Decision comparison
Grafana vs New Relic
Grafana and New Relic answer the same monitoring need with different cost shapes. Grafana is the open-source dashboarding and alerting layer over stores you run — Prometheus, Loki, Tempo — so telemetry stays yours and cost is infrastructure plus engineering time. New Relic is a commercial platform that ingests and analyses everything as a service, with application performance analysis built in and pricing based on data ingested.
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 — Metrics & Dashboards and Observability Platform.
Quick Comparison
| Decision factor | Grafana | New Relic |
|---|---|---|
| What it is | An open-source dashboarding and alerting layer over metrics, logs and traces you store yourself | A commercial observability platform that ingests, stores and analyses telemetry as a service |
| What you deploy | Grafana plus Prometheus, Loki and Tempo, usually as containers on Kubernetes with S3 storage behind them | An agent or OpenTelemetry SDK in the application, and an API key |
| What you run | Grafana plus the stores behind it — Prometheus, Loki, Tempo or equivalents | Nothing; agents and OpenTelemetry send data to the platform |
| Cost shape | Infrastructure and engineering time, with Grafana Cloud available as a managed option | Usage-based on data ingested and users, with a free tier |
| Data ownership | Telemetry stays in stores you operate and can retain as you choose | Telemetry lives in the vendor's platform, with retention set by plan |
| Analysis | Dashboards and alerting, with correlation assembled from the components you chose | Correlated traces, metrics, logs and errors with application performance analysis built in |
| Instrumentation | OpenTelemetry and Prometheus exposition, both open standards | OpenTelemetry plus its own agents for deeper application detail |
| Best fit | Teams who want control of telemetry storage and cost, with engineering capacity to match | Teams who want application performance answers without assembling a stack |
Grafana
- What it is:
- An open-source dashboarding and alerting layer over metrics, logs and traces you store yourself
- What you deploy:
- Grafana plus Prometheus, Loki and Tempo, usually as containers on Kubernetes with S3 storage behind them
- What you run:
- Grafana plus the stores behind it — Prometheus, Loki, Tempo or equivalents
- Cost shape:
- Infrastructure and engineering time, with Grafana Cloud available as a managed option
- Data ownership:
- Telemetry stays in stores you operate and can retain as you choose
- Analysis:
- Dashboards and alerting, with correlation assembled from the components you chose
- Instrumentation:
- OpenTelemetry and Prometheus exposition, both open standards
- Best fit:
- Teams who want control of telemetry storage and cost, with engineering capacity to match
New Relic
- What it is:
- A commercial observability platform that ingests, stores and analyses telemetry as a service
- What you deploy:
- An agent or OpenTelemetry SDK in the application, and an API key
- What you run:
- Nothing; agents and OpenTelemetry send data to the platform
- Cost shape:
- Usage-based on data ingested and users, with a free tier
- Data ownership:
- Telemetry lives in the vendor's platform, with retention set by plan
- Analysis:
- Correlated traces, metrics, logs and errors with application performance analysis built in
- Instrumentation:
- OpenTelemetry plus its own agents for deeper application detail
- Best fit:
- Teams who want application performance answers without assembling a stack
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 | Grafana | New Relic |
|---|---|---|
| Docker Hub pulls(Product adoption) | 5.3B | Not available |
| GitHub commits, 90d(Product adoption) | 3.2k | Not available |
| GitHub stars(Product adoption) | 76,000+ | Not available |
| Search interest(Market interest) | 21 | 4 |
| Hacker News mentions, 90d(Community interest) | 33 | 1 |
| npm weekly downloads(Developer adoption) | 80.4k | 847.6k |
| Product Hunt comments(Community interest) | 0 | 1 |
| Product Hunt rating(Community interest) | 5.0/5 | Unavailable |
| Product Hunt reviews(Community interest) | 1 | 0 |
| Product Hunt votes(Community interest) | 5 | 16 |
| PyPI weekly downloads(Ecosystem adoption) | 45.6k | Not available |
| Stack Overflow questions(Community interest) | 5.8k | 1.5k |
| GitHub commits, 90d(Developer adoption) | Not available | 352 |
| GitHub stars(Developer adoption) | Not available | 10 |
| PyPI weekly downloads(Developer adoption) | Not available | 929.1k |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Grafana
September 21, 2026Package vulnerabilities
npm · @grafana/data@13.2.2 · PyPI · grafana-client@5.1.2
0 vulnerabilities
across 2 packages
Repository security score
github.com/grafana/grafana
6.8/10
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
Interface Preview
Grafana
Feature Comparison
| Feature | Grafana | New Relic |
|---|---|---|
| Telemetry | ||
| Metrics dashboards and alerting | Full support | Full support |
| Log search | Full support | Full support |
| Distributed tracing | Full support | Full support |
| OpenTelemetry ingestion | Full support | Full support |
| Analysis | ||
