AI applications generate far more than model outputs. Every request includes prompts, retrieval, tool calls, agent steps, latency, token usage, and evaluation signals that all contribute to the final response. When something goes wrong, engineering teams need to understand what happened, why it happened, what it cost, and whether the outcome met quality expectations.

The best AI observability tools bring this telemetry together so teams can troubleshoot production issues, evaluate model behavior, and optimize cost and performance.

This guide compares 15 leading AI observability tools for production teams based on:

• Best-fit use case
• Tracing and instrumentation model
• Evaluation support
• Token cost visibility
• OpenTelemetry compatibility
• Deployment model and pricing considerations

Quick comparison of the best AI observability tools

AI observability tools vary widely by primary use case. Some are built for full-stack production observability, some for LLM evaluation, some for cost tracking, and others for model governance or open-source self-hosted deployment. The table below provides a quick comparison of the best AI tools.

How we evaluated AI observability tools

We evaluated each platform on how well it supports modern AI workloads, including AI agents, RAG applications, and multi-step workflows commonly monitored with agent observability tools.

While many LLM observability tools focus on prompts and model interactions, production teams often need visibility across the entire request lifecycle. A practical AI observability platform should help engineering teams understand four things:

1. What happened?
2. Why did it happen?
3. What did it cost?
4. Was the output good?

The strongest tools connect all four. They don't just collect telemetry. They help engineering teams make decisions when production behavior is messy, non-deterministic, and expensive.

Tracing depth and agent workflow visibility

A strong AI observability tool captures the full request path: the prompt, model call, retrieval step, tool calls, handoffs, downstream service behavior, latency, errors, and final outcome. AI agents introduce new debugging challenges because the system can fail even when the underlying infrastructure is technically healthy.

Evaluation and quality monitoring

Seeing what happened is different from knowing whether the output was good. Evaluating AI observability tools means looking for offline eval datasets, online evaluations on live traffic, regression checks, prompt scoring, and CI-integrated quality workflows.

Token cost attribution

Beyond latency and reliability, engineering teams increasingly care about observability cost management, using request-level telemetry to understand token usage, infrastructure consumption, and the operational cost of AI features. Monthly provider invoices are not enough. Engineering teams need receipts with cost visibility by request, feature, environment, customer, model, and outcome so they can tie spend to value and catch runaway usage early.

OpenTelemetry and stack fit

OpenTelemetry AI observability is becoming an important buying criterion as engineering teams look to collect AI telemetry using the same open standards that power the rest of their observability stack. OTel compatibility matters for teams that do not want AI telemetry trapped in a separate silo. OTel-native instrumentation lets you connect AI behavior to services, infrastructure, and existing distributed traces.

The 15 best AI observability tools in 2026

1. Honeycomb

Honeycomb is a full-stack observability platform that helps engineering teams debug production systems, trace complex requests, and connect AI behavior to application, infrastructure, user, and cost context.

Its high-cardinality query engine lets teams investigate by customer, session, feature, prompt version, tool, model, and outcome without pre-defining every dimension. That makes Honeycomb a strong fit for production teams that need request-level visibility across AI workflows and the systems those workflows depend on.

Best for: Teams that need an AI agent observability tool connected to full-stack production debugging.

Key strengths:

• Request-level traces across prompts, models, users, tools, retrieval paths, and agent steps.
• High-cardinality querying for fast, open-ended investigation of non-deterministic workflows.
• Cost and performance visibility are tied directly to production behavior and outcomes.

Considerations: Teams that only want a narrow, eval-only LLM workflow may use a smaller subset of the platform.

Pricing: Free tier up to approximately 20M events; Pro from $130/mo for 100M events; Enterprise custom. Unlimited seats.

2. Datadog

Datadog is an enterprise observability platform that brings LLM monitoring into its broader suite for infrastructure, APM, logs, security, dashboards, and alerting.

Datadog is the strongest fit for teams already using it for infrastructure, APM, logs, and security that want LLM observability inside the same ecosystem. Its LLM Observability product monitors model calls alongside application, infrastructure, and security telemetry, with the enterprise-friendly breadth and dashboarding Datadog is known for.

