Short answer: choose Grafana when your organization already runs Grafana or wants one production observability system for infrastructure, services, and AI agents. Choose Langfuse when the main job is improving LLM applications with AI-specific traces, prompts, evaluations, datasets, and cost analysis. OpenTelemetry underpins both approaches, so a staged or combined deployment can avoid a permanent instrumentation lock-in.
Grafana vs Langfuse at a glance
These products overlap, but they are optimized for different operating models. Grafana treats AI-agent telemetry as an extension of a broad observability platform. Langfuse treats observability as part of an AI-engineering workflow centered on LLM behavior and iteration.
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| Decision axis | Grafana | Langfuse |
|---|---|---|
| Primary orientation | Broad observability platform extended for AI agents | AI-native tracing and engineering platform |
| Best fit | Teams already operating Grafana Cloud or needing infrastructure and agent telemetry together | Teams focused on LLM behavior, prompts, evaluations, datasets, and agent debugging |
| Trace scope | Agent conversations, planning, tool calls, LLM calls, final responses, and related OpenTelemetry telemetry | LLM calls and application logic such as retrieval, embeddings, and agent actions |
| Evaluation | Online evaluation rules, guards, offline experiments, and quality analysis | LLM-as-a-Judge and other evaluation workflows, with experiments and datasets |
| Cost visibility | Generation, token, and telemetry analytics in Grafana Cloud | Token and cost tracking with Metrics API aggregation and filtering |
| Interoperability | OpenTelemetry foundation plus framework integrations | OpenTelemetry-based and model- and framework-agnostic positioning |
| Deployment | Managed Grafana Cloud, with Grafana’s broader open-source foundations | Managed Cloud or self-hosted open-source deployment |
The practical dividing line is breadth versus AI-specific depth. Grafana is usually the better operational control plane when reliability teams already use its dashboards, alerting, metrics, logs, traces, and profiles. Langfuse is usually the better specialist workspace when application developers need to inspect generations, compare prompt versions, build datasets, and run systematic evaluations.
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End-to-end agent traces
Grafana Cloud Agent Observability is documented as an OpenTelemetry-based service for production AI agents. It can capture LLM generations, tool calls, workflow steps, token usage, and cost data. A trace can connect a user request to planning, tool invocations, model calls, and the final response, while related metrics and traces remain available in the wider Grafana environment.
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Grafana Labs describes the service this way: “Grafana Agent Observability provides end-to-end tracing and monitoring for AI agents, allowing you to track user requests through planning, tool invocations, LLM calls, and final responses.” That scope matters when an incident crosses boundaries—for example, a slow database call inside a tool, an overloaded service, and an unexpectedly expensive model response.
Conversation and version analysis
The product positioning includes browsing and replaying conversations, tracing tool calls, tracking costs, and detecting unsafe output, hallucinations, and policy violations. Teams can compare agent versions and analyze behavior alongside ordinary infrastructure telemetry rather than switching to a separate operational console.
Framework integrations
Grafana lists built-in integrations for LangChain, LangGraph, OpenAI Agents, the Vercel AI SDK, and other frameworks. The exact instrumentation available depends on the framework and the telemetry emitted by your application, so confirm field coverage for the versions you run before standardizing dashboards.
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Grafana supports online evaluations, guards, offline experiments, and analytics across metrics, logs, traces, and profiles. Its broader AI stack is presented as open at the foundation, while the full managed experience is available in Grafana Cloud. This combination is most useful when the same team owns service-level objectives and agent-quality controls.
What Langfuse provides for AI engineering
AI-native traces and agent views
Langfuse describes itself as an open-source AI engineering platform for debugging, analyzing, and iterating on LLM and AI-agent applications. Its documented views include traces, sessions, timelines, users, agent graphs, dashboards, and alerts. These views are designed around the questions developers ask when a model response is wrong, slow, inconsistent, or too expensive.
Langfuse traces can cover LLM calls and application logic such as retrieval, embeddings, and agent actions. That makes it possible to follow the path from an input through retrieval and tool use to the generated answer, then inspect the individual spans rather than treating the model call as an opaque request.
Prompt management, experiments, and datasets
Langfuse’s observability documentation differentiates it from general-purpose APM by emphasizing LLM-as-a-Judge evaluation, prompt management, experiments and datasets, and custom dashboards. Teams can compare latency, cost, and evaluation metrics across prompt or application versions, which supports a repeatable improvement loop instead of ad hoc trace inspection.
