Decision comparison
New Relic vs Prometheus
New Relic delivers a comprehensive, fully managed observability platform with AI-powered features and 780+ integrations, while Prometheus provides a battle-tested, free open-source metrics monitoring system that gives teams complete control over their monitoring infrastructure. The right choice depends on whether you prioritize operational simplicity or cost control and customization.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
These are different kinds of product — Observability Platform and Metrics & Dashboards.
Quick Comparison
| Decision factor | New Relic | Prometheus |
|---|---|---|
| Pricing Model | Free tier available, paid plans start at $19/mo per host, additional costs based on usage and features | Free and open source |
| Best For | Teams needing a fully managed, all-in-one observability platform with AI-powered insights and 780+ integrations | Cloud-native teams running Kubernetes who need a proven, flexible metrics monitoring system with full control |
| Deployment Model | Fully managed SaaS platform with no infrastructure to maintain; enterprise tier offers FedRAMP and HIPAA eligibility | Self-hosted on your own infrastructure; servers operate independently using local storage with Go-based static binaries |
| Data Collection | Agent-based instrumentation across applications, infrastructure, logs, and browser with 780+ quickstart integrations | HTTP pull model scraping metrics endpoints; supports push via Pushgateway and automatic Kubernetes service discovery |
| Query Language | NRQL (New Relic Query Language) for querying telemetry data across all signal types in a unified data store | PromQL, a purpose-built functional query language for dimensional time series data, widely adopted as an industry standard |
| Learning Curve | Moderate learning curve with guided setup wizards; users note the platform can feel complex for non-technical staff | Steep initial learning curve for PromQL and configuration; users report difficulty getting started without prior experience |
New Relic
- Pricing Model:
- Free tier available, paid plans start at $19/mo per host, additional costs based on usage and features
- Best For:
- Teams needing a fully managed, all-in-one observability platform with AI-powered insights and 780+ integrations
- Deployment Model:
- Fully managed SaaS platform with no infrastructure to maintain; enterprise tier offers FedRAMP and HIPAA eligibility
- Data Collection:
- Agent-based instrumentation across applications, infrastructure, logs, and browser with 780+ quickstart integrations
- Query Language:
- NRQL (New Relic Query Language) for querying telemetry data across all signal types in a unified data store
- Learning Curve:
- Moderate learning curve with guided setup wizards; users note the platform can feel complex for non-technical staff
Prometheus
- Pricing Model:
- Free and open source
- Best For:
- Cloud-native teams running Kubernetes who need a proven, flexible metrics monitoring system with full control
- Deployment Model:
- Self-hosted on your own infrastructure; servers operate independently using local storage with Go-based static binaries
- Data Collection:
- HTTP pull model scraping metrics endpoints; supports push via Pushgateway and automatic Kubernetes service discovery
- Query Language:
- PromQL, a purpose-built functional query language for dimensional time series data, widely adopted as an industry standard
- Learning Curve:
- Steep initial learning curve for PromQL and configuration; users report difficulty getting started without prior experience
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | New Relic | Prometheus |
|---|---|---|
| GitHub commits, 90d(Developer adoption) | 352 | Not available |
| GitHub stars(Developer adoption) | 10 | Not available |
| Search interest(Market interest) | 4 | 0 |
| Hacker News mentions, 90d(Community interest) | 1 | 0 |
| npm weekly downloads(Developer adoption) | 847.6k | Not available |
| Product Hunt comments(Community interest) | 1 | 1 |
| Product Hunt reviews(Community interest) | 0 | 0 |
| Product Hunt votes(Community interest) | 16 | 9 |
| PyPI weekly downloads(Developer adoption) | 929.1k | 30.7M |
| Stack Overflow questions(Community interest) | 1.5k | 7.0k |
| Docker Hub pulls(Product adoption) | Not available | 2.0B |
| GitHub commits, 90d(Product adoption) | Not available | 752 |
| GitHub stars(Product adoption) | Not available | 66,000+ |
| npm weekly downloads(Ecosystem adoption) | Not available | 6.5M |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
New Relic
September 21, 2026Package vulnerabilities
npm · newrelic@14.5.0 · PyPI · newrelic@13.5.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Prometheus
Package vulnerabilities
npm · prom-client@15.1.3 · PyPI · prometheus-client@0.26.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Feature Comparison
| Feature | New Relic | Prometheus |
|---|---|---|
| Core Monitoring | ||
| Application Performance Monitoring | Full APM 360 with code-level diagnostics, distributed tracing, error tracking, and code profiling across cloud and datacenter environments | Metrics-focused monitoring through instrumentation libraries; no built-in APM but integrates with tracing tools like Jaeger for distributed tracing |
