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Overview

Fireworks provides a metrics endpoint in Prometheus format, enabling integration with popular observability tools like Prometheus, OpenTelemetry (OTel) Collector, Datadog Agent, and Vector.

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Setting Up Metrics Collection

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Endpoint

The metrics endpoint is as follows. This URL and authorization header can be directly used by services like Grafana Cloud to ingest Fireworks metrics. 
https://api.fireworks.ai/v1/accounts/<account-id>/metrics

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Authentication

Use the Authorization header with your Fireworks API key: 
{
  "Authorization": "Bearer YOUR_API_KEY"
}

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Scrape Interval

We recommend using a 1-minute scrape interval as metrics are updated every 30s.

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Supported Integrations

Fireworks metrics can be collected via OpenTelemetry Collector and exported to various observability platforms including:
  • Prometheus
  • Datadog
  • Grafana
  • New Relic

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Prometheus Integration

To integrate with Prometheus, specify the Fireworks metrics endpoint in your scrape config. You will then need to configure a Prometheus receiver to scrape the endpoint and push it to services like Datadog. 
global:
  scrape_interval: 60s
scrape_configs:
  - job_name: 'fireworks'
    metrics_path: 'v1/accounts/<account-id>/metrics'
    authorization:
      type: "Bearer"
      credentials: "YOUR_API_KEY"
    static_configs:
      - targets: ['api.fireworks.ai']
    scheme: https
For more details on Prometheus configuration, refer to the Prometheus documentation.

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Ratelimits

To ensure service stability and fair usage:
  • Maximum of 6 requests per minute per account
  • Exceeding this limit results in HTTP 429 (Too Many Requests) responses
  • Use a 1-minute scrape interval to stay within limits

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Available Metrics

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Common Labels

All metrics include the following common labels:
  • base_model: The base model identifier (e.g., “accounts/fireworks/models/deepseek-v3”)
  • deployment: Full deployment path (e.g., “accounts/account-name/deployments/deployment-id”)
  • deployment_account: The account name
  • deployment_id: The deployment identifier

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Rate Metrics (per second)

These metrics show activity rates calculated using 1-minute windows:

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Request Rate

  • request_counter_total:sum_by_deployment: Request rate per deployment

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Token Processing Rates

  • tokens_cached_prompt_total:sum_by_deployment: Rate of cached prompt tokens per deployment
  • tokens_prompt_total:sum_by_deployment: Rate of total prompt tokens processed per deployment

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Latency Histogram Metrics

These metrics provide latency distribution data with histogram buckets, calculated using 1-minute windows:

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Generation Latency

  • latency_generation_per_token_ms_bucket:sum_by_deployment: Per-token generation time distribution
  • latency_generation_queue_ms_bucket:sum_by_deployment: Time spent waiting in generation queue

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Request Latency

  • latency_overall_ms_bucket:sum_by_deployment: End-to-end request latency distribution
  • latency_to_first_token_ms_bucket:sum_by_deployment: Time to first token distribution

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Prefill Latency

  • latency_prefill_ms_bucket:sum_by_deployment: Prefill processing time distribution
  • latency_prefill_queue_ms_bucket:sum_by_deployment: Time spent waiting in prefill queue

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Token Distribution Metrics

These histogram metrics show token count distributions per request, calculated using 1-minute windows:
  • tokens_generated_per_request_bucket:sum_by_deployment: Distribution of generated tokens per request
  • tokens_prompt_per_request_bucket:sum_by_deployment: Distribution of prompt tokens per request

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Resource Utilization Metrics

These gauge metrics show average resource usage:
  • generator_kv_blocks_fraction:avg_by_deployment: Average fraction of KV cache blocks in use
  • generator_kv_slots_fraction:avg_by_deployment: Average fraction of KV cache slots in use
  • generator_model_forward_time:avg_by_deployment: Average time spent in model forward pass
  • requests_coordinator_concurrent_count:avg_by_deployment: Average number of concurrent requests
  • prefiller_prompt_cache_ttl:avg_by_deployment: Average prompt cache time-to-live