Best for: Teams already standardized on Datadog that want LLM monitoring in one ecosystem.

Key strengths:

• LLM monitoring alongside application, infrastructure, and security telemetry.
• Mature dashboards, alerting, and enterprise-grade breadth.
• Fits teams that want AI observability inside an existing operating model.

Considerations: Datadog may be more platform than smaller AI-native teams need. Review billing settings carefully, since LLM observability may be billed separately.

Pricing: Usage-based. APM is approximately $31–$40/host/mo plus per-span charges; LLM Observability is billed separately. Enterprise custom.

3. New Relic

New Relic is a unified observability platform that helps teams monitor application performance, infrastructure, and AI workloads through OpenTelemetry-aligned telemetry and ingest-based workflows.

New Relic suits teams that want AI monitoring inside a broader APM and observability platform. It positions AI monitoring alongside application performance data under a unified observability model, with OpenTelemetry alignment that makes it a good fit for teams modernizing observability while adding LLM or AI features.

Best for: Teams adding AI monitoring to a broad, unified APM platform.

Key strengths:

• Unified observability across application, infrastructure, and AI data.
• OpenTelemetry alignment and ingest-based pricing.
• Good fit for teams modernizing observability while adding AI features.

Considerations: May not be as specialized as eval-first AI-native tools for prompt scoring, dataset workflows, or AI quality iteration.

Pricing: The free tier includes 100GB/mo ingest and one full user; usage beyond that is approximately $0.40/GB, or $0.60/GB for Data Plus; users are from $49/mo; Enterprise custom.

4. Arize AI/Phoenix

Arize AI is an AI observability and evaluation platform, with Phoenix as its open-source option for tracing, evaluating, and monitoring LLM, RAG, and agent applications.

Phoenix covers tracing and evaluation workflows for LLM and agent systems, while the Arize AX cloud product adds a fuller evaluation suite and enterprise controls.

Best for: ML and LLM teams that want tracing plus evaluation, with an open-source option.

Key strengths:

• AI-native tracing and evaluation for LLM and agent systems.
• Open-source, self-hostable Phoenix with no usage caps.
• Fit for ML platform teams, RAG systems, and AI quality monitoring.

Considerations: Teams may still pair Arize/Phoenix with a broader production observability platform for deeper infrastructure and service-level debugging.

Pricing: Phoenix open-source free to self-host; AX Free tier; AX Pro approximately $50/mo; Enterprise custom.

5. Langfuse

Langfuse is an open-source LLM observability platform for tracing, prompt management, evaluations, token usage tracking, and self-hosted or cloud-based AI application monitoring.

Its core is MIT-licensed and free to self-host, which makes it a strong fit for teams with strict data-control or data-residency needs that still want a modern LLM observability workflow.

Best for: Open-source-first teams that want LLM tracing with a self-hosting option.

Key strengths:

• MIT-licensed core, free to self-host with no usage caps.
• LLM tracing, prompt/version visibility, and evaluation workflows.
• Cost and token tracking with unlimited users on paid cloud tiers.

Considerations: Self-hosting may require more ownership from the engineering team than a fully managed enterprise platform.

Pricing: Self-host core free; Cloud Hobby free; Core from $29/mo; Pro from $199/mo; Enterprise from around $2,499/mo.

6. LangSmith

LangSmith is LangChain’s observability and evaluation platform for tracing, debugging, testing, and monitoring LLM applications built with LangChain and LangGraph. It gives developers visibility into chain and agent execution, making it easier to inspect intermediate steps, troubleshoot failures, compare prompt versions, and evaluate outputs across development and production workflows.

Because LangSmith is designed around the LangChain ecosystem, it aligns well with teams already using LangChain patterns, LangGraph agent workflows, and related developer tooling. That makes it especially useful for chain-level debugging and agent graph visibility when the application architecture already follows LangChain conventions.