| Application performance monitoring | Partial support | Full support |
| Automatic service dependency mapping | Partial support | Full support |
| Error tracking with release context | Partial support | Full support |
| Correlation across signal types | Partial support | Full support |
| Operations | ||
| Fully managed, nothing to run | Partial support | Full support |
| Self-hosted deployment | Full support | Not verified |
| Control over retention and storage cost | Full support | Partial support |
| Data stays in your own infrastructure | Full support | Not verified |
| Commercial | ||
| Open source core | Full support | Not verified |
| Free tier | Full support | Full support |
| Usage-based pricing | Partial support | Full support |
| Large plugin and integration ecosystem | Full support | Full support |
Telemetry
Metrics dashboards and alerting
Log search
Distributed tracing
OpenTelemetry ingestion
Analysis
Application performance monitoring
Automatic service dependency mapping
Error tracking with release context
Correlation across signal types
Operations
Fully managed, nothing to run
Self-hosted deployment
Control over retention and storage cost
Data stays in your own infrastructure
Commercial
Open source core
Free tier
Usage-based pricing
Large plugin and integration ecosystem
Which approach fits
Grafana and New Relic answer the same monitoring need with different cost shapes. Grafana is the open-source dashboarding and alerting layer over stores you run — Prometheus, Loki, Tempo — so telemetry stays yours and cost is infrastructure plus engineering time. New Relic is a commercial platform that ingests and analyses everything as a service, with application performance analysis built in and pricing based on data ingested.
When each approach fits
Choose Grafana if:
Choose Grafana when you want control of where telemetry lives and what retention costs. The stores behind it are yours, so a noisy service does not arrive as a surprise invoice, and Prometheus exposition and OpenTelemetry are open standards rather than agent formats. Grafana Cloud is available when the dashboards should be managed but the approach should stay open.
Choose New Relic if:
Choose New Relic when you want application performance answers without assembling a stack. Traces, metrics, logs and errors arrive correlated, service dependencies are mapped automatically, and its agents collect application detail that generic instrumentation does not. Nothing is deployed or operated, and the free tier makes the evaluation cheap.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What does each one need to run?
New Relic needs an agent or an OpenTelemetry SDK in the application and an API key; nothing else is deployed. Grafana needs the stores behind it — Prometheus for metrics, Loki for logs, Tempo for traces — typically as containers on Kubernetes with S3 or compatible object storage underneath, plus Grafana itself. Applications in Python, Java, Node.js or Go emit OTLP over HTTP to either, so the instrumentation work is the same and only the destination differs.
Is this really a build-versus-buy decision?
Largely, and framing it that way keeps it honest. Grafana is one component of a stack you assemble and operate — a metrics store, a log store, a trace store and the storage behind them. New Relic is that whole stack as a service. Comparing the licence cost of one against the subscription for the other misses most of the difference; the honest comparison prices infrastructure plus the engineering time to run it.
Does OpenTelemetry make the backend replaceable?
Largely, and that is the most useful change in this category. Applications emit OTLP and the backend becomes a routing decision rather than a rewrite, so trialling a second platform on a slice of traffic is realistic. Vendor agents still collect application detail that generic instrumentation does not, so check whether the depth you rely on comes from the standard or from the agent before assuming a clean swap.
What drives the bill on a usage-priced platform?
Telemetry volume, which grows as engineers instrument more — not as they get more value from it. A team that adds debug logging during an incident can move the invoice without anyone making a decision. The controls that matter are sampling, dropping low-value logs before ingestion, and retention tiers; a telemetry pipeline in front of the platform exists mostly to make those controls someone's job.
What does self-hosting actually cost?
Storage is cheap, especially on object storage; the engineer who keeps the cluster healthy is not. Running metrics, log and trace stores at scale means capacity planning, upgrades, cardinality management and an on-call rotation that understands them. For a team already operating Kubernetes that is an incremental step. For a team that is not, it is a commitment the licence saving may not cover.
Can we run both?
Many do, and it is a reasonable end state: the open stack scraping infrastructure metrics where volume is high and value per byte is low, and a commercial platform receiving application traces where correlation earns its price. The cost is two places to look during an incident, so decide in advance which is the front door and route alerts accordingly.