Prometheus¶
What it is¶
- Dimensional Data Model: Time series identified by metric name and key/value pairs (labels), enabling granular slicing and dicing of system telemetry.
- Pull-Based Metrics Collection: Scrapes HTTP/HTTPS endpoints exporting Prometheus metrics format or OpenTelemetry metrics at configured scrape intervals.
- PromQL Query Engine: Flexible expression language to calculate rates, aggregations, percentiles, and dynamic alerting thresholds.
- Autonomous & Local Architecture: Single-binary operational model without external distributed storage dependencies, ensuring high reliability during network partitions.
- FastMCP 3.1 & Agentic Observability Integration: Native scraping of AI agent runtime metrics, token throughput, model latency, and tool invocation counts.
What problem it solves¶
- Solves the challenge of monitoring dynamic cloud-native infrastructure, Kubernetes clusters, and microservices without relying on complex external database dependencies.
- Eliminates blind spots in AI model inference runtimes by tracking real-time request counts, GPU utilization, and token latencies.
Where it fits in the stack¶
- Operates in the Observability & Monitoring layer of the cloud-native stack.
- Integrates directly with the Grafana LGTM suite, OpenTelemetry Collector, and FastMCP 3.1 agent runtime metrics exporters.
Typical use cases¶
- Kubernetes & Cloud-Native Monitoring: Collecting node, container, pod, and service control plane metrics via kube-state-metrics and cAdvisor.
- AI Model Inference Telemetry: Tracking request throughput, first-token latency, GPU memory utilization, and KV cache hit ratios for vLLM, TGI, and TensorRT-LLM servers.
- Distributed Agent Telemetry: Monitoring agentic workflow executions, sub-task runtimes, and MCP tool failure rates across agent swarms.
- Alerting & Escalation: Evaluating PromQL rules against threshold criteria and routing notifications through Alertmanager to Slack, PagerDuty, or Webhooks.
Strengths¶
- High Performance: Optimized time-series storage engine handling millions of samples per second on modest CPU/RAM footprints.
- Ecosystem Dominance: Near-universal support across cloud platforms, ingress controllers, databases, and LLM inference runtimes.
- Service Discovery: Seamless auto-discovery of targets across Kubernetes, AWS EC2, Consul, Azure, and Google Cloud.
Limitations¶
- Long-Term Storage Limitations: Local TSDB is designed for operational short-to-medium term retention; long-term analytical storage requires integration with systems like Grafana Mimir, Thanos, or Cortex.
- No Event Logs or Traces: Strictly focused on numeric time-series metrics; logs (Loki) and distributed traces (Tempo) require complementary tools in the Grafana LGTM stack.
- Pull Model Edge Challenges: Short-lived transient batch jobs or edge devices require a Pushgateway proxy for metric collection.
When to use it¶
- When building cloud-native observability for Kubernetes infrastructure and microservice architectures.
- When tracking real-time performance indicators and operational metrics for LLM inference servers and AI agent runtimes.
- When you require a proven, lightweight time-series database and alerting framework without mandatory cloud dependencies.
When not to use it¶
- When you require a long-term analytical store for logs or distributed traces (use Loki or Tempo instead).
- When monitoring ephemeral, highly transient batch processes without a Pushgateway.
Getting started¶
+-------------------------------------------------------------------+
| Prometheus Server |
| |
| +-------------------+ +----------------+ +------------+ |
| | Service | | Retrieval | | Local TSDB | |
| | Discovery |===>| (Scrape Engine)|===>| Storage | |
| +-------------------+ +----------------+ +------------+ |
| || |
| \/ |
| +----------------------------------+ |
| | PromQL Engine & Web UI / API | |
| +----------------------------------+ |
+-------------------------------------------------------------------+
^ ||
| Scrapes HTTP /metrics \/
+-----------------------+ +---------------+
| Targets: | | Alertmanager / |
| - vLLM / TensorRT-LLM | | Grafana Cloud |
| - FastMCP 3.1 Agents | +---------------+
| - K8s Nodes / Pods |
+-----------------------+
CLI examples¶
API examples¶
Prometheus scrapes metrics formatted as plain text time-series measurements. The following Python example demonstrates exposing custom AI inference and FastMCP 3.1 agent metrics using the official Prometheus Python client, validated via strict Pydantic v2 models before recording metric observations.
