Prometheus vs InfluxDB 2026: Performance & Features
Prometheus is a pull-based time-series database optimized for metrics monitoring with built-in alerting, while InfluxDB is a push-based TSDB designed for high-write-throughput scenarios with superior data compression. Prometheus excels in Kubernetes/container environments; InfluxDB handles IoT and event streaming workloads better.
Prometheus
Open-source pull-based time-series database for metrics monitoring and alerting.
Kubernetes operators, DevOps teams, microservices monitoring, cloud-native infrastructure
InfluxDB
High-write-throughput open-source and commercial time-series database with Flux query language.
IoT platforms, real-time analytics, event streaming, high-cardinality metrics, data-heavy applications
Quick Answer
AI SummaryPrometheus is a pull-based time-series database optimized for metrics monitoring with built-in alerting, while InfluxDB is a push-based TSDB designed for high-write-throughput scenarios with superior data compression. Prometheus excels in Kubernetes/container environments; InfluxDB handles IoT and event streaming workloads better.
Our Verdict
AI-assistedChoose Prometheus if you're monitoring cloud-native infrastructure, Kubernetes clusters, or microservices—its pull-based architecture, built-in alerting, and PromQL make it the industry standard for observability. Choose InfluxDB if you need to ingest massive volumes of metrics (IoT sensors, real-time analytics, events), require extreme compression, or prefer push-based collection with Flux's functional query language.
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Choose Prometheus if
Best pickKubernetes operators, DevOps teams, microservices monitoring, cloud-native infrastructure
Choose InfluxDB if
IoT platforms, real-time analytics, event streaming, high-cardinality metrics, data-heavy applications
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Key Differences at a Glance
- Data Collection Model:Pull-based (scrapes targets) vs Push-based (receives data)
- Write Throughput:✓ InfluxDB wins(~10M+ data points/sec vs ~1M data points/sec)
- Query Language:✓ Prometheus wins(PromQL (specialized for time-series) vs InfluxQL and Flux (general-purpose))
Key Facts & Figures
49 numeric metrics compared
| Metric | Prometheus | InfluxDB | Ratio |
|---|---|---|---|
| GitHub Stars (Community Adoption)(stars) | 54,000+ | — | — |
| Data Sources Supported(integrations) | Remote storage endpoints only (~5 types) | — | — |
| Memory Usage (Typical Instance)(MB) | 50-150 | — | — |
| Query Language Learning Curve(weeks) | 1-2 (PromQL specific but consistent) | — | — |
| Time to First Dashboard(hours) | 15-30 (local metrics immediately available) | — | — |
| Default Local Data Retention(days) | 15 days | — | — |
| Minimum RAM Requirements (Production)(GB) | 2 GB | — | — |
| Query Latency (1 billion data points)(milliseconds) | 150-300 ms | — | — |
| Number of Required Components(components) | 1 (single binary) | — | — |
| Community Stars on GitHub(stars) | 25,000+ stars | — | — |
| Setup Time (Kubernetes)(minutes) | 10-15 minutes | — | — |
| Memory Footprint (Minimal Setup)(MB) | ~50 MB | — | — |
| Community Dashboard Templates(templates) | Limited (basic graphs) | — | — |
| Setup Time (Basic Production)(minutes) | 30-60 minutes | — | — |
| Query Language Complexity(learning hours required) | 20-40 hours (PromQL) | — | — |
| Write Throughput(metrics/second) | ~1,000,000 | ~10-100,000,000 | |
| Maximum Active Time Series(series) | 10 million (memory limited) | Unlimited high-cardinality | — |
| Community Adoption Rate(% of monitoring stacks) | 78% | 22% | |
| Typical Memory Per Instance(GB) | 2-8 GB | 4-16 GB | |
| Starting Monthly Cost per Host(USD) | $0 (open-source) | — | — |
| Setup Time to Production(minutes) | 5 hours | — | — |
| Data Retention Period(days) | 15 days (configurable to 365+) | — | — |
| Pre-built Integrations(count) | 800+ community exporters | — | — |
| Metric Cardinality Ceiling(millions) | 10+ (practical limit before degradation) | — | — |
| Memory Footprint (Typical Setup)(MB) | 50-150 (single instance) | — | — |
| Compression Ratio(ratio) | 1:1 (uncompressed) | — | — |
| Query Latency at 1B Samples(milliseconds) | 50-100ms | — | — |
| Memory Per 1M Series(GB) | 8-12 GB | — | — |
| GitHub Stars(stars) | 50,000+ | — | — |
| Official Integrations(count) | 600+ | — | — |
| PromQL Compatibility(percent) | 100% | — | — |
| Typical Setup Time(hours) | 1-2 hours | — | — |
| Storage Cost for 1M metrics/sec (1 year)(USD) | $15,000-25,000 | — | — |
| Time-Series Retention (Default)(days) | 15 days | — | — |