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Langfuse documents aggregated usage and cost queries through a Metrics API. Those queries can be filtered by application type, user, or tags. This is useful when you need to attribute model spend to a tenant, feature, workflow, or experiment rather than viewing only a global total.
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Deployment choices
Langfuse documents both Cloud and self-hosted paths. Its self-hosting material describes the platform as open source, based on OpenTelemetry, self-hostable at the same scale as Langfuse Cloud, and model- and framework-agnostic. Those are vendor documentation claims; validate capacity, upgrades, backups, security controls, support expectations, and data-residency requirements against your own operating standards.
Where the products differ in day-to-day work
Operations team versus AI-development team
Grafana fits a shared operations workflow. An on-call engineer can move from an infrastructure alert to a service trace and then into the agent conversation or tool call that caused the user-visible failure. The value is context consolidation and a common alerting and dashboarding model.
Langfuse fits an AI-development workflow. A developer can inspect a session, compare prompt or application versions, attach evaluations, manage an experiment dataset, and examine cost or latency changes. The value is the tight connection between telemetry and iteration on the application itself.
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Grafana’s trace scope explicitly includes planning, tools, model calls, final responses, and adjacent OpenTelemetry signals. Langfuse emphasizes LLM calls and application logic such as retrieval, embeddings, and agent actions, plus AI-specific engineering features. Neither scope automatically replaces the other: a specialist trace view may not provide every infrastructure signal, while a broad observability platform may not provide the same prompt-and-dataset workflow.
Evaluation model
Grafana documents online evaluation rules, guards, offline experiments, and quality analysis. This is suited to production controls that should run continuously or gate unsafe behavior.
Langfuse emphasizes LLM-as-a-Judge and related evaluation workflows, with experiments and datasets. This is suited to comparing changes before release and investigating quality regressions with repeatable examples. Decide whether your primary requirement is a live production guard, an offline development loop, or both.
Interoperability
Both products document an OpenTelemetry-oriented foundation. That gives teams a path to emit standardized telemetry and change analysis tools later. Instrumentation design still matters: preserve stable trace and span identifiers, record model and prompt-version metadata, classify tool calls consistently, and avoid putting secrets or unnecessary personal data into attributes.
Which should you choose?
Choose Grafana when these conditions describe you
- Your organization already runs Grafana Cloud dashboards, alerting, metrics, logs, traces, profiles, or OpenTelemetry pipelines.
- On-call engineers need infrastructure and agent behavior in the same incident view.
- You want one production system covering service health, tool failures, model calls, token usage, and cost.
- You need online evaluation rules or guards alongside offline experiments.
- You prefer managed operations and want framework integrations for common agent stacks.
The strongest case is organizational rather than merely technical: the telemetry lands where your operators already work, and ownership of service reliability and agent reliability can remain together.
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Choose Langfuse when these conditions describe you
- Your central problem is understanding and improving LLM application behavior rather than consolidating all infrastructure telemetry.
- Developers need first-class traces, sessions, timelines, users, and agent graphs.
- Prompt management, version comparison, experiments, datasets, and LLM-as-a-Judge evaluations are core requirements.
- You need cost and usage queries filtered by application type, user, or tags.
- You want a documented Cloud option or to evaluate a self-hosted open-source deployment.
The strongest case is an AI product team that measures quality as actively as latency and availability, and that wants observability connected directly to prompt and dataset iteration.
Use both when responsibilities are split
A combined deployment can be sensible when Grafana is the enterprise telemetry plane and Langfuse is the specialist AI-development plane. Grafana can provide broad production visibility, while Langfuse can support prompt experiments, datasets, and detailed evaluation workflows. Define ownership before deployment: decide which system is authoritative for incidents, which retains raw generations, how traces are correlated, how sampling works, and who pays for evaluation traffic.
Pricing and operating model
Grafana’s metering dimensions
Grafana documents three independent usage dimensions for Agent Observability:
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- Tokens consumed by LLM-based evaluations and guards.
- Standard OpenTelemetry metrics and traces.
The documentation states that generation and evaluation/guard metering starts on October 1, 2026, while telemetry follows standard Grafana Cloud pricing. Because the current date is October 2026, verify the active commercial terms for your region and plan before budgeting.
