Prometheus vs InfluxDB 2026: Time-Series Monitoring

Prometheus pulls metrics from targets. InfluxDB receives pushed data. Prometheus is built for Kubernetes monitoring. InfluxDB is a general-purpose time-series database for any data.

Architecture

Data collection

Prometheus (pull)

# prometheus.yml
scrape_configs:
  - job_name: 'kubernetes-pods'
    kubernetes_sd_configs:
      - role: pod
    relabel_configs:
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true

  - job_name: 'node-exporter'
    static_configs:
      - targets: ['node1:9100', 'node2:9100']

Prometheus discovers and scrapes targets. The target exposes metrics at /metrics. Prometheus controls the collection schedule.

InfluxDB (push)

# Write data via line protocol
curl -X POST "http://influxdb:8086/api/v2/write?bucket=mydb" \
  -H "Authorization: Token ${TOKEN}" \
  -d "cpu,host=server1,region=eu usage=72.5 $(date +%s)000000000"

# Or via Telegraf agent
# telegraf.conf
[[inputs.cpu]]
  percpu = true
[[outputs.influxdb_v2]]
  urls = ["http://influxdb:8086"]
  token = "${TOKEN}"
  organization = "myorg"
  bucket = "metrics"

InfluxDB relies on agents (Telegraf) or applications pushing data. More flexible for non-infrastructure data (IoT sensors, business metrics, custom events).

Query languages

PromQL

# CPU usage above 80% for 5 minutes
avg by (instance) (rate(node_cpu_seconds_total{mode!="idle"}[5m])) > 0.8

# 99th percentile request latency
histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m]))

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

InfluxDB SQL

-- CPU usage last hour
SELECT mean(usage) FROM cpu
WHERE time > now() - 1h
GROUP BY host, time(5m)

-- 99th percentile latency
SELECT percentile(duration, 99) FROM requests
WHERE time > now() - 5m
GROUP BY service

-- Error rate
SELECT count(status) FROM requests
WHERE status >= 500 AND time > now() - 5m

PromQL is purpose-built for metrics — powerful but has a learning curve. InfluxDB SQL is familiar to anyone who knows SQL.

Kubernetes monitoring

Prometheus dominates Kubernetes monitoring:

# Install kube-prometheus-stack (Prometheus + Grafana + alerting)
helm install monitoring prometheus-community/kube-prometheus-stack

# Instantly get:
# - Node metrics (CPU, memory, disk, network)
# - Pod metrics (resource usage, restarts)
# - Kubernetes API metrics
# - etcd metrics
# - CoreDNS metrics
# - 100+ pre-built Grafana dashboards
# - 100+ pre-configured alert rules

InfluxDB can monitor Kubernetes via Telegraf, but lacks the native service discovery and pre-built ecosystem.

Long-term storage

Prometheus alone is not designed for long-term storage. You need Thanos, Cortex, or Mimir for multi-month retention. InfluxDB handles long-term storage natively.

Decision guide

Choose Prometheus when:

• Kubernetes monitoring — native service discovery, kube-prometheus-stack
• Pull-based model — you control what gets scraped and when
• PromQL is acceptable (your team will learn it)
• Alerting with Alertmanager is needed
• Grafana dashboards (Prometheus is the #1 data source)
• CNCF ecosystem — every cloud-native tool exposes Prometheus metrics

Choose InfluxDB when:

• IoT / sensor data — high-cardinality, high-write-volume time-series
• Push-based collection — devices push data, not scraped
• SQL familiarity — team prefers SQL over PromQL
• Business metrics — custom events, financial data, application KPIs
• Long-term storage built-in without external components
• Non-Kubernetes environments where Prometheus service discovery does not apply

Related

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