| Signal Types Supported(count) | Metrics only | — | — |
| Supported Backends/Exporters(count) | 500+ exporters | — | — |
| Minimum Setup Time(minutes) | 5-10 minutes (single binary) | — | — |
| Programming Language Instrumentation(languages) | Official client libraries for 10 languages | — | — |
| Alert Evaluation Throughput(rules/second) | 50 alert rules evaluated per second | — | — |
| Community Adoption (GitHub Stars)(stars) | 54,000+ stars | — | — |
| Monthly Cost (10 hosts, standard tier)(USD) | $0 | — | — |
| Default Data Retention(days) | 15 days | — | — |
| Agent Installation Time(minutes) | 120-480 minutes (with config) | — | — |
| Setup Complexity (1-10 scale)(complexity score) | 7/10 - YAML config, service discovery setup | — | — |
| Write Throughput (Peak)(million data points/sec) | ~1M points/sec | ~10M+ points/sec | |
| Data Compression Ratio(ratio) | 2-4 bytes | 0.5-1.5 bytes | |
| Default Retention Period(days) | 15 days | 30 days | |
| Community Exporters/Integrations(count (approximate)) | 1,000+ exporters | 200+ integrations | |
| Startup Time (Single Node)(seconds) | 2-5 seconds | 5-10 seconds |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- Pull-based (scrapes targets)Data Collection ModelPush-based (receives data)
- ~1M data points/secWrite Throughput~10M+ data points/sec(winner)
- PromQL (specialized for time-series)(winner)Query LanguageInfluxQL and Flux (general-purpose)
- 2-4 bytes per sampleStorage Efficiency0.5-1.5 bytes per sample(winner)
- Yes (Alertmanager included)(winner)Built-in AlertingLimited (requires Tasks/external setup)
- Native support, service discovery(winner)Kubernetes IntegrationThird-party integrations required
- 15 daysRetention Window (Default)30 days(winner)
- Data Collection Model
Prometheus
Pull-based (scrapes targets)
InfluxDB
Push-based (receives data)
- Write Throughput
Prometheus
~1M data points/sec
InfluxDB
~10M+ data points/sec(winner)
- Query Language
Prometheus
PromQL (specialized for time-series)(winner)
InfluxDB
InfluxQL and Flux (general-purpose)
- Storage Efficiency
Prometheus
2-4 bytes per sample
InfluxDB
0.5-1.5 bytes per sample(winner)
- Built-in Alerting
Prometheus
Yes (Alertmanager included)(winner)
InfluxDB
Limited (requires Tasks/external setup)
- Kubernetes Integration
Prometheus
Native support, service discovery(winner)
InfluxDB
Third-party integrations required
- Retention Window (Default)
Prometheus
15 days
InfluxDB
30 days(winner)
Full Comparison
| Attribute | Prometheus | InfluxDB |
|---|---|---|
| GitHub Stars (Community Adoption)(stars) | 54,000+ | — |
| Community Adoption Rate(% of monitoring stacks) | 78%(winner) | 22% |
| Community Adoption (GitHub Stars)(stars) | 54,000+ stars | — |
| Data Sources Supported(integrations) | Remote storage endpoints only (~5 types) | — |
| Built-in Alerting Capabilities(null) | Advanced (AlertManager with routing, grouping, silencing) | — |
| Multi-Cluster Query Support | No (federation workaround available) | — |
| Query Language | PromQL (specialized time-series DSL) | InfluxQL and Flux (functional language) |
| Built-in Alerting | Yes (AlertManager) | No (Enterprise only) |
Show 8 more attributesData Retention Period(days) 15 days (configurable to 365+) — Pre-built Integrations(count) 800+ community exporters — APM Distributed Tracing(languages supported) Requires third-party (Jaeger, Zipkin) — Signal Types Supported(count) Metrics only — Distributed Trace Support(null) Limited (Exemplars linking only) — Default Data Retention(days) 15 days — Kubernetes Integration Native (service discovery, relabeling) Third-party via Telegraf Built-in Alerting System Yes (Alertmanager included) Limited (requires Tasks setup) | ||
| Memory Usage (Typical Instance)(MB) | 50-150 | — |
| Minimum RAM Requirements (Production)(GB) | 2 GB | — |
| Query Latency (1 billion data points)(milliseconds) | 150-300 ms | — |
| Memory Footprint (Minimal Setup)(MB) | ~50 MB | — |
| Write Throughput(metrics/second) | ~1,000,000 | ~10-100,000,000(winner) |
Show 7 more attributesTypical Memory Per Instance(GB) 2-8 GB 4-16 GB Metric Cardinality Ceiling(millions) 10+ (practical limit before degradation) — Memory Footprint (Typical Setup)(MB) 50-150 (single instance) — Query Latency at 1B Samples(milliseconds) 50-100ms — Alert Evaluation Throughput(rules/second) 50 alert rules evaluated per second — Write Throughput (Peak)(million data points/sec) ~1M points/sec ~10M+ points/sec Startup Time (Single Node)(seconds) 2-5 seconds 5-10 seconds | ||
| Query Language Learning Curve(weeks) | 1-2 (PromQL specific but consistent) | — |
