While Grafana is the industry standard for monitoring LLM infrastructure health, it cannot determine if a model's response is actually correct or safe. Braintrust fills this gap by providing the evaluation framework necessary to score output quality and run regression tests against real datasets.
By linking production traces directly to scoring workflows, Braintrust allows teams to move beyond basic system uptime and focus on the accuracy of the user experience. Together, these tools create a comprehensive observability stack where Grafana manages the stability of the pipe and Braintrust validates the quality of the water flowing through it.
This guide explores how to integrate both platforms to protect your application from both infrastructure failures and "silent" quality regressions.
Grafana includes AI Observability as part of its broader monitoring platform, giving teams visibility into LLM request behavior, infrastructure signals, and basic output checks within the same operational environment.
The GenAI Observability dashboard tracks request volume, response times, error rates, token usage, and cost by provider. Teams can review latency trends, compare token consumption across models, and monitor spending over time. Grafana's alerting system sends notifications when latency exceeds configured thresholds, error rates increase, or usage drives unexpected cost changes.
Grafana also monitors the infrastructure around LLM calls. Separate dashboards cover vector database query performance, GPU utilization and thermal metrics, and MCP tool usage patterns, so the full AI stack is visible from a single interface that most engineering teams already know how to operate.
Grafana includes basic output checks through OpenLIT's evaluators. The GenAI Evaluations dashboard runs hallucination detection, toxicity analysis, and bias assessment on model outputs, providing visibility into common safety and content risks.
Grafana's built-in evaluations primarily focus on safety checks. Hallucination detection, toxicity scoring, and bias assessment flag outputs that cross predefined boundaries, but they do not measure answer correctness for specific use cases, the quality impact of prompt changes, or an agent's reasoning path. As LLM applications move from prototype to production, broader evaluation requirements become more important.
Grafana renders OTEL spans with start time, duration, and status code. In a multi-step agent that retrieves context, reasons about it, calls tools, and composes a response, the trace appears as a sequence of operations with basic timing and status information. The trace can show that a retrieval call took 120ms and a model call took 800ms, but it does not reveal which documents were retrieved, whether that context was relevant, how the model used the retrieved information, or why one tool was selected instead of another. Debugging an incorrect agent output in Grafana requires reading raw span attributes and manually reconstructing the reasoning chain.
Grafana's OpenLIT evaluations run a fixed set of safety checks, but teams building customer support agents, code generation tools, or search experiences often require evaluation criteria tied to their domain. A support agent's output may need to be scored for factual accuracy against a specific knowledge base and adherence to company tone guidelines, while a code generation tool may require scoring for correctness, security, and style compliance. Grafana does not support LLM-as-a-judge scoring with custom rubrics, code-based scoring functions, or structured human annotation workflows that feed results back into measurable quality metrics.
Grafana has no mechanism for storing, versioning, or deploying prompts. When output quality degrades in production, teams cannot trace the regression back to a specific prompt change through Grafana's interface because Grafana's data model does not link a prompt version to the output quality.
Grafana provides dashboards for viewing historical telemetry data, but it does not include an environment where teams can test prompts or model changes against an evaluation suite before deployment. Engineers cannot load two prompt versions side by side, run them against production data, and compare structured quality scores to determine which version should ship. As a result, changes move to production first, and teams monitor the outcome afterward.
Grafana does not post eval results on pull requests, block merges when quality drops below thresholds, or run automated regression detection when prompts or models change. Quality gates for LLM outputs are not part of Grafana's deployment workflow.
To see how Braintrust addresses evaluation gaps in Grafana, consider a real failure pattern: An AI support agent starts giving wrong answers after a developer updates a system prompt on Tuesday morning.
In Grafana, the GenAI Observability dashboard shows a slight latency increase and a small uptick in error rates, while token usage remains stable and every trace shows spans completing successfully. The dashboard does not indicate that response quality declined because Grafana measures infrastructure health rather than output correctness. The team only identifies the issue on Wednesday afternoon, when a customer service manager reports a spike in complaint tickets.
In Braintrust, the regression surfaces within minutes because each response is automatically scored against the team's defined evaluation criteria. Tracing captures the full execution chain for every request, including which documents the retrieval step returned, what the model generated, and which tools it invoked. When the Tuesday prompt update introduces inaccuracies, the accuracy scorer drops from 0.85 to 0.3 across support requests, and the decline is immediately visible in the monitoring view.
