Our Pick Prometheus — Prometheus is the standard for Kubernetes and cloud-native monitoring. InfluxDB is the better choice for high-cardinality IoT/sensor data, longer retention requirements, and teams who need SQL-like queries or a fully managed time series cloud. The decision is usually driven by ecosystem: Kubernetes shops use Prometheus; IoT/industrial shops use InfluxDB.
Prometheus vs InfluxDB

import ComparisonTable from ’../../components/ComparisonTable.astro’;

Prometheus and InfluxDB are both time series databases designed for metrics, but they have fundamentally different designs, ecosystems, and strengths. Prometheus is a pull-based system built for cloud-native monitoring with excellent Kubernetes integration. InfluxDB is a push-based system built for high-volume time series data with strong IoT and analytics capabilities.

Quick Verdict

Choose Prometheus if: Kubernetes monitoring, cloud-native infrastructure, Grafana ecosystem, or you want the industry-standard open-source observability stack.

Choose InfluxDB if: IoT sensor data, industrial time series, need longer data retention at scale, SQL-familiar team, or want a managed time series cloud (InfluxDB Cloud).

Feature Comparison

<ComparisonTable headers={[“Feature”, “Prometheus”, “InfluxDB”]} rows={[ [“Data model”, “Metric + labels (key-value)”, “Measurement + tags + fields”], [“Collection model”, “Pull (scrapes endpoints)”, “Push (write API)”], [“Query language”, “PromQL”, “Flux / SQL (InfluxDB v3)”], [“Storage”, “Local (TSDB)”, “IOx columnar (v3) / TSM (v2)”], [“Retention”, “Limited (disk-based, default 15d)”, “Configurable (unlimited)”], [“Alerting”, “Alertmanager (separate)”, “Built-in alerting”], [“Kubernetes native”, “Excellent”, “Manual setup”], [“Cardinality limit”, “~10M series (practical)”, “Higher cardinality support”], [“Managed cloud”, “Grafana Cloud (Mimir)”, “InfluxDB Cloud”], [“Long-term storage”, “Thanos/Cortex/Mimir”, “Built-in”], [“High availability”, “External (Thanos)”, “Built-in clustering”], [“Ecosystem”, “CNCF, Grafana, huge”, “InfluxData ecosystem”], [“IoT suitability”, “Limited”, “Excellent”], [“License”, “Apache 2.0”, “MIT (v2), Source Available (v3)”], ]} />

Prometheus Architecture

Prometheus is a pull-based monitoring system — it actively scrapes HTTP endpoints that expose metrics:

How Prometheus works:

Target (your app)     Prometheus          Grafana
/metrics endpoint  → scrapes every 15s → queries PromQL → Dashboard
                  
                      Alertmanager ← evaluation rules
                          ↓
                      PagerDuty / Slack / email

Instrumentation (Python):

from prometheus_client import Counter, Histogram, Gauge, start_http_server
import time

# Define metrics
REQUEST_COUNT = Counter(
    'http_requests_total',
    'Total HTTP requests',
    ['method', 'endpoint', 'status_code']
)

REQUEST_LATENCY = Histogram(
    'http_request_duration_seconds',
    'HTTP request latency',
    ['method', 'endpoint'],
    buckets=[.005, .01, .025, .05, .1, .25, .5, 1, 2.5, 5, 10]
)

ACTIVE_CONNECTIONS = Gauge(
    'active_connections',
    'Number of active connections'
)

# Use in your application
def handle_request(method, endpoint):
    start = time.time()
    ACTIVE_CONNECTIONS.inc()
    
    try:
        response = process_request(method, endpoint)
        REQUEST_COUNT.labels(
            method=method, 
            endpoint=endpoint, 
            status_code=response.status_code
        ).inc()
        return response
    finally:
        ACTIVE_CONNECTIONS.dec()
        REQUEST_LATENCY.labels(
            method=method, 
            endpoint=endpoint
        ).observe(time.time() - start)

# Expose /metrics endpoint (port 8000)
start_http_server(8000)

Prometheus configuration:

# prometheus.yml
global:
  scrape_interval: 15s
  evaluation_interval: 15s

rule_files:
  - "alert_rules.yml"

alerting:
  alertmanagers:
    - static_configs:
        - targets: ['alertmanager:9093']

scrape_configs:
  # Scrape Prometheus itself
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']

  # Scrape your application
  - job_name: 'my-app'
    static_configs:
      - targets: ['app:8000']
    
  # Kubernetes pod discovery
  - job_name: 'kubernetes-pods'
    kubernetes_sd_configs:
      - role: pod
    relabel_configs:
      # Only scrape pods with annotation
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true
      # Use custom port if specified
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_port]
        action: replace
        target_label: __address__
        regex: (.+)
        replacement: ${1}

  # Node exporter (host metrics)
  - job_name: 'node-exporter'
    static_configs:
      - targets: ['node-exporter:9100']

PromQL queries:

# Request rate (per second, last 5 minutes)
rate(http_requests_total[5m])

# Error rate
rate(http_requests_total{status_code=~"5.."}[5m])
  / rate(http_requests_total[5m])

# P99 latency
histogram_quantile(0.99, 
  rate(http_request_duration_seconds_bucket[5m])
)

# CPU usage by pod
100 - (avg by (pod) (rate(container_cpu_usage_seconds_total[5m])) * 100)

# Memory usage > 80%
container_memory_usage_bytes / container_spec_memory_limit_bytes > 0.8

Alerting rules:

# alert_rules.yml
groups:
  - name: application
    rules:
      - alert: HighErrorRate
        expr: |
          rate(http_requests_total{status_code=~"5.."}[5m])
          / rate(http_requests_total[5m]) > 0.05
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High error rate on {{ $labels.job }}"
          description: "Error rate is {{ $value | humanizePercentage }}"

      - alert: HighLatency
        expr: |
          histogram_quantile(0.99, 
            rate(http_request_duration_seconds_bucket[5m])
          ) > 1.0
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "P99 latency above 1 second"

InfluxDB Architecture

InfluxDB uses a push model — applications write data to InfluxDB’s API:

InfluxDB data model:

Measurement: http_requests
Tags (indexed, for filtering): 
  method=GET, endpoint=/api/users, status_code=200
Fields (numeric data):
  count=1, duration_ms=45.2
Timestamp: 2025-01-15T10:30:00Z

Line protocol (wire format):
http_requests,method=GET,endpoint=/api/users,status_code=200 count=1i,duration_ms=45.2 1705312200000000000

Writing data (Python):

from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import SYNCHRONOUS
import time

client = InfluxDBClient(
    url="http://influxdb:8086",
    token="your-token",
    org="my-org"
)

write_api = client.write_api(write_options=SYNCHRONOUS)

def record_request(method, endpoint, status_code, duration_ms):
    point = (
        Point("http_requests")
        .tag("method", method)
        .tag("endpoint", endpoint)
        .tag("status_code", str(status_code))
        .field("count", 1)
        .field("duration_ms", duration_ms)
    )
    write_api.write(
        bucket="metrics",
        org="my-org",
        record=point
    )

# IoT sensor data
def record_sensor(sensor_id, location, temperature, humidity, pressure):
    point = (
        Point("environment")
        .tag("sensor_id", sensor_id)
        .tag("location", location)
        .field("temperature", temperature)
        .field("humidity", humidity)
        .field("pressure", pressure)
    )
    write_api.write(bucket="sensors", org="my-org", record=point)

Flux queries (InfluxDB v2):

// Error rate over last 5 minutes
from(bucket: "metrics")
  |> range(start: -5m)
  |> filter(fn: (r) => r._measurement == "http_requests")
  |> filter(fn: (r) => r.status_code =~ /5\d\d/)
  |> count()
  |> map(fn: (r) => ({r with _value: float(v: r._value) / total}))

// Mean temperature by location (last hour)
from(bucket: "sensors")
  |> range(start: -1h)
  |> filter(fn: (r) => r._measurement == "environment")
  |> filter(fn: (r) => r._field == "temperature")
  |> group(columns: ["location"])
  |> mean()

SQL queries (InfluxDB v3):

-- P99 latency by endpoint
SELECT 
  endpoint,
  PERCENTILE_CONT(0.99) WITHIN GROUP (ORDER BY duration_ms) as p99_latency
FROM http_requests
WHERE time >= now() - INTERVAL '5 minutes'
  AND status_code < 500
GROUP BY endpoint
ORDER BY p99_latency DESC;

-- Sensor data downsampling for long-term storage
SELECT 
  DATE_BIN('1 hour', time, '1970-01-01') as hour,
  location,
  AVG(temperature) as avg_temp,
  MIN(temperature) as min_temp,
  MAX(temperature) as max_temp
FROM environment
WHERE time >= '2025-01-01'
GROUP BY hour, location
ORDER BY hour;

Long-Term Storage Solutions

Prometheus long-term storage:

# Thanos — adds long-term storage to Prometheus
# Thanos sidecar uploads Prometheus blocks to object storage

thanos-sidecar:
  image: thanosio/thanos:v0.34.0
  args:
    - sidecar
    - --prometheus.url=http://prometheus:9090
    - --objstore.config=$(OBJSTORE_CONFIG)
    # OBJSTORE_CONFIG points to S3/GCS/Azure bucket config

thanos-query:
  # Query across multiple Prometheus instances and object storage
  - --store=thanos-sidecar:10091
  - --store=thanos-store:10091  # Historical data from object storage

InfluxDB handles this natively — configure retention periods per bucket.

When to Choose Each

Choose Prometheus:

  • Kubernetes and cloud-native infrastructure monitoring
  • Integration with Grafana, AlertManager, and CNCF ecosystem
  • Developer/SRE teams familiar with PromQL
  • Application metrics (RED method: Rate, Error, Duration)
  • When you want operator flexibility and open source control

Choose InfluxDB:

  • IoT and industrial sensor data (high write volume, irregular intervals)
  • Need longer built-in retention without external solutions
  • Teams who prefer SQL over PromQL (InfluxDB v3)
  • Time series analytics beyond monitoring (business metrics, financial data)
  • Managed cloud with less operational burden (InfluxDB Cloud)

Bottom Line

For cloud-native and Kubernetes environments, Prometheus is the default choice — it’s the CNCF standard with the widest ecosystem support and Grafana integration. For IoT, industrial, or use cases needing long-term time series storage with SQL queries, InfluxDB is often the better fit. Many mature observability stacks use both: Prometheus for infrastructure and application metrics (short retention, fast queries), InfluxDB for business metrics and long-term trend storage. The Prometheus + Grafana + Alertmanager stack has become so standard in cloud-native that choosing anything else requires justification.