Langfuse cost model
Langfuse documents Cloud and self-hosted paths and provides cost analytics, but there is no universal “cheaper” result without workload assumptions. A self-hosted deployment exchanges vendor fees for infrastructure, storage, upgrades, monitoring, backups, and operational ownership. A Cloud deployment may reduce those tasks but still requires you to model event volume, retention, and evaluation activity.
Build a workload estimate before comparing invoices
| Input | Why it changes the result |
|---|---|
| Agent generations per day | Drives generation volume and storage in either platform |
| Tokens per generation | Changes model spend and, for Grafana, evaluation or guard token usage when those features are used |
| Evaluation frequency | More online judges and guards create additional model calls and metered evaluation tokens |
| Model mix | Different models have different token prices and latency profiles |
| Retention period | Longer retention increases storage and can affect query performance and governance work |
| Telemetry volume | Metrics, logs, traces, and profiles are separate from AI-generation activity in the Grafana model |
| Data residency and controls | May determine whether Cloud, self-hosting, or a particular region is acceptable |
| Operations capacity | Self-hosting requires people and processes for upgrades, backups, security, and incident response |
Calculate at least three scenarios—pilot, expected production, and peak traffic—and include evaluation traffic rather than modeling only user requests.
Implementation guide: Grafana-first rollout
- Inventory your existing telemetry. Identify current OpenTelemetry collectors, metric and trace destinations, agent frameworks, and Grafana Cloud workspaces. Record which services already emit trace IDs that can be propagated into agent requests.
- Instrument the agent path. Capture the user request, planning steps, tool calls, LLM generations, token usage, and final response. Use stable span names and attributes for model, provider, agent version, prompt version, and tool name.
- Connect framework integrations. Use the documented integration for your framework where available, such as LangChain, LangGraph, OpenAI Agents, or the Vercel AI SDK. Test a representative workflow and verify that nested tool and model spans are present.
- Create operational views. Build dashboards for request volume, latency, error rate, token usage, cost, tool failures, and evaluation results. Link panels to the corresponding traces and conversations.
- Add production controls. Configure online evaluations and guards for the failure modes you can define reliably, such as unsafe output or policy violations. Set alert thresholds only after observing normal traffic.
- Run offline experiments. Use a fixed set of representative conversations to compare agent versions and quality before changing production prompts or tools.
- Review metering. Separate generation volume, evaluation and guard tokens, and standard OpenTelemetry telemetry in your forecast. Recheck the October 1, 2026 metering change against your contract.
Implementation guide: Langfuse-first rollout
- Choose Cloud or self-hosted deployment. For self-hosting, confirm that your team can operate the required infrastructure at the scale and availability you need; treat the documented same-scale claim as something to validate, not an automatic capacity guarantee.
- Define the trace schema. Decide how sessions, users, agent versions, prompt versions, retrieval operations, embeddings, and tool actions will be represented. Keep names and tags stable so comparisons remain meaningful.
- Capture complete application traces. Record the path from input through retrieval, tool use, model calls, and output. Exclude secrets and minimize sensitive content before it enters telemetry.
- Centralize prompts and versions. Attach every production generation and experiment result to an identifiable prompt or application version.
- Build evaluation datasets. Start with real failure cases and representative successful cases. Keep the dataset versioned so quality changes can be attributed to a specific test set.
- Add evaluation workflows. Use LLM-as-a-Judge or other evaluators for appropriate quality dimensions, then review a sample manually to detect judge drift or rubric problems.
- Analyze cost and latency. Use Metrics API queries filtered by application type, user, or tags to find expensive workflows and regressions.
- Promote tested changes. Compare prompt or application versions on quality, latency, and cost before rollout, and retain the evidence used for the release decision.
How to operate a combined Grafana and Langfuse deployment
Use OpenTelemetry as the common instrumentation layer and establish a clear system boundary. Grafana can receive the production telemetry needed by on-call teams, while Langfuse receives the AI-focused data needed for prompt, evaluation, and dataset workflows.
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- Correlation: propagate one trace or correlation identifier through the user request, agent workflow, tools, and model calls.
- Ownership: assign Grafana incident alerting to the production operations team and Langfuse experiment ownership to the AI application team, unless your organization has a different split.