The trace interface links each output to the exact prompt version that produced it, allowing the team to identify the Tuesday update as the source of the regression without reviewing application logs. An engineer opens the Braintrust Playground, loads the previous and current prompt versions side by side, and runs both against the existing evaluation dataset. The comparison confirms that the earlier version maintains an accuracy score of 0.85 while the updated version falls to 0.3.
After reverting the prompt, the engineer opens a rollback pull request, and the Braintrust GitHub Action runs the evaluation suite automatically to verify that accuracy is restored before the change reaches users. The failing traces can then be converted into permanent test cases with a single click, adding them to the dataset so that similar regressions are detected automatically in future deployments.
The workflow above, which identified the prompt regression, validated the fix, and enforced quality before deployment, relies on several Braintrust features working together.
Exhaustive trace logging: Every LLM call is logged with automatic capture of duration, token usage, tool calls, and errors. The trace view renders the full execution chain with nested spans, allowing teams to inspect retrieved context, generated outputs, and tool decisions at each step.
Custom scorers: LLM-as-a-judge scorers, code-based scoring functions, and human annotation workflows evaluate outputs against criteria defined by the team. Braintrust includes more than 25 built-in scorers through its open-source autoevals library, covering factuality, closeness, relevance, and additional evaluation dimensions. Quality standards stay consistent from development to production because the same scorers from offline experiments can evaluate live traffic asynchronously, without any added latency.
Brainstore database: Brainstore is a database engineered for AI workloads, enabling teams to debug production issues and search through millions of traces in seconds, something traditional observability databases struggle with given the size and complexity of LLM traces.
Prompt versioning: Each prompt receives a content-addressable version ID that links to every trace it produces, allowing regressions to be traced back to the exact prompt update responsible. Teams version prompts within Braintrust, test variants against datasets in the evaluation playground, compare scorer outputs, and deploy validated versions directly.
Evaluation playground: Teams test prompt or model changes against real production data before deployment, comparing variants side by side using structured quality scores. Product managers iterate on prompts in the interface while engineers maintain code-based tests, and both roles rely on the same evaluation results when approving changes.
CI/CD quality gates: The native GitHub Action, braintrustdata/eval-action, posts evaluation results directly on pull requests and blocks merges when scores fall below configured thresholds. Teams define quality standards so that evaluation becomes part of release control rather than a separate post-deployment review step.
Loop AI assistant: Braintrust's built-in assistant generates scorers, evaluation datasets, and improved prompt variants from natural language descriptions, reducing the manual effort required to maintain evaluation infrastructure.
Organizations including Notion, Stripe, Zapier, Vercel, Instacart, and Airtable use Braintrust in production for LLM evaluation and observability. Notion reported an increase in the number of issues identified and resolved per day after adopting Braintrust.
Start with Braintrust's free tier, which includes 1 million trace spans and 10,000 evaluation scores per month, allowing teams to test structured evaluation workflows before committing.
