What is the main difference between Grafana Cloud and LangSmith?

Grafana Cloud connects agent debugging with full-stack observability, so you can reduce mean time to resolution by tracing AI failures to the services and dependencies that caused them. LangSmith offers agent tracing, evaluation, prompt management, and deployment. Both can monitor production agents.

Do I need LangChain to use LangSmith?

No. You can trace other frameworks and custom code. LangSmith does not require LangChain.

Is LangSmith a better AI observability platform than Grafana Cloud?

Each fits a different starting point. Choose LangSmith if you want agent testing and deployment services together. Choose Grafana Cloud if you want agent debugging connected to observability. Both can monitor production agents.

LangSmith vs. Grafana Cloud

Grafana vs LangSmith: AI agent observability compared

Grafana Cloud is an observability platform that puts AI agent conversations and evaluations next to the metrics, logs, and traces for your services. LangSmith is a platform for agent tracing, evaluation, prompt management, and deployment. If your team already investigates production issues in Grafana Cloud, you can debug agents in the same place. See how they compare.

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Follow the tool call where an agent went wrong

Open the trace behind a conversation to inspect tool calls, errors, and time spent in each step.

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Get alerted on agent problems

Set alerts on thresholds for agent errors, latency, estimated cost, or evaluation scores using Grafana Alerting.

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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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Ask Assistant to turn a failure into a test

Find failed model calls and inspect their captured inputs and tool results. Ask Assistant to help create an evaluation so the quality or safety failure doesn’t repeat.

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Comparing Grafana Cloud vs. LangSmith

Capabilities		LangSmith
What you investigate	Follow agent conversations into traces and related service metrics and logs, all in one place, to connect AI failures to underlying services and dependencies and resolve issues faster. Learn more	LangSmith Traces agent execution across frameworks and custom stacks.
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	LangSmith Accepts OpenTelemetry traces and documents OTel tracing configuration.
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	LangSmith You can overwrite pricing for default models or create custom entries for other models if you have negotiated rates.
Evaluation & experiments	Score live traffic with online evaluations, including built-in safety and secret-detection templates, and alert on score drops. Compare agent versions before release with experiments you run yourself. Learn more	LangSmith Online evaluations, curated datasets, offline experiments, and human, code, or model-based scoring.
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	LangSmith Every saved prompt update creates a new commit with a unique commit hash.
Dashboards & alerts	Built-in dashboards and Grafana alerts for agent metrics. Learn more	LangSmith Custom dashboards, with threshold alerts via webhooks or PagerDuty.
Usage and billing	Bills model calls as generations (one model call made by your agent), plus evaluation/guard tokens and telemetry. Learn more	LangSmith Paid seats and metered services. A trace can contain many model calls, and retention affects trace pricing.
Ecosystem & integrations	SDKs for Go, Python, TypeScript, Java, and .NET; integrations include LangChain, LangGraph, OpenAI Agents, and Vercel AI SDK. Learn more	LangSmith Works with LangChain, LangGraph, other frameworks, and custom implementations.
AI-assisted investigation	Assistant can inspect calls and help configure evaluations. Alert Enrichment can start an investigation, call an external service, or query a Grafana data source when an alert fires. Learn more	LangSmith Engine detects recurring trace issues, diagnoses against connected code, and proposes pull requests. Requires enablement and configuration.
Hosting	Agent Observability runs in Grafana Cloud. Setup requires enabling it on a Cloud stack. Learn more	LangSmith Cloud service; Enterprise offers hybrid and self-hosted options.
Setup	Install the gcx plugin, then use its agento11y-instrument skill to instrument your app and send generations to Agent Observability. The skill verifies that data lands in Grafana Cloud. Learn moreOpens in new tab	LangSmith Use LangSmith instrumentation or its OTel ingestion path; map attributes for the views you need.

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.}