

W&B Weave is a tracing and evaluation toolkit for teams that already track model training in Weights & Biases. Weave traces functions marked with the @weave.op decorator and accepts OpenTelemetry traces through an OTLP endpoint, so agents can report without the Weave SDK. Weights & Biases now sits within CoreWeave, which sells Weave through its CoreWeave Forge plans.

Pros

• LLM judges and custom scorers evaluate responses, with documented pipelines for retrieval-augmented generation.
• The Agents view shows sessions, turns, LLM calls, and tool calls from integrated or OTel-instrumented agents.
• The Weave toolkit is open source under Apache 2.0 and includes Python and TypeScript libraries.

Cons

• The docs describe CoreWeave Agent Lens as the successor to W&B Weave, so confirm the product roadmap before you standardize.
• Weave ingestion beyond the plan allowance is billed by the megabyte, at $0.10 per MB on Pro and $0.20 per MB on Enterprise.
• Private connectivity and customer-managed encryption keys require Enterprise private hosting.

Pricing

The free plan includes one GB of Weave ingestion per month for up to five seats. Pro starts at $60 per month with 1.5 GB of ingestion and up to 10 seats. Enterprise pricing is custom.

Who is W&B Weave best for?

Teams already running experiments in Weights & Biases that want agent traces in the same workspace and can plan around the move to Agent Lens.

11. AgentOps

AgentOps is an agent observability tool for developers who want session replays of multi-agent runs with little setup. Its SDK adds session replays in two lines of code and integrates natively with frameworks including CrewAI, AG2 (AutoGen), the OpenAI Agents SDK, LangGraph, and LlamaIndex. The app is open source under the MIT license, and teams can run its dashboard and API backend themselves.

Pros

• Step-by-step execution graphs replay each agent run for debugging.
• Token and cost tracking monitors token counts and agent spend.
• Enterprise adds self-hosting on Amazon Web Services, Google Cloud, or Azure, plus on-premises deployment.

Cons

• Public pages don't detail built-in output evaluation.
• The free Basic plan stops at 5,000 events.
• Pro is pay-as-you-go from $40 per month, so cost follows event volume.

Pricing

Basic is free up to 5,000 events. Pro starts at $40 per month on pay-as-you-go terms. Enterprise pricing is custom.

Who is AgentOps best for?

Developers on CrewAI, AG2, or the OpenAI Agents SDK who want session replays quickly and don't need built-in output scoring.

How to Choose the Right AI Observability Tool for Your Team

The right tool depends on where your agents run, what APM data you already have, and how deep your evaluation needs go. Each tool's strengths map to the LLM observability pillars of tracing, evaluation, cost, and latency. The scenarios below are starting points:

• Infrastructure and AI in one tool: Coralogix AI Center fits when you need to trace an AI failure back to the database, retrieval index, or host behind it.
• Small team with one LLM app: Langfuse Cloud's free Hobby tier or Logfire's Personal plan covers tracing and cost at no charge. Helicone wires up faster if maintenance mode is acceptable.
• Self-hosted or air-gapped requirement: Langfuse fits for an MIT core with an optional license key. Phoenix fits if ELv2 terms work and you want built-in RAG evaluators.
• Enterprise already on Datadog: Datadog LLM Observability adds AI spans without a new vendor or a new bill.
• Eval-gated releases or high-volume scoring: Braintrust suits teams that gate deploys on eval scores, and Galileo suits teams that need low-cost scoring across production traffic.

Once a scenario narrows the field, the table's pricing column shows which remaining tool fits your budget model.

Standalone AI Observability Tools vs. One Platform for All Telemetry

Choosing among these AI observability tools comes down to whether AI telemetry lives in its own tool or beside the logs, metrics, and traces it depends on. A standalone tool can score outputs well, but when a hallucination traces back to a stale retrieval index or a slow database, your team still has to switch consoles to find the cause. If you need agent sessions, evaluator scores, and guardrail actions in the same place as your production telemetry, Coralogix is built for that.

Book a Coralogix demo to see AI Center score sessions from your own agents next to the rest of your production telemetry. You'll see evaluator scores, guardrail actions, and cost per session for your workloads before you commit.

Frequently Asked Questions About AI Observability Tools

What is the difference between LLM observability and AI agent observability?

LLM observability captures GenAI telemetry for each model call, including input, output, latency, and token cost. AI agent observability links every plan step, tool call, retrieval, and handoff across many turns into one trace. The OpenTelemetry GenAI conventions reflect this split with separate chat, invoke_agent, and execute_tool spans.

Can standard application performance monitoring handle AI workloads?

Only partly, because APM tracks latency, error rates, and throughput, which means it catches a slow or failing model endpoint. Without output evaluation, those performance signals miss the quality measures AI workloads need, such as whether a successful response carried a hallucination, relevance drift, or leaked PII. LLM observability tools add OpenTelemetry GenAI trace schemas and output scoring on top of standard tracing.

Is an open-source AI observability tool always cheaper than SaaS?

No, engineering time can make self-hosting more expensive than SaaS. Your cost comparison needs to include single-node or high-availability infrastructure and ongoing maintenance, not storage alone. Residency requirements can still favor self-hosting, because it keeps traces inside your own Kubernetes clusters.

Do I need both tracing and evaluation for production AI agents?

Yes, tracing records which tools an agent called, in what order, and at what latency and cost. A trace can show six tool calls in four seconds, but only an evaluator tells you whether the answer was accurate, relevant, and free of PII or toxicity.

Which AI observability tool is best for a team already using OpenTelemetry?

The best fit ingests gen_ai.* spans natively, which lets your existing collector route AI telemetry alongside application traces. Phoenix converts them to OpenInference at ingest, Logfire builds on OTel, and Datadog accepts supported GenAI convention versions. LangSmith and W&B Weave accept traces over OTLP endpoints. Coralogix AI Center ingests the spans directly.