| Typical Setup Time(hours) | 1-2 hours | — |
| Standalone Operation | Yes—fully self-contained with TSDB | — |
| Time to First Dashboard(hours) | 15-30 (local metrics immediately available) | — |
| Default Local Data Retention(days) | 15 days | — |
| Compression Ratio(ratio) | 1:1 (uncompressed) | — |
| Default Retention Period(days) | 15 days | 30 days(winner) |
| Number of Required Components(components) | 1 (single binary) | — |
| Setup Time (Kubernetes)(minutes) | 10-15 minutes | — |
| Object Storage Cost (1 TB/month)(USD) | N/A (local storage only) | — |
| Community Stars on GitHub(stars) | 25,000+ stars | — |
| Supported Data Sources(count) | Scrape-based (any Prometheus-compatible exporter) | — |
| Community Dashboard Templates(templates) | Limited (basic graphs) | — |
| Official Integrations(count) | 600+ | — |
| Community Exporters/Integrations(count (approximate)) | 1,000+ exporters(winner) | 200+ integrations |
| Commercial Support Cost(USD/month) | Free (no commercial tier) | — |
| Setup Time (Basic Production)(minutes) | 30-60 minutes | — |
| Setup Time to Production(minutes) | 5 hours | — |
| Agent Installation Time(minutes) | 120-480 minutes (with config) | — |
| Query Language Complexity(learning hours required) | 20-40 hours (PromQL) | — |
| Data Query Language | PromQL (Prometheus Query Language) | — |
| Built-in Alerting Rules(feature) | Yes (Alertmanager with 50+ integrations) | — |
| Maximum Active Time Series(series) | 10 million (memory limited) | Unlimited high-cardinality |
| CNCF Project Status(status) | Graduated | Archived (moved to InfluxData) |
| Starting Monthly Cost per Host(USD) | $0 (open-source) | — |
| Monthly Cost (10 hosts, standard tier)(USD) | $0 | — |
| Customer Support Availability | Community forums (async) | — |
| Enterprise Support Availability | Community forums and GitHub issues | — |
| Memory Per 1M Series(GB) | 8-12 GB | — |
| GitHub Stars(stars) | 50,000+ | — |
| PromQL Compatibility(percent) | 100% | — |
| Storage Cost for 1M metrics/sec (1 year)(USD) | $15,000-25,000 | — |
| Time-Series Retention (Default)(days) | 15 days | — |
| Supported Backends/Exporters(count) | 500+ exporters | — |
| Minimum Setup Time(minutes) | 5-10 minutes (single binary) | — |
| Programming Language Instrumentation(languages) | Official client libraries for 10 languages | — |
| Uptime SLA(percent) | Self-hosted (user responsibility) | — |
| Setup Complexity (1-10 scale)(complexity score) | 7/10 - YAML config, service discovery setup | — |
| Data Compression Ratio(ratio) | 2-4 bytes(winner) | 0.5-1.5 bytes |
PrometheusShow 8 more attributes
Show 7 more attributes
Pros & Cons
10 pros·6 cons across both
Prometheus
Pros
- PromQL query language optimized for time-series data with powerful aggregation functions
- Built-in Alertmanager for rule-based alerting without external dependencies
- Native Kubernetes service discovery (DNS-SD, Consul, etcd, cloud provider APIs)
- Excellent community ecosystem with 1,000+ pre-built exporters for integrations
- Efficient for cardinality management with label-based metrics model
Cons
- Pull-based model requires network connectivity to targets; poor for ephemeral services
- Limited to ~15-day default retention; long-term storage requires external solutions (Thanos, Cortex)
- Single-node scaling limitations; horizontal scaling requires third-party tools
InfluxDB
Pros
- 10x higher write throughput (10M+ points/sec vs Prometheus's 1M/sec)
- Superior data compression (0.5-1.5 bytes/sample vs Prometheus's 2-4 bytes)
- Push-based ingestion ideal for distributed IoT sensors, mobile apps, and event streams
- Flux language enables functional queries, variable interpolation, and complex transformations
- 30-day default retention with native downsampling and continuous aggregates
Cons
- Alerting is cumbersome; requires InfluxDB Tasks (Flux-based) or external tools like PagerDuty
- Kubernetes integration is third-party; no native service discovery
- Steeper learning curve for teams familiar with SQL; PromQL users face context switching
Frequently Asked Questions
5 questions
Prometheus is the de facto standard for Kubernetes. It has native service discovery that automatically scrapes metrics from pods and nodes, built-in alerting via Alertmanager, and is deeply integrated into the CNCF ecosystem. InfluxDB requires additional tooling (Telegraf) and doesn't have native Kubernetes discovery, making it less ideal for container orchestration scenarios.
Resources & Learn More
Curated sources to dive deeper
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