> ## Documentation Index > Fetch the complete documentation index at: https://docs.getbifrost.ai/llms.txt > Use this file to discover all available pages before exploring further. # Telemetry > Comprehensive Prometheus-based monitoring for Bifrost Gateway with custom metrics and labels. ## Overview Bifrost provides built-in telemetry and monitoring capabilities through Prometheus metrics collection. The telemetry system tracks both HTTP-level performance metrics and upstream provider interactions, giving you complete visibility into your AI gateway's performance and usage patterns. **Key Features:** * **Prometheus Integration** - Native metrics collection at `/metrics` endpoint * **Comprehensive Tracking** - Success/error rates, token usage, costs, and cache performance * **Custom Labels** - Configurable dimensions for detailed analysis * **Dynamic Headers** - Runtime label injection via `x-bf-dim-*` headers * **Cost Monitoring** - Real-time tracking of AI provider costs in USD * **Cache Analytics** - Direct and semantic cache hit tracking * **Async Collection** - Zero-latency impact on request processing * **Multi-Level Tracking** - HTTP transport + upstream provider metrics The telemetry plugin operates asynchronously to ensure metrics collection doesn't impact request latency or connection performance. *** ## Default Metrics ### HTTP Transport Metrics These metrics track all incoming HTTP requests to Bifrost: | Metric | Type | Description | | ------------------------------- | --------- | ------------------------------- | | `http_requests_total` | Counter | Total number of HTTP requests | | `http_request_duration_seconds` | Histogram | Duration of HTTP requests | | `http_request_size_bytes` | Histogram | Size of incoming HTTP requests | | `http_response_size_bytes` | Histogram | Size of outgoing HTTP responses | Labels: * `path`: HTTP endpoint path * `method`: HTTP verb (e.g., `GET`, `POST`, `PUT`, `DELETE`) * `status`: HTTP status code * custom labels: Custom labels configured in the Bifrost configuration ### Upstream Provider Metrics These metrics track requests forwarded to AI providers: | Metric | Type | Description | Labels | | ---------------------------------- | --------- | ---------------------------------------------------- | ----------------------------------------- | | `bifrost_upstream_requests_total` | Counter | Total requests forwarded to upstream providers | Base Labels, custom labels | | `bifrost_success_requests_total` | Counter | Total successful requests to upstream providers | Base Labels, custom labels | | `bifrost_error_requests_total` | Counter | Total failed requests to upstream providers | Base Labels, `status_code`, custom labels | | `bifrost_upstream_latency_seconds` | Histogram | Latency of upstream provider requests | Base Labels, `is_success`, custom labels | | `bifrost_input_tokens_total` | Counter | Total input tokens sent to upstream providers | Base Labels, custom labels | | `bifrost_output_tokens_total` | Counter | Total output tokens received from upstream providers | Base Labels, custom labels | | `bifrost_cache_hits_total` | Counter | Total cache hits by type (direct/semantic) | Base Labels, `cache_type`, custom labels | | `bifrost_cost_total` | Counter | Total cost in USD for upstream provider requests | Base Labels, custom labels | Base Labels: * `provider`: AI provider name (e.g., `openai`, `anthropic`, `azure`) * `model`: Model name (e.g., `gpt-4o-mini`, `claude-3-sonnet`) * `method`: Request type (`chat`, `text`, `embedding`, `speech`, `transcription`) * `virtual_key_id`: Virtual key ID * `virtual_key_name`: Virtual key name * `routing_engines_used`: Comma-separated routing engines used ("routing-rule", "governance", "loadbalancing", "model-catalog") * `routing_rule_id`: Routing rule ID that matched the request * `routing_rule_name`: Routing rule name that matched the request * `selected_key_id`: ID of the key that successfully served the request (`null` on final errors) * `selected_key_name`: Name of the key that successfully served the request (`null` on final errors) * `number_of_retries`: Number of retries * `fallback_index`: Fallback index (0 for first attempt, 1 for second attempt, etc.) * custom labels: Custom labels configured in the Bifrost configuration ### Streaming Metrics These metrics capture latency characteristics specific to streaming responses: | Metric | Type | Description | Labels | | -------------------------------------------- | --------- | ----------------------------------------------- | ----------- | | `bifrost_stream_first_token_latency_seconds` | Histogram | Time from request start to first streamed token | Base Labels | | `bifrost_stream_inter_token_latency_seconds` | Histogram | Latency between subsequent streamed tokens | Base Labels | *** ## Monitoring Examples ### Success Rate Monitoring Track the success rate of requests to different providers: ```promql theme={null} # Success rate by provider rate(bifrost_success_requests_total[5m]) / rate(bifrost_upstream_requests_total[5m]) * 100 ``` ### Token Usage Analysis Monitor token consumption across different models: ```promql theme={null} # Input tokens per minute by model increase(bifrost_input_tokens_total[1m]) # Output tokens per minute by model increase(bifrost_output_tokens_total[1m]) # Token efficiency (output/input ratio) rate(bifrost_output_tokens_total[5m]) / rate(bifrost_input_tokens_total[5m]) ``` ### Cost Tracking Monitor spending across providers and models: ```promql theme={null} # Cost per second by provider sum by (provider) (rate(bifrost_cost_total[1m])) # Daily cost estimate sum by (provider) (increase(bifrost_cost_total[1d])) # Cost per request by provider and model sum by (provider, model) (rate(bifrost_cost_total[5m])) / sum by (provider, model) (rate(bifrost_upstream_requests_total[5m])) ``` ### Cache Performance Track cache effectiveness: ```promql theme={null} # Cache hit rate by type rate(bifrost_cache_hits_total[5m]) / rate(bifrost_upstream_requests_total[5m]) * 100 # Direct vs semantic cache hits sum by (cache_type) (rate(bifrost_cache_hits_total[5m])) ``` ### Error Rate Analysis Monitor error patterns: ```promql theme={null} # Error rate by provider rate(bifrost_error_requests_total[5m]) / rate(bifrost_upstream_requests_total[5m]) * 100 # Errors by model sum by (model) (rate(bifrost_error_requests_total[5m])) ``` *** ## Configuration Configure custom Prometheus labels to add dimensions for filtering and analysis: Prometheus Labels 1. **Navigate to Configuration** * Open Bifrost UI at `http://localhost:8080` * Go to **Config** tab 2. **Prometheus Labels** ``` Custom Labels: team, environment, organization, project ``` ```bash theme={null} # Update prometheus labels via API curl -X PATCH http://localhost:8080/config \ -H "Content-Type: application/json" \ -d '{ "client": { "prometheus_labels": ["team", "environment", "organization", "project"] } }' ``` ```json theme={null} { "client": { "prometheus_labels": ["team", "environment", "organization", "project"], "drop_excess_requests": false, "initial_pool_size": 300 } } ``` ### Dynamic Label Injection Add custom label values at runtime using `x-bf-dim-*` headers: ```bash theme={null} # Add custom labels to specific requests curl -X POST http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -H "x-bf-dim-team: engineering" \ -H "x-bf-dim-environment: production" \ -H "x-bf-dim-organization: my-org" \ -H "x-bf-dim-project: my-project" \ -d '{ "model": "gpt-4o-mini", "messages": [{"role": "user", "content": "Hello!"}] }' ``` **Header Format:** * Prefix: `x-bf-dim-` * Label name: Any string after the prefix, except reserved metric labels like `path` and `method` * Value: String value for the label These runtime dimensions are also forwarded to the other observability backends. The same `x-bf-dim-*` values appear in internal logs, OpenTelemetry span attributes, and Maxim tags. Legacy `x-bf-prom-*` headers still work for Prometheus-only behavior, but they are deprecated. When both prefixes provide the same label, `x-bf-dim-*` wins. *** ## Infrastructure Setup ### Development & Testing For local development and testing, use the provided Docker Compose setup: ```bash theme={null} # Navigate to telemetry plugin directory cd plugins/telemetry # Start Prometheus and Grafana docker-compose up -d # Access endpoints # Prometheus: http://localhost:9090 # Grafana: http://localhost:3000 (admin/admin) # Bifrost metrics: http://localhost:8080/metrics ``` **Development Only**: The provided Docker Compose setup is for testing purposes only. Do not use in production without proper security, scaling, and persistence configuration. You can use the Prometheus scraping endpoint to create your own Grafana dashboards. Given below are few examples created using the Docker Compose setup. Grafana Dashboard ### Production Deployment For production environments: 1. **Deploy Prometheus** with proper persistence, retention, and security 2. **Configure scraping** to target your Bifrost instances at `/metrics` 3. **Set up Grafana** with authentication and dashboards 4. **Configure alerts** based on your SLA requirements **Prometheus Scrape Configuration:** ```yaml theme={null} scrape_configs: - job_name: "bifrost-gateway" static_configs: - targets: ["bifrost-instance-1:8080", "bifrost-instance-2:8080"] scrape_interval: 30s metrics_path: /metrics # If Bifrost auth is enabled, add: # basic_auth: # username: '' # password: '' ``` If you have Bifrost authentication enabled (`auth_config`), you must include `basic_auth` in the scrape config with your `admin_username` and `admin_password`. See the [Prometheus docs](/features/observability/prometheus#pull-based-scraping) for details. ### Production Alerting Examples Configure alerts for critical scenarios using the new metrics: **High Error Rate Alert:** ```yaml theme={null} - alert: BifrostHighErrorRate expr: sum by (provider) (rate(bifrost_error_requests_total[5m])) / sum by (provider) (rate(bifrost_upstream_requests_total[5m])) > 0.05 for: 2m labels: severity: warning annotations: summary: "High error rate detected for provider {{ $labels.provider }} ({{ $value | humanizePercentage }})" ``` **High Cost Alert:** ```yaml theme={null} - alert: BifrostHighCosts expr: sum by (provider) (increase(bifrost_cost_total[1d])) > 100 # $100/day threshold for: 10m labels: severity: warning annotations: summary: 'Daily cost for provider {{ $labels.provider }} exceeds $100 ({{ $value | printf "%.2f" }})' ``` **Cache Performance Alert:** ```yaml theme={null} - alert: BifrostLowCacheHitRate expr: sum by (provider) (rate(bifrost_cache_hits_total[15m])) / sum by (provider) (rate(bifrost_upstream_requests_total[15m])) < 0.1 for: 5m labels: severity: info annotations: summary: "Cache hit rate for provider {{ $labels.provider }} below 10% ({{ $value | humanizePercentage }})" ``` *** ## Next Steps * **[Prometheus Documentation](https://prometheus.io/docs/)** - Official Prometheus guides * **[Grafana Setup](https://grafana.com/docs/)** - Dashboard creation and management * **[Tracing](./observability/default)** - Request/response logging for detailed analysis