| Infrastructure Monitoring | Comprehensive infrastructure monitoring covering AWS, Azure, GCP, hosts, Kubernetes, network, serverless, and database monitoring in one view | Infrastructure metrics collection via node_exporter and platform-specific exporters; requires Grafana or similar for unified dashboarding |
| Log Management | Integrated log management with Logs in Context that correlates logs directly with APM traces, infrastructure events, and error tracking | No native log management; designed exclusively for metrics. Teams typically pair Prometheus with Loki or the ELK stack for log collection |
| Alerting and Incident Response | ||
| Alerting System | AIOps-powered alerting with automated detection, correlation of related incidents, and notification workflows integrating with Slack and other tools | Alerting rules defined in PromQL with a separate Alertmanager component that handles routing, silencing, grouping, and notification delivery |
| Incident Correlation | AI-driven incident correlation ties multiple alerts to single issues, reducing alert fatigue and enabling automated root cause analysis | Alertmanager groups related alerts by configurable labels; manual correlation required without additional tooling like Grafana OnCall |
| SLA and Service Level Tracking | Built-in service level management lets teams define and track SLIs and SLOs with a few clicks, tied directly to business outcomes | SLO tracking possible through PromQL recording rules and dedicated tools like Sloth; requires manual setup and configuration |
| Data Model and Querying | ||
| Data Model | Unified telemetry data store ingesting metrics, events, logs, and traces (MELT) with cross-signal correlation in a single platform | Dimensional time series data model where each series is identified by a metric name and key-value label pairs for flexible filtering |
| Query Capabilities | NRQL provides SQL-like syntax for querying all telemetry types; supports joins, facets, and custom dashboards across the full data stack | PromQL is a purpose-built functional language for time series selection, aggregation, and transformation; widely adopted as an industry standard |
| Data Retention | Cloud-based storage with configurable retention periods; 100 GB free data ingest per month with additional capacity at $0.40/GB beyond the free 100 GB limit | Local storage on disk with configurable retention; long-term storage requires remote write to Thanos, Cortex, or similar backends |
| Integration and Ecosystem | ||
| Integrations | 780+ quickstart integrations covering major cloud providers, databases, frameworks, and languages with pre-built dashboards and alerts | Hundreds of official and community-contributed exporters for extracting metrics from existing systems; native Kubernetes service discovery |
| OpenTelemetry Support | Full OpenTelemetry support for ingesting metrics, traces, and logs from open-source instrumentation without vendor lock-in | Native support for OpenMetrics format; OpenTelemetry Collector can export to Prometheus as a backend for metrics data |
| Visualization | Built-in customizable dashboards with drag-and-drop widgets, pre-built views for each capability, and session replay with AI analysis | Basic built-in expression browser for ad-hoc queries; most teams use Grafana as the primary visualization and dashboarding layer |
| AI and Advanced Capabilities | ||
| AI-Powered Features | AI and agentic monitoring for LLM applications, SRE Agent for automated remediation, and AI-powered session replay analysis | No built-in AI capabilities; community projects and third-party tools can add anomaly detection on top of Prometheus metrics |
| Security Monitoring | Integrated vulnerability management that prioritizes risks using production impact data, with guided remediation and AI reasoning | No native security monitoring; security metrics can be collected via custom exporters but require separate SIEM tooling for analysis |
| Scalability Architecture | Fully managed cloud platform that scales automatically; handles unlimited data ingest volumes without user-managed infrastructure | Single-server architecture with federation for scaling; hierarchical and horizontal federation modes available for large deployments |
Core Monitoring
Application Performance Monitoring
Infrastructure Monitoring
Log Management
Alerting and Incident Response
Alerting System
Incident Correlation
SLA and Service Level Tracking
Data Model and Querying
Data Model
Query Capabilities
Data Retention
Integration and Ecosystem
Integrations
OpenTelemetry Support
Visualization
AI and Advanced Capabilities
AI-Powered Features
Security Monitoring
Scalability Architecture
Which approach fits
New Relic delivers a comprehensive, fully managed observability platform with AI-powered features and 780+ integrations, while Prometheus provides a battle-tested, free open-source metrics monitoring system that gives teams complete control over their monitoring infrastructure. The right choice depends on whether you prioritize operational simplicity or cost control and customization.