- Retention: keep high-volume raw generations for the shortest period that supports debugging and evaluation; retain aggregate metrics longer when trend analysis requires it.
- Sampling: sample routine traffic deliberately, but preserve complete traces for errors, policy events, evaluation failures, and selected evaluation datasets.
- Cost boundaries: tag traffic by application, environment, tenant, and experiment so generation and evaluation costs can be attributed.
- Privacy: redact secrets and unnecessary personal data before export, and document who can view conversation content.
Evaluation strategy: production controls versus improvement loops
Do not treat every evaluation as the same job. A production guard answers whether a response should be blocked, flagged, or routed differently now. An offline experiment answers whether a new prompt, model, or tool policy performs better across a controlled set of examples.
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Use online evaluations and guards for
- Unsafe output and policy violations that require immediate action.
- Regression signals that can trigger an alert or fallback.
- Quality checks attached to live workflows where latency and evaluation cost are acceptable.
Use offline experiments and datasets for
- Comparing prompt or agent versions before release.
- Testing retrieval, tool selection, and response quality on repeatable examples.
- Investigating failures without adding judge latency to every user request.
Grafana documents both online and offline capabilities. Langfuse emphasizes LLM-as-a-Judge, experiments, and datasets. In either system, write the rubric first, version the test set, and inspect borderline cases manually.
Common pitfalls and how to fix them
Choosing by feature count
A long checklist can obscure the operating question. Map each requirement to an owner and a workflow: who responds to a latency incident, who changes prompts, who approves a guard, and who reviews evaluation failures. Choose the platform that reduces handoffs for the highest-risk work.
Comparing prices without workload data
Generation volume, evaluation frequency, telemetry volume, retention, model mix, residency requirements, and self-hosting labor can reverse a superficial price comparison. Build the three-scenario estimate before selecting a plan.
Tracing only the model call
A model span alone cannot explain a bad answer caused by retrieval, a failed tool, a slow dependency, or a planning loop. Instrument the complete workflow and preserve parent-child relationships between spans.
Logging sensitive conversation data
Full prompts and responses may contain credentials, personal information, or proprietary content. Redact before export, limit access to conversation views, and define retention by environment and use case.
Running judges without validation
An LLM-based evaluator can be inconsistent or biased by wording. Version the rubric, compare judge results with human reviews, and monitor disagreement before using the score as an automated release gate.
Duplicating telemetry without a policy
Sending every event to two systems can double storage, ingestion, and access-control work. Decide which events each platform needs, sample routine traffic, and preserve complete traces only where they support incidents or evaluation.
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FAQ
Can Grafana trace AI agents?
Yes. Grafana Cloud Agent Observability is documented to trace user requests through planning, tool invocations, LLM calls, and final responses, while also capturing token, cost, and related OpenTelemetry data.
Is Langfuse open source and self-hostable?
Langfuse documents an open-source platform with Cloud and self-hosted deployment paths. Confirm operational capacity, security controls, support, and data-residency fit for your environment before choosing self-hosting.
Which is better for LLM observability?
For AI-specific debugging, prompt iteration, evaluations, datasets, and cost analysis, Langfuse is generally the closer fit. For one production observability system spanning infrastructure and agents, Grafana is generally the closer fit.
Do I have to choose one permanently?
No. OpenTelemetry-oriented instrumentation supports a staged or combined deployment, provided you define trace correlation, ownership, retention, sampling, and cost boundaries.
Frequently Asked Questions
Can Grafana trace AI agents?
Yes. Grafana Cloud Agent Observability is documented to trace user requests through planning, tool invocations, LLM calls, and final responses, while also capturing token, cost, and related OpenTelemetry data.
Is Langfuse open source and self-hostable?
Langfuse documents an open-source platform with Cloud and self-hosted deployment paths. Confirm operational capacity, security controls, support, and data-residency fit for your environment before choosing self-hosting.
Which is better for LLM observability?
For AI-specific debugging, prompt iteration, evaluations, datasets, and cost analysis, Langfuse is generally the closer fit. For one production observability system spanning infrastructure and agents, Grafana is generally the closer fit.
Do I have to choose one permanently?
No. OpenTelemetry-oriented instrumentation supports a staged or combined deployment, provided you define trace correlation, ownership, retention, sampling, and cost boundaries.
Quick Recap
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