Top 10 LLM Observability Platforms of 2026

A green dashboard can hide a broken AI system. The model answered, the API returned 200, and the agent still chose the wrong tool.

That is why production AI needs more than conventional application monitoring. Modern LLM applications can involve model calls, retrieval, prompts, tool calls, agent decisions, evaluations, and multiple providers before a user sees the final response. When something goes wrong, engineering teams need to reconstruct that path, measure its cost, and determine whether the output was actually good.

The platforms below approach that problem differently. Some focus on open-source tracing, others connect observability with evaluations or agent development, while enterprise platforms bring LLM telemetry into a broader monitoring stack. This comparison looks at tracing, evaluations, agent visibility, production monitoring, cost attribution, instrumentation, deployment options, and developer workflows.

What Should an LLM Observability Platform Capture?

LLM observability goes beyond checking whether an API request succeeded. A useful platform should help teams inspect what happened inside an AI application and connect technical telemetry with the quality and cost of the resulting output.

• Traces and spans: Follow model calls and application steps across an AI workflow.
• Agent activity: Inspect tool calls, decisions, handoffs, and multi-step runs.
• Retrieval: See which documents or context were retrieved by RAG applications.
• Evaluations: Measure output quality and detect regressions.
• Cost and tokens: Track usage across models and requests.
• Latency and errors: Identify slow or failing components.
• Prompts and experiments: Connect application changes with changes in performance.
• OpenTelemetry support: Export or ingest standardized telemetry where supported.

For agent-heavy systems, the depth of the trace becomes particularly important. A final response may look normal even when an agent took an inefficient route, called the wrong tool, or consumed far more resources than expected.

How We Compared These LLM Observability Platforms

We compared the platforms using documented capabilities across eight areas: trace and span visibility, agent and workflow visibility, evaluations, production monitoring, cost attribution, instrumentation, deployment and data control, and developer workflow.

This is a capability comparison rather than an independent performance benchmark. Pricing and product capabilities can also change, so buyers should confirm current limits and plan details before making a purchase.

Quick Comparison

1. Langfuse

Langfuse is an open-source LLM observability platform covering tracing, evaluations, prompt management, experiments, latency, token usage, and cost monitoring. Its OpenTelemetry support also makes it useful for teams that want standardized telemetry rather than a completely isolated observability stack.

One of its biggest differentiators is deployment flexibility. Teams can use Langfuse Cloud or self-host the open-source platform when they need greater control over their observability data.

Best for: Engineering teams that want open-source LLM observability and self-hosting options.

Pricing: Free tier; paid Cloud plans start at $29/month.

Limitation: Self-hosting shifts infrastructure, upgrades, scaling, and operational responsibility to the customer.

2. LangSmith

LangSmith combines observability with the broader AI application and agent development workflow. It provides tracing, evaluations, production monitoring, debugging, cost tracking, and visibility into multi-step agent runs.

It is particularly relevant for teams already working with LangChain or LangGraph, although the platform also supports applications built outside that ecosystem.

Best for: Teams building and monitoring agents as part of a broader development workflow.

Pricing: Free Developer plan; Plus starts at $39/seat/month.

Limitation: Teams that do not need its broader agent-development workflow should compare its capabilities and cost against more observability-focused platforms.

3. Braintrust

Braintrust puts evaluations and AI quality close to the center of its platform. Teams can trace production activity, create datasets, run evaluations, compare experiments, and use production data to identify quality regressions.

This makes it particularly useful when prompts, models, retrieval logic, or agent workflows are changing frequently and engineering teams need evidence that a new version actually performs better.

Best for: AI teams where evaluation and quality measurement are central to the development cycle.

Pricing: Free Starter plan; Pro starts at $249/month.

Limitation: Teams mainly looking for lightweight telemetry collection may not need the broader evaluation workflow.

4. Helicone

Helicone approaches observability partly through the gateway layer. It provides monitoring for LLM requests, sessions, costs, latency, errors, and usage while also offering gateway capabilities for applications working across model providers.

This architecture can be useful when a team wants to centralize model traffic and observability rather than instrument every part of the application independently.

Best for: Teams looking for gateway-oriented LLM monitoring and centralized model visibility.

Pricing: Free plan; Pro starts at $79/month.

Limitation: Teams should verify the current deployment, feature, and pricing details before purchase because gateway and observability requirements can vary significantly by architecture.

5. Datadog LLM Observability

Datadog LLM Observability brings AI application telemetry into the wider Datadog monitoring environment. It can track LLM and agent traces, latency, token usage, costs, errors, and evaluations alongside existing infrastructure and application monitoring.

That makes it particularly relevant for organizations already using Datadog and wanting AI workloads represented within the same operational environment.

Best for: Enterprise teams that already use Datadog for infrastructure and application monitoring.

Pricing: Usage-based.

Limitation: Its value is easier to justify when Datadog is already an important part of the organization's observability stack.

6. W&B Weave

W&B Weave connects LLM tracing and production monitoring with the broader Weights & Biases development ecosystem. It supports tracing, evaluations, experiments, monitoring, alerts, and AI quality workflows.

The platform is useful for teams that want to connect experimentation and production behavior rather than treating observability as an isolated dashboard.

Best for: AI engineering teams already using or evaluating the W&B ecosystem.

Pricing: Usage-based, with allowances determined by the current W&B plans.

Limitation: Pricing can depend on telemetry volume and usage, so teams should estimate their expected production volume before comparing costs.