When each approach fits
Choose New Relic if:
We recommend New Relic for organizations that want a single, unified observability platform covering APM, infrastructure, logs, security, and AI monitoring without managing monitoring infrastructure. Teams that need rapid onboarding through 780+ pre-built integrations, AI-powered incident correlation, and enterprise compliance features like FedRAMP and HIPAA eligibility will get the most value from New Relic's managed approach. The usage-based pricing with 100 GB of free monthly data ingest makes it accessible for teams of all sizes.
Choose Prometheus if:
We recommend Prometheus for cloud-native engineering teams, particularly those running Kubernetes, who want a proven, cost-free monitoring solution with deep community support and 63,000+ GitHub stars. If your team has the operational expertise to manage self-hosted monitoring infrastructure and values the flexibility of PromQL along with the ability to choose your own visualization, alerting, and long-term storage backends, Prometheus gives you full control without licensing costs. It is the ideal foundation for organizations building a custom observability stack from best-of-breed open-source components.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
Can I use New Relic and Prometheus together?
Yes, many organizations run both tools in a complementary setup. Prometheus handles metrics collection at the infrastructure level, particularly for Kubernetes environments where its native service discovery excels, while New Relic serves as the centralized observability platform for APM, logs, and cross-signal correlation. New Relic supports OpenTelemetry ingestion and can receive data from Prometheus exporters through the OpenTelemetry Collector, allowing teams to keep their existing Prometheus instrumentation while gaining New Relic's AI-powered analysis and unified dashboarding capabilities.
What are the total cost differences between New Relic and Prometheus?
Prometheus is free to download and run with zero licensing costs under the Apache 2.0 license, but you must account for infrastructure hosting, storage, operational staff time, and any managed Prometheus services you might use for long-term retention. New Relic offers a free tier with 100 GB of data ingest per month and unlimited basic users. Paid plans start at $49/user/month for core users and go up to $349/user/month for full platform access, with data ingest priced at $0.40/GB beyond the free 100 GB limit. For smaller teams, Prometheus is significantly cheaper in direct costs, while larger organizations often find New Relic's managed approach reduces total cost of ownership through lower operational overhead.
How does long-term data retention compare between the two?
New Relic handles data retention as a fully managed cloud service, so teams do not need to worry about storage infrastructure, capacity planning, or backup strategies. Retention periods are configurable based on your plan and data volume. Prometheus uses local disk storage by default, which limits retention to what a single server can hold. For long-term storage beyond weeks or months, you need to configure remote write to external backends such as Thanos, Cortex, or Mimir. These add architectural complexity but give you complete control over retention policies, storage costs, and data sovereignty.
Which tool is better for Kubernetes monitoring?
Prometheus was purpose-built for cloud-native environments and is the second project to graduate from the CNCF after Kubernetes itself. It features native Kubernetes service discovery that automatically finds and scrapes metrics from pods, services, and nodes without manual configuration. New Relic also provides robust Kubernetes monitoring with cluster-level dashboards, pod-level resource tracking, and correlation between Kubernetes infrastructure and application performance metrics. If you need a Kubernetes-specific metrics backend with minimal dependencies, Prometheus is the natural choice. If you want Kubernetes monitoring as part of a broader observability strategy that includes APM, logs, and AI-powered insights, New Relic offers a more integrated experience.
How do the query languages compare?
PromQL is a purpose-built functional query language designed specifically for dimensional time series data. It excels at selecting, aggregating, and transforming metrics with operations like rate calculations, histogram analysis, and label-based filtering. PromQL has become an industry standard adopted by many other monitoring tools. NRQL (New Relic Query Language) uses SQL-like syntax that is more approachable for teams already familiar with relational databases. NRQL can query across all telemetry types including metrics, events, logs, and traces in a single query, which is something PromQL cannot do since Prometheus focuses exclusively on metrics. We find PromQL more powerful for pure metrics analysis, while NRQL offers broader cross-signal querying capabilities.