What is the main difference between Grafana Cloud and Langfuse?

Grafana Cloud lets you debug an agent and the services behind it in one place, with agent conversations linked to traces, metrics, and logs across your stack. Langfuse gives you agent tracing, evaluations, and prompt management in a dedicated platform. Both can monitor production agents.

Can I reuse existing OpenTelemetry instrumentation?

You can send compatible generative AI spans without the Agent Observability SDK. OpenTelemetry ingestion is in public preview. Refer to Instrument agents with OpenTelemetry for setup steps.

Is Grafana cheaper than Langfuse?

Compare costs for the same workload: Langfuse counts traces, observations, and scores; Grafana Cloud counts generations and separately bills evaluation and guard tokens and telemetry. Those units are not equivalent.

Is Langfuse a better AI observability platform than Grafana Cloud?

Each fits a different starting point. Choose Langfuse if you want an open-source platform for agent tracing, evaluations, and prompt management, with a self-hosted option. Choose Grafana Cloud if you want agent debugging connected to full-stack observability. Both support production evaluations and offline experiments, and both can monitor production agents.

Langfuse vs. Grafana Cloud

Grafana vs Langfuse: AI agent observability compared

Grafana Cloud is an observability platform that connects AI agent conversations and evaluations to the metrics, logs, and traces behind your services. Langfuse is a platform built for agent tracing, evaluations, and prompt management. Use Grafana Cloud to trace a bad answer to the service that caused it. See how they compare.

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Trace a bad answer to the service that caused it

Open the trace behind any conversation and see which tool call failed, where the error is, and how long each step took

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Hear about agent problems before your users do

Set thresholds for agent errors, latency, estimated cost, or evaluation scores using Grafana Alerting, the same alerting you run for your services

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Check a change before release

Run the same test suite against two agent versions and compare scores, cost, and linked traces

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Turn a failure into a test with Assistant

Ask Assistant to find failed model calls, inspect their inputs, and tool results, and help you write an evaluation so the quality or safety failure doesn’t repeat

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Comparing Grafana Cloud vs. Langfuse

Capabilities		Langfuse
What you investigate	Agent conversations linked to traces, alongside service metrics and logs in Grafana Cloud. Reduce mean time to resolution by connecting AI failures to the services and dependencies that caused them. Learn more	Langfuse Traces LLM calls, tool and retrieval steps, sessions, and evaluation results inside a dedicated agent platform.
OpenTelemetry support	The Agent Observability SDK emits OpenTelemetry traces and metrics, plus a separate generation-data export. You can also send compatible generative AI spans without the SDK; OpenTelemetry ingestion is in public preview. Learn more	Langfuse OTel-based Python and JavaScript SDKs; other languages can send spans to the OTel endpoint.
Optimize cost and performance	Agent Observability estimates the cost of every generation from its token usage and a price for the model that served it. By default, it uses public list prices from the model card catalog. If you pay a different negotiated price, set your own price for that model. Learn more	Langfuse You can set negotiated rates by creating a custom model definition.
Usage and billing	Bills model calls as generations (one model call made by your agent), plus evaluation and guard tokens and telemetry. The agent's model-provider bill is separate. Learn more	Langfuse Cloud bills for traces, observations, and scores, so one agent run can create several billable units. Self-hosted open source has no usage-based license charge.
Dashboards & alerts	Built-in agent dashboards and Grafana alerts on latency, errors, estimated cost, and evaluation scores, including SLO-based alerts. Route incidents through IRM and work from the Grafana bot in Slack or Microsoft Teams. Learn more	Langfuse Custom dashboards for quality, latency, volume, and cost, with metric-threshold alerts and a Slack integration.
Ecosystem & integrations	SDKs for Go, Python, TypeScript, Java, and .NET, with integrations for LangChain, LangGraph, OpenAI Agents, and Vercel AI SDK. Learn more	Langfuse Native Python and JavaScript SDKs, framework integrations, and APIs; additional languages can send OpenTelemetry spans.
AI & investigations	Grafana Assistant inspects conversations and model calls and helps create evaluation rules, subject to permissions and approval. Alert Enrichment can be configured to auto-trigger an investigation, run webhooks, or query an external data source. Learn more	Langfuse Langfuse Assistant explores project data and proposes resource changes with approval. Its self-hosted version is documented as public beta.
Prompt versioning	Tracks agent versions and prompt changes reported by your instrumentation (or derived from the system prompt) and compares quality, cost, and latency across versions. Learn more	Langfuse Each prompt save creates a new immutable version with an automatic version ID, and versions can be labeled.
Guards (block / redact / transform)	Guards, invoked through SDK hooks, evaluate requests before model calls to block requests, redact sensitive text, or filter tool calls. Learn more	Langfuse Not a primary capability. Runtime blocking and redaction require external guardrail tools. Langfuse traces these checks and evaluates their effectiveness.
Hosting	Agent Observability runs in Grafana Cloud. Setup requires enabling it on a Cloud stack. Learn more	Langfuse Langfuse Cloud, or self-hosted open source under the MIT license. A self-hosted Enterprise option is also available.

Grafana Labs: A Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms

Gartner evaluated 19 vendors based on their Ability to Execute and Completeness of Vision, and Grafana Labs was positioned furthest in Completeness of Vision.

^{Gartner, Magic Quadrant for Observability Platforms, Padraig Byrne, Pankaj Prasad, Martin Caren, D.B. Cummings, Tanmay Bisht, July 2026.}

^{Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.}

^{Gartner and Magic Quadrant are a trademark of Gartner, Inc., and/or its affiliates. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Grafana Labs.}