7. Arize Phoenix

Arize Phoenix is an open-source observability and evaluation platform built around tracing, OpenTelemetry, OpenInference, datasets, experiments, and AI application evaluation. Its commercial Arize AX platform extends these capabilities for production environments.

Phoenix is particularly relevant for engineering teams that want an open-source route to tracing and evaluation while retaining the option to move into a commercial platform.

Best for: Teams building around open standards and open-source AI observability.

Pricing: Phoenix is open source; Arize AX has free and paid commercial plans.

Limitation: Buyers need to distinguish Phoenix from the broader commercial Arize platform when comparing enterprise features.

8. Galileo

Galileo combines LLM and agent observability with evaluation and runtime guardrails. Its focus extends beyond simply recording traces to understanding agent behavior and measuring the quality of AI workflows.

That makes it relevant for teams deploying agents where tool use, multi-step execution, and output quality need to be monitored together.

Best for: Teams that need agent observability combined with evaluation and runtime controls.

Pricing: Free tier; Pro starts at $100/month when billed annually.

Limitation: Teams only looking for basic LLM tracing may not need the broader agent and guardrail capabilities.

9. Laminar

Laminar focuses heavily on debugging AI agents and understanding their execution traces. It provides visibility into agent runs, tool calls, model interactions, and the sequence of events that produced an outcome.

This is useful when the main debugging challenge is not a failed API request but an agent taking an unexpected route through a multi-step workflow.

Best for: Developers debugging complex AI agent workflows.

Pricing: Free plan; Hobby starts at $25/month.

Limitation: Its strongest use case is agent debugging, so teams looking for a broad enterprise observability suite should compare it against platforms with wider infrastructure integrations.

10. AgentOps

AgentOps focuses on agent observability through session tracking, replay, debugging, monitoring, audit information, and cost visibility. The platform is designed around reconstructing what an agent did during a run.

Session-level visibility can be particularly useful when an agent behaves inconsistently or when developers need to reproduce a problematic run and inspect the sequence of actions.

Best for: Developers who need session replay and debugging for AI agents.

Pricing: Free plan; Pro starts at $40/month.

Limitation: Teams seeking a complete enterprise observability stack may need additional infrastructure monitoring alongside AgentOps.

How These Platforms Differ

The biggest difference is not whether a platform supports tracing. Most serious options do. The more useful question is what happens around the trace.

How to Choose an LLM Observability Platform

Start with the failure you need to diagnose
If the problem is rising latency or model cost, basic request and token telemetry may be enough. If agents are making poor decisions, you need visibility into tool calls and multi-step execution. If responses are technically valid but getting worse, evaluations and regression testing become more important.

Check how telemetry enters your stack
Instrumentation matters because it determines what the platform can actually see. Check support for your frameworks, SDKs, OpenTelemetry, OpenInference, gateways, and custom instrumentation before committing to a platform.

Understand your data boundary
Prompts and responses can contain sensitive information, while agent traces can expose tool arguments, retrieved documents, and user data. If deployment or data residency is a major requirement, review our guide to self-hosted vs SaaS LLM observability before choosing between cloud and self-managed options.

Calculate the cost at production scale
Free tiers can make platforms look inexpensive during development. Production pricing may depend on traces, spans, seats, data ingestion, retention, events, or evaluation volume. Estimate your expected telemetry before comparing monthly prices.

Separate observability from evaluation
Tracing tells you what happened. Evaluation helps determine whether the result was good. Some platforms combine both tightly, while others are stronger in one area. Teams building production AI should decide whether they need debugging visibility, quality measurement, or both.

FAQs

What are LLM observability platforms?

LLM observability platforms help teams inspect and monitor AI applications by collecting information such as model calls, traces, latency, token usage, costs, retrieval steps, tool calls, and evaluation results.

How is LLM observability different from traditional APM?

Traditional APM focuses primarily on application and infrastructure behavior. LLM observability adds AI-specific information such as prompts, model outputs, token usage, retrieval context, evaluations, and agent tool calls.

Which LLM observability platforms support OpenTelemetry?

Several platforms support OpenTelemetry or related open standards, including Langfuse and Arize Phoenix. Support varies by product and integration, so teams should check the current documentation for the exact telemetry they need to export or ingest.

Do AI agents need specialized observability?

Not necessarily a completely separate platform, but agents create additional observability requirements. Their traces may need to show tool calls, intermediate steps, handoffs, loops, costs, and task outcomes that are not visible in a simple model request.

What is the difference between LLM tracing and evaluation?

Tracing reconstructs what happened during an AI workflow. Evaluation measures the quality or correctness of the resulting behavior. Production AI systems often need both because a trace can explain a failure while an evaluation can identify whether the system actually improved.

Final Verdict

LLM observability is becoming less about collecting another set of AI logs and more about understanding the complete behavior of production AI systems.

Langfuse and Arize Phoenix stand out for teams that value open-source observability. LangSmith connects tracing with agent development, while Braintrust emphasizes evaluation and AI quality. Helicone takes a gateway-oriented approach, Datadog brings LLM telemetry into enterprise monitoring, and W&B Weave connects production behavior with experimentation. Galileo, Laminar, and AgentOps put more emphasis on agent behavior, debugging, and runtime visibility.

The right platform depends on what you need to see. Before choosing one, define the failures you need to diagnose, the telemetry your architecture produces, the evaluation workflows you require, and the data boundary your organization can accept. That will narrow the shortlist more effectively than choosing the platform with the longest feature list.