Best for: LangChain- and LangGraph-heavy teams that want first-party tracing, debugging, prompt management, and evaluation workflows.

Key strengths:

• Strong fit for LangChain-native development teams.
• Useful agent graph visibility and chain-level debugging.
• Developer workflow alignment for teams already in the LangChain ecosystem.

Considerations: Less neutral for teams that are not building primarily on LangChain or that want vendor-agnostic observability across broader production systems.

7. Braintrust

Braintrust is an evaluation-first platform for testing prompts, managing datasets, running experiments, and improving AI application quality through repeatable eval workflows.

It centers an evaluation-first workflow with strong testing and iteration support, helping product and engineering teams improve prompt quality before and after release and connect production observations back to repeatable eval workflows.

Best for: Teams that lead with evaluation and CI-integrated quality workflows.

Key strengths:

• Evaluation-first workflow with scoring and experiments.
• Strong testing and iteration loop for prompt quality.
• Connects production observations to repeatable eval datasets.

Considerations: May be paired with deeper production tracing or infrastructure observability when teams need to debug downstream system behavior.

Pricing: Starter free with 1GB data, 10K scores, and unlimited users; Pro $249/mo plus usage overages; Enterprise custom.

8. Galileo

Galileo is an AI observability and evaluation platform focused on monitoring, testing, and improving GenAI applications through traces, evals, guardrails, and production feedback loops.

It is a strong fit for teams that want to build evaluation datasets, tune metrics from live feedback, monitor AI systems in production, and turn offline evals into guardrails for RAG, agents, safety, and security.

Best for: Teams that want eval engineering, production monitoring, and guardrails for GenAI apps and agents.

Key strengths:

• Strong eval workflows for RAG, agents, safety, security, and custom evaluators.
• Production observability that connects traces, prompts, functions, context, datasets, and failure modes.
• Guardrail-oriented workflow that turns evals into scalable production checks.

Considerations: Galileo is more evaluation- and reliability-focused than full-stack application observability; teams may still need a production tracing platform for service and infrastructure dependencies.

Pricing: Free plan includes 5,000 traces/mo and unlimited users/custom evals. Pro starts at $100/mo billed yearly with 50,000 traces/mo. Enterprise is custom.

9. Helicone

Helicone is a gateway-based LLM observability tool that gives teams fast visibility into model requests, latency, usage, caching, and token costs.

Its gateway/proxy deployment gives you usage, latency, and cost analytics with low setup overhead, making it useful for teams that need quick visibility into provider usage and request patterns.

Best for: Teams that want fast, proxy-based LLM cost and usage visibility.

Key strengths:

• Gateway/proxy deployment with one-line integration.
• Request-level usage, latency, and cost analytics.
• Built-in caching at the proxy layer to reduce repeated-query cost.

Considerations: May not replace deeper application/service tracing or evaluation workflows for complex production agents; confirm the current roadmap.

Pricing: Free with 10K requests/mo; Pro $79/mo; Team $799/mo; Enterprise custom.

10. Grafana Labs

Grafana Labs provides an open-source observability stack for visualizing metrics, logs, and traces across systems using tools like Grafana, Prometheus, Loki, Tempo, and OpenTelemetry.

It fits open-source and platform teams with an LGTM stack. It is a strong choice when you want to keep AI telemetry inside an existing observability stack, visualizing traces, logs, and metrics together rather than adopting a separate AI-only tool.

Best for: Open-source and platform teams already invested in the Grafana stack.

Key strengths:

• Strong fit for existing Grafana, Loki, Tempo, and Prometheus users.
• Open-source observability ecosystem with OTel support.
• Unified trace, log, and metric visualization.

Considerations: AI-specific workflows may require more configuration or additional tooling compared with purpose-built AI observability platforms.

Pricing: Self-managed OSS free; Grafana Cloud Free tier; Pro $19/mo base plus users/usage; Advanced/Enterprise custom.

11. Dynatrace

Dynatrace is an enterprise-grade observability platform for teams that want AI-assisted root cause analysis and automated observability across large environments. It supports complex application and infrastructure monitoring, with the Davis AI engine providing automated dependency insights and anomaly detection where verified.

