Building Dashboards for LLM Usage and Performance

Moussa Coulibaly — Thu, 02 Jul 2026 17:28:46 +0000

An analysis of key metrics and tools for creating effective LLM usage and performance dashboards. For teams needing enterprise-grade observability, tools like Bifrost provide built-in metrics and integrations to simplify the process.

Tracking the behavior of large language models in production is essential for maintaining application reliability, managing costs, and ensuring a high-quality user experience. As AI applications scale, manually monitoring API calls becomes impractical. Engineering teams require dedicated LLM usage and performance dashboards to visualize key metrics, identify trends, and troubleshoot issues. An open-source AI gateway like Bifrost can serve as a central point for collecting the necessary data for these dashboards.

Why Dashboards are Critical for LLM Operations

Dashboards provide a consolidated, real-time view of an AI application's health. Without them, teams operate with significant blind spots, reacting to problems only after they impact users. A well-designed dashboard helps teams proactively manage several key areas:

Cost Management: Visualize token consumption and cost per request, per user, or per model to prevent budget overruns.
Performance Monitoring: Track metrics like latency (time to first token and total response time) and throughput to ensure the application meets performance SLOs.
Error Detection: Quickly identify and diagnose spikes in API errors, provider outages, or model-specific failures.
Usage Analysis: Understand which models are being used most frequently, who the top users are, and how request patterns change over time.

Key Metrics to Track in an LLM Dashboard

An effective LLM dashboard goes beyond simple request counts. It should provide a granular view into the operational metrics that directly affect cost, performance, and reliability. Teams should focus on visualizing the following categories.

Cost and Usage Metrics

Token Counts: Track prompt tokens, completion tokens, and total tokens per request. Aggregate this data by model, user, and time period.
Request Volume: Monitor the total number of requests, broken down by model and API key.
Estimated Cost: If cost data is available, visualize the cumulative cost over time to align with budget forecasts.

Performance and Latency Metrics

End-to-End Latency: The total time from when a request is sent to when the final token is received.
Time to First Token (TTFT): Measures how quickly the model begins generating a response. This is a critical metric for user-perceived performance in streaming applications.
Tokens per Second (Throughput): Indicates the generation speed of the model once it starts responding.

Reliability and Error Metrics

Error Rate: The percentage of requests that fail, categorized by HTTP status code (e.g., 4xx, 5xx) and provider-specific error types.
Provider Health: Monitor the uptime and response times of each connected LLM provider to detect outages or degradation.
Fallback and Retry Rates: If using a gateway with automatic fallbacks, track how often requests are rerouted due to primary provider failures.

Approaches to Building LLM Dashboards

Teams have several options for building and deploying dashboards, ranging from using managed services to building custom solutions on open-source tooling.

1. Using an AI Gateway with Built-in Observability

The most direct approach is to use an AI gateway that provides observability features out of the box. A gateway like Bifrost is positioned to capture detailed metadata about every request and expose it in standard formats.

Native Prometheus Metrics: Bifrost exposes a /metrics endpoint compatible with Prometheus, a leading open-source monitoring system. This allows teams to scrape detailed metrics on requests, latency, token counts, and errors directly from the gateway. These metrics can then be visualized in Grafana, a popular open-source dashboarding tool.
OpenTelemetry Integration: For more complex environments, Bifrost supports the OpenTelemetry (OTLP) standard. This enables the export of distributed traces and metrics to compatible backends like Honeycomb, New Relic, or Jaeger, providing deeper insights into the entire request lifecycle.
Dedicated Connectors: For enterprises standardized on specific platforms, Bifrost offers a Datadog connector that sends traces, metrics, and logs directly to Datadog for unified observability.

This approach centralizes data collection at the infrastructure layer, requiring no changes to the application code itself.

2. Instrumenting Application Code

Alternatively, teams can add monitoring libraries directly to their application's source code. SDKs for platforms like OpenAI and Anthropic can be wrapped with custom code to log metrics to a time-series database or observability platform.

While this method offers high flexibility, it also has drawbacks:

Increased Complexity: Each application and service must be individually instrumented and maintained.
Inconsistent Data: It can be difficult to ensure that all teams are collecting the same set of metrics in a consistent format.
Lack of Central Control: Governance and routing logic are distributed across applications rather than managed from a central point.

3. Leveraging Managed LLM Observability Platforms

Several third-party platforms specialize in LLM observability. These services typically provide an SDK that teams integrate into their applications. The SDK sends data to the vendor's platform, which offers pre-built dashboards and analytics tools. This can accelerate deployment, but it also introduces a dependency on an external service and may not provide the same level of control as a self-hosted gateway.

How Bifrost Simplifies Dashboard Creation

Using an AI gateway like Bifrost as the data source for dashboards provides a powerful and scalable solution. Because all LLM traffic routes through the gateway, it becomes the single source of truth for all operational metrics.

The gateway's native observability features mean that engineering teams can connect their existing monitoring tools like Grafana or Datadog and start building dashboards immediately. For example, a team could create a Grafana dashboard with panels for:

Requests per Minute: A time-series graph showing total throughput.
P95 Latency by Model: A chart tracking the 95th percentile latency for each model.
Token Usage by Virtual Key: A table showing which projects or users are consuming the most tokens, using Bifrost's virtual keys for attribution.
Error Rate by Provider: A pie chart breaking down errors by the upstream LLM provider.

This setup not only provides deep visibility but also reinforces security and governance. Bifrost applies central governance policies, and with Bifrost Edge, that same visibility and control can be extended to AI usage on employee endpoints, ensuring that even traffic from desktop tools is captured in the central dashboards.

Getting Started with LLM Dashboards

Effective dashboards are a cornerstone of reliable AI operations. They transform raw operational data into actionable insights, enabling teams to optimize performance, control costs, and quickly resolve production issues. While multiple approaches exist, centralizing metric collection at the gateway layer offers a clean, scalable, and non-intrusive solution.

Teams evaluating AI gateways for this purpose can request a Bifrost demo or review the open-source repository to explore its observability capabilities.

DEV Community: Moussa Coulibaly