---
title: "Telemetry"
description: "Comprehensive Prometheus-based monitoring for Bifrost Gateway with custom metrics and labels."
icon: "gauge"
---

## 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-prom-*` 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")
- `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
# 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
# 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
# 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
# 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
# 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:

<Tabs group="config-method">
<Tab title="Web UI">

![Prometheus Labels](../media/ui-prometheus-labels.png)

1. **Navigate to Configuration**
   - Open Bifrost UI at `http://localhost:8080`
   - Go to **Config** tab

2. **Prometheus Labels**
   ```
   Custom Labels: team, environment, organization, project
   ```

</Tab>
<Tab title="API">

```bash
# 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"]
    }
  }'
```

</Tab>
<Tab title="config.json">

```json
{
  "client": {
    "prometheus_labels": ["team", "environment", "organization", "project"],
    "drop_excess_requests": false,
    "initial_pool_size": 300
  }
}
```

</Tab>
</Tabs>

### Dynamic Label Injection

Add custom label values at runtime using `x-bf-prom-*` headers:

```bash
# Add custom labels to specific requests
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-bf-prom-team: engineering" \
  -H "x-bf-prom-environment: production" \
  -H "x-bf-prom-organization: my-org" \
  -H "x-bf-prom-project: my-project" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'
```

**Header Format:**
- Prefix: `x-bf-prom-`
- Label name: Any string after the prefix
- Value: String value for the label

---

## Infrastructure Setup

### Development & Testing

For local development and testing, use the provided Docker Compose setup:

```bash
# 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
```

<Warning>
**Development Only**: The provided Docker Compose setup is for testing purposes only. Do not use in production without proper security, scaling, and persistence configuration.
</Warning>

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](../media/ui-grafana-dashboard.png)

### 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
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: '<admin_username>'
    #   password: '<admin_password>'
```

<Info>
  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.
</Info>

### Production Alerting Examples

Configure alerts for critical scenarios using the new metrics:

**High Error Rate Alert:**
```yaml
- 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
- 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
- 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