Best for: Enterprise teams that want AI-assisted root cause analysis at scale.

Key strengths:

• Automated, AI-assisted observability with the Davis AI engine.
• Deep application and infrastructure dependency mapping.
• Built for complex, large-scale enterprise environments.

Considerations: Consumption-based pricing and platform depth can be more than smaller AI-native teams need for lightweight tracing or evals.

Pricing: Consumption-based. Full-stack approximately $0.08/host-hour; Davis AI add-on; volume discounts and committed pools vary.

12. Splunk Observability Cloud

Splunk Observability Cloud is an enterprise observability platform that connects metrics, traces, logs, incidents, and operational analytics across complex production systems.

It’s best for teams that want observability connected to Splunk’s broader analytics, log, security, and incident workflows. It fits organizations already invested in Splunk that want to connect observability with operational and security data, with OpenTelemetry-native ingestion across the platform.

Best for: Organizations already invested in Splunk's analytics and security ecosystem.

Key strengths:

• OpenTelemetry-native ingestion across metrics, traces, and logs.
• Connects observability to Splunk security and incident workflows.
• Strong buyer relevance for Splunk-standardized enterprises comparing AI observability options.

Considerations: Best value comes when you are already standardized on Splunk; otherwise, the platform breadth may exceed AI-only needs.

Pricing: Per-host annual pricing: Infrastructure approximately $15/host/mo, App & Infra $60/host/mo, End-to-End $75/host/mo.

13. Elastic Observability

Elastic Observability is a search-centered observability platform that helps teams analyze logs, metrics, traces, and application performance data within the Elastic ecosystem.

Elastic Observability is best for teams already using Elastic for logs, search, and analytics who want observability and AI-assisted investigation in the same stack. Its strength is log/search depth paired with integrated observability workflows, so teams can pivot from a search query to a trace without leaving the platform.

Best for: Teams already using Elastic for logs, search, and analytics.

Key strengths:

• Strong log and search foundation with integrated observability.
• OpenTelemetry support and AI-assisted investigation; self-managed option available.
• Good fit for log/search-centric teams that want AI telemetry inside the Elastic ecosystem.

Considerations: Consumption is billed on ingested/retained volume, so high-cardinality AI telemetry should be budgeted carefully.

Pricing: Serverless usage-based: logs ingest from approximately $0.105/GB; retention from approximately $0.018/GB/mo. Self-managed option available.

14. IBM Instana

IBM Instana is an enterprise observability platform for automatically discovering, tracing, and monitoring applications, infrastructure, services, and AI-related workflows.

IBM Instana is strongest for enterprises that want AI workflow visibility in the same place they manage application performance, infrastructure dependencies, and incident response. Instana emphasizes automatic discovery of AI components, end-to-end traces across agents and services, and correlation of performance, cost, and quality signals in one platform.

Best for: Enterprises that want AI agent and LLM observability connected to traditional application observability.

Key strengths:

• Traces AI requests across agents, LLM calls, tools, and traditional services.
• Monitors latency, throughput, token usage, modeled cost, and AI-specific failure patterns.
• Includes built-in evaluations, adaptive baselining, and task-level visibility into agent reasoning.

Considerations: Instana is a better fit for enterprise APM and IT operations teams than lightweight AI-native teams looking only for prompt experimentation or eval workflows.

Pricing: Per managed virtual server/host model, with Essentials and Standard plans and a minimum order quantity for Standard licenses. Pricing varies by plan, deployment, region, discounts, and data ingestion.

15. Coralogix

Coralogix is a full-stack observability platform for streaming logs, metrics, traces, and security data with cost controls for managing telemetry volume at scale. It’s best for teams that want streaming analytics, logs, metrics, traces, and AI-assisted investigation in one platform with cost-conscious data management.

Its TCO Optimizer lets teams route telemetry by business value, keeping high-priority data hot and lower-value data in cheaper tiers, useful when AI telemetry volume would otherwise drive costs up fast.

