Telemetry

Overview

DeepIntShield 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.

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Default Metrics

HTTP Transport Metrics

These metrics track all incoming HTTP requests to DeepIntShield:

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 DeepIntShield 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, reason, 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: Selected key ID
• selected_key_name: Selected key name
• 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 DeepIntShield 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

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Monitoring Examples

Success Rate Monitoring

Track the success rate of requests to different providers:

# 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:

# 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:

# 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:

# 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:

# 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]))

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Configuration

Configure custom Prometheus labels to add dimensions for filtering and analysis:

Web UI

1. Navigate to Configuration

   • Open DeepIntShield UI at http://localhost:8080
   • Go to Config tab

2. Prometheus Labels

   Custom Labels: team, environment, organization, project

API

# 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"]
    }
  }'

config.json

{
  "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-prom-* headers:

# 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

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Infrastructure Setup

Development & Testing

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

# 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)
# DeepIntShield metrics: http://localhost:8080/metrics

Caution

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.

Production Deployment

For production environments:

1. Deploy Prometheus with proper persistence, retention, and security
2. Configure scraping to target your DeepIntShield instances at /metrics
3. Set up Grafana with authentication and dashboards
4. Configure alerts based on your SLA requirements

Prometheus Scrape Configuration:

scrape_configs:
  - job_name: "deepintshield-gateway"
    static_configs:
      - targets: ["deepintshield-instance-1:8080", "deepintshield-instance-2:8080"]
    scrape_interval: 30s
    metrics_path: /metrics
    # If DeepIntShield auth is enabled, add:
    # basic_auth:
    #   username: '<admin_username>'
    #   password: '<admin_password>'

Note

If you have DeepIntShield authentication enabled (auth_config), you must include basic_auth in the scrape config with your admin_username and admin_password. See the Prometheus docs for details.

Production Alerting Examples

Configure alerts for critical scenarios using the new metrics:

High Error Rate Alert:

- alert: DeepIntShieldHighErrorRate
  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:

- alert: DeepIntShieldHighCosts
  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:

- alert: DeepIntShieldLowCacheHitRate
  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 }})"

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Next Steps

• Prometheus Documentation - Official Prometheus guides
• Grafana Setup - Dashboard creation and management
• Tracing - Request/response logging for detailed analysis