| Capability | Grafana | Braintrust | Winner |
|---|---|---|---|
| OpenTelemetry-native instrumentation | Yes, via OpenLIT SDK | Yes, via Braintrust SDK with OpenTelemetry support | Tie |
| Latency, error rate, and cost dashboards | Yes, pre-built GenAI dashboards for latency, errors, token usage, and cost | Yes, automatic capture of latency, token usage, and cost metrics on every trace | Tie |
| GPU and infrastructure monitoring | Yes, dedicated GPU dashboards with utilization, thermals, and memory tracking | No | Grafana |
| Vector database monitoring | Yes, dedicated VectorDB dashboards | Vector database calls traced within agent execution chains, but no dedicated infrastructure dashboards | Grafana |
| MCP server monitoring | Yes, dedicated MCP dashboards | Tool calls traced within execution chains, but no MCP infrastructure dashboards | Grafana |
| Safety evaluations (hallucination, toxicity, bias) | Yes, via OpenLIT built-in safety evaluators | Supported through custom LLM-as-a-judge scorers and evaluation workflows | Tie |
| Custom quality scoring with domain-specific rubrics | No native support for domain-specific rubric-based scoring | Yes, with LLM-as-a-judge, code-based scorers, and human annotation workflows | Braintrust |
| Full agent reasoning chain visibility | Generic OpenTelemetry spans with timing and metadata | Nested trace views showing retrieval context, model outputs, and tool calls within a structured execution chain | Braintrust |
| Prompt versioning and management | No | Yes, with content-addressable version IDs linked to every trace | Braintrust |
| Eval playground for pre-deployment testing | No structured evaluation environment for dataset-based prompt testing | Yes, side-by-side prompt comparison against real datasets before deployment | Braintrust |
| CI/CD quality gates | No built-in evaluation gating in CI workflows | Yes, native GitHub Action with PR comments and merge blocking based on evaluation thresholds | Braintrust |
| One-click trace to test case conversion | No | Yes | Braintrust |
| Human annotation workflows | No built-in structured annotation workflows tied to quality metrics | Yes, configurable review interfaces per task type | Braintrust |
| AI-assisted eval generation | No | Yes, via Loop for scorer, dataset, and prompt generation | Braintrust |
| Free tier | Forever-free Grafana Cloud tier with core observability features | 1M trace spans, 10K evaluation scores, unlimited users | Tie |
Start with Braintrust for free to integrate evaluation into your release workflow.
Teams already using Grafana can extend it to LLM services, tracking uptime, latency, GPU utilization, and cost alongside existing infrastructure within a familiar console.
Braintrust handles output quality and release decisions by connecting production traces to scoring workflows, regression datasets, prompt version history, and CI-integrated merge controls, with quality thresholds determining whether a prompt update or model change ships.
Using both Braintrust and Grafana creates a natural division of responsibilities, with Grafana owning infrastructure health and Braintrust enforcing model quality standards. OpenTelemetry lets teams route the same trace data to both platforms without duplicating instrumentation.
When one platform must own the LLM layer end-to-end, infrastructure monitoring alone cannot determine whether responses meet business, policy, or accuracy standards. Braintrust combines tracing with structured evaluation and release enforcement, making it a stronger standalone choice for governing production LLM behavior.
Start with Braintrust for free to integrate structured evaluation into your production workflow.
While Grafana records operational data like latency and cost, it cannot verify if a model's response is actually correct. Braintrust provides the essential evaluation layer needed to score output quality against specific criteria such as accuracy, relevance, and safety. By integrating Braintrust, teams move beyond simply confirming that a request executed to determine whether the response actually met the application's standards. This ensures that while Grafana monitors the infrastructure, Braintrust validates the intelligence and reliability of the system.
Grafana provides limited output evaluation through OpenLIT's built-in checks for hallucination, toxicity, and bias. These safety-oriented checks flag outputs that exceed predefined safety thresholds, but Grafana does not support custom quality scoring, LLM-as-a-judge evaluations with domain-specific rubrics, prompt-level regression testing, or human-annotation workflows. Teams that need to measure whether outputs meet their specific quality standards typically use a dedicated evaluation platform, such as Braintrust, alongside Grafana.
If an LLM application only requires uptime monitoring, latency tracking, and cost management, Grafana covers all three. When release decisions depend on measurable output quality, prompt version control, structured regression testing before deployment, or CI-integrated quality gates, infrastructure monitoring alone does not provide sufficient control. Braintrust provides structured scoring, prompt version management, regression workflows, and CI enforcement while connecting to the same OpenTelemetry pipeline that feeds Grafana dashboards.
Grafana focuses on infrastructure monitoring, and its evaluation capabilities are limited to safety checks through OpenLIT. Braintrust centers on structured evaluation, prompt versioning, regression testing, and CI-based quality gates for LLM applications. For teams that need evaluation results to determine whether changes are safe to deploy, Braintrust provides a more complete system.
Braintrust is the best evaluation platform for teams shipping LLM applications to production. Braintrust's integrated workflow, where production traces become test cases with one click, evals run on every pull request, and prompt changes are validated before deployment, closes the loop between observing failures and fixing them. Companies like Notion, Stripe, and Zapier rely on Braintrust because it connects evaluation directly to the development and deployment cycle rather than treating it as a separate activity. Get started with Braintrust for free.