Best for: Cost-conscious teams that want observability with flexible data-tiering.

Key strengths:

• Streaming analytics across logs, metrics, and traces with OTel support.
• TCO Optimizer routes data by value to control cost at scale.
• Useful for teams managing growing AI telemetry volumes.

Considerations: Getting the most from the cost model requires deliberate pipeline configuration up front.

Pricing: Usage-based; 1 unit = $1.50 of data, with per-GB rates varying by pipeline. Contact sales for enterprise.

What features matter most in an AI observability platform?

The right AI observability platform should help your team understand what happened, why it happened, what it cost, and whether the output was successful. Use this as a practical buyer checklist when comparing tools:

• Prompt and completion tracing: visibility into what the model received, what it returned, and how the response changed across versions or environments.
• Agent step and tool-call visibility: see agent decisions, handoffs, loops, failed actions, and repeated tool calls across a full workflow.
• Retrieval and context tracking: for RAG and agentic systems, visibility into retrieved documents, context quality, and missing or irrelevant context.
• Token usage and cost attribution: connect AI spend to each request, feature, environment, customer, and outcome, not just a monthly invoice.
• Evaluation workflows: a way to measure whether outputs are accurate, safe, useful, or aligned with expected behavior.
• Guardrail and policy monitoring: track blocked prompts, risky outputs, policy violations, and escalation patterns.
• OpenTelemetry compatibility: AI telemetry should connect to the broader production stack instead of living in a separate debugging silo.
• High-cardinality querying: debug by user, session, feature, prompt version, model, tool, or outcome without pre-defining every dimension.
• Alerting and anomaly detection: alerts for cost spikes, latency regressions, quality drops, repeated retries, and runaway usage.

The strongest platforms help teams connect AI behavior to production reliability, cost accountability, and real user impact.

How Honeycomb fits into a modern AI observability stack

A modern AI observability stack typically includes multiple layers rather than a single platform. Teams often use AI-native tools for prompt development, evaluations, or model monitoring, alongside a production observability platform that provides visibility into the applications and infrastructure powering those AI experiences.

Honeycomb fills that production observability layer. It brings together request-level context across prompts, models, tool calls, retrieval paths, users, services, and downstream dependencies so engineers can investigate AI requests alongside the systems that support them.

This becomes especially valuable when AI issues extend beyond the model itself. Whether the root cause is a failed retrieval, an overloaded service, or an unexpected increase in latency or cost, Honeycomb helps teams understand how AI behavior and production systems interact within a single request.

Traces over evals for production issues

Evals are extremely useful tools for reviewing agentic behavior, and they are used effectively in development or in samples of production conversations. While it’s theoretically possible to run an evaluation against every single agent LLM call, the cost of doing so could significantly inflate your existing AI spend.

AI is nondeterministic by design, so traditional tests and evals won’t be able to predict every outcome of software in production. You might observe model drift or hallucinations, or see errors in the final outputs that are not related to the model at all, but rather a downstream service the LLM never sees. This is why evals on their own can’t give you the full story on how your agentic software is behaving in production.

Instrumenting your agents with OpenTelemetry, and following the semantic conventions for Generative AI will provide access to the traces and errors, in production, for every agent conversation. Using OpenTelemetry keeps your telemetry standardized with no vendor lock-in.

Sending agentic telemetry also exposes the entire production conversation flow to Agent Timeline, Canvas, BubbleUp, SLOs, and other key Honeycomb features and services.

Build your AI observability stack with Honeycomb

No single tool solves every AI observability challenge. Many engineering teams combine AI-native tools for prompt tracing, evaluations, or model monitoring with a production observability platform that provides visibility across the entire request lifecycle.

Honeycomb provides the production layer that connects AI behavior to services, users, costs, and outcomes.

If you’re looking for a platform that connects AI behavior to production systems, explore Honeycomb Intelligence, an AI-powered observability workspace that helps engineering teams investigate AI-powered applications with request-level context across models, tools, services, and infrastructure