import time
from typing import Dict, Any
from prometheus_client import Counter, Histogram, start_http_server
from pydantic import BaseModel, Field, FieldValidationInfo, field_validator
# ---------------------------------------------------------------------------
# Pydantic v2 Telemetry Schema
# ---------------------------------------------------------------------------
class AgentTelemetryEvent(BaseModel):
agent_id: str = Field(..., description="Unique identifier for the agent instance")
provider: str = Field(..., description="LLM provider (e.g. Anthropic, OpenAI)")
model_name: str = Field(..., description="Model identifier")
mcp_tool: str = Field(..., description="MCP tool invoked by agent")
latency_seconds: float = Field(..., ge=0.0, description="Execution duration in seconds")
tokens_used: int = Field(..., ge=0, description="Total prompt and completion tokens consumed")
status: str = Field(..., description="Execution status: 'success' or 'error'")
@field_validator("status")
@classmethod
def validate_status(cls, v: str) -> str:
allowed = {"success", "error"}
if v.lower() not in allowed:
raise ValueError(f"Status must be one of {allowed}")
return v.lower()
# ---------------------------------------------------------------------------
# Prometheus Metric Declarations
# ---------------------------------------------------------------------------
AGENT_REQUESTS_TOTAL = Counter(
"agent_requests_total",
"Total number of agent tool executions",
["agent_id", "provider", "model_name", "mcp_tool", "status"]
)
AGENT_LATENCY_SECONDS = Histogram(
"agent_latency_seconds",
"Agent tool execution latency in seconds",
["agent_id", "mcp_tool"],
buckets=(0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0, 30.0)
)
AGENT_TOKEN_CONSUMPTION_TOTAL = Counter(
"agent_token_consumption_total",
"Total LLM tokens consumed by agent operations",
["agent_id", "model_name"]
)
def record_agent_execution(event_data: Dict[str, Any]) -> None:
"""Validate telemetry event and observe metrics in Prometheus."""
event = AgentTelemetryEvent.model_validate(event_data)
AGENT_REQUESTS_TOTAL.labels(
agent_id=event.agent_id,
provider=event.provider,
model_name=event.model_name,
mcp_tool=event.mcp_tool,
status=event.status
).inc()
AGENT_LATENCY_SECONDS.labels(
agent_id=event.agent_id,
mcp_tool=event.mcp_tool
).observe(event.latency_seconds)
AGENT_TOKEN_CONSUMPTION_TOTAL.labels(
agent_id=event.agent_id,
model_name=event.model_name
).inc(event.tokens_used)
if __name__ == "__main__":
# Start Prometheus HTTP metrics exporter on port 8000
start_http_server(8000)
print("Prometheus metrics exporter running on http://localhost:8000/metrics")
sample_payload = {
"agent_id": "agent-researcher-01",
"provider": "Anthropic",
"model_name": "claude-5.1-sonnet",
"mcp_tool": "vector_search",
"latency_seconds": 0.42,
"tokens_used": 1250,
"status": "success"
}
record_agent_execution(sample_payload)
print("Recorded metrics sample successfully.")
Related tools / concepts¶
- Grafana Cloud: Directly query Prometheus metrics alongside Loki logs and Tempo traces in unified dashboards.
- OpenTelemetry Collector: Export OTel metric pipelines seamlessly into Prometheus scrapers or remote write receivers.
- Logfire: Operational Python tracing platform with native Prometheus metric bridges.
Sources / references¶
- Prometheus Official Documentation
- CNCF Prometheus Project Page
- Prometheus Python Client GitHub Repository
- PromQL Query Language Documentation
Contribution Metadata¶
- Last reviewed: 2027-01-07
- Confidence: high