Sentry
Application error tracking and performance monitoring.
Alternatives · 2026
Alternatives to New Relic
Full-stack observability platform for engineering teams.
6 hand-curated alternatives from MintedSaaS's directory. See the New Relic listing →
New Relic is a full-stack observability platform built to help engineering teams monitor applications, infrastructure, and user experience across distributed systems. It collects metrics, logs, and traces from applications and their underlying infrastructure, then surfaces them in a unified interface for debugging and performance optimization. Teams use it to detect anomalies, troubleshoot incidents, and optimize system performance in real time. The platform is designed around organizations that have complex, multi-service architectures and want centralized visibility across the entire stack.
Most teams reach for New Relic during rapid scaling or when they've outgrown simpler monitoring tools. It's common in companies with dedicated DevOps and SRE teams who can integrate it deeply into their CI/CD pipelines and on-call workflows. Some organizations choose it because they need vendor support and SLAs; others find it expensive and look for alternatives that offer similar breadth at a lower price point or with different architectural assumptions. If you're shopping alternatives, you're likely deciding between agents that ingest your observability data versus dashboarding on top of open-source systems, and whether you prefer a managed service or self-hosted infrastructure.
What we offer that competes
Prometheus
Open-source monitoring system with a powerful query language.
Splunk
Enterprise platform for searching and analyzing log data.
Dynatrace
AI-driven observability for cloud-native applications.
Grafana
Open-source dashboards for metrics, logs, and traces.
Datadog
Observability platform for cloud-scale infrastructure.
What to look for
- Whether the platform charges per data ingested or per host/user (affects cost predictability at scale).
- Whether you can self-host the entire platform or if it's SaaS-only (determines infrastructure control).
- Whether the platform supports your primary observability signal first (metrics, logs, errors, or traces).
- Whether the platform offers a free tier or open-source version you can trial without a sales call.
- Whether built-in dashboards are provided or you must build visualizations from raw data yourself.
- Whether the platform has a published API and documentation for custom integrations and exports.
FAQ
What are the best alternatives to New Relic?
Sentry excels at error and performance monitoring for applications, Prometheus is a popular open-source metrics backend, Splunk is a logs-first platform with broader IT operations capabilities, Dynatrace specializes in application performance management with AI-driven analysis, Grafana offers open-source dashboarding with multiple data backends, and Datadog provides a commercial platform comparable to New Relic's breadth. Which is best depends on whether you prioritize cost, self-hosting, or specific observability signals like errors or metrics.
Are there free alternatives to New Relic?
Prometheus and Grafana are both free and open-source, though they require you to operate the infrastructure and build your own dashboards. Sentry has a generous free tier for error tracking. Datadog and Splunk offer free trials but charge for production use. If cost is the primary driver, open-source tools shift the burden from licensing to operational labor.
Which observability platform should I choose for microservices?
Choose based on your team's depth and existing infrastructure. Prometheus works well if you already run Kubernetes and have SRE expertise. Grafana is ideal if you want flexible dashboarding across multiple data sources. Datadog, Dynatrace, and New Relic are easiest if you want managed services and don't want to run your own backend. Sentry is best if your primary pain point is application errors and crashes.
What's the difference between metrics, logs, and traces?
Metrics are numerical measurements (CPU, memory, request latency) aggregated over time. Logs are timestamped text events from applications and services. Traces show the path a request takes across multiple services. Most platforms now ingest all three, but some specialize: Prometheus focuses on metrics, Splunk on logs, Sentry on errors (which often include traces).
Can I use multiple observability tools together?
Yes. Many teams run Prometheus for metrics, Sentry for errors, and Splunk or Grafana for logs, then stitch them together in dashboards. It costs more in total licensing but lets you optimize each signal separately. New Relic and Datadog attempt to make this single tool; self-hosted alternatives let you build a custom stack with open-source components.
Do I need observability agents installed on every server?
It depends on the platform. Prometheus scrapes metrics from HTTP endpoints (agents optional). Sentry requires an SDK in your application code. Datadog, New Relic, and Dynatrace typically need agents on servers or containers to capture deep system and application data. Self-hosted options like Grafana rely on whatever data sources you wire in, so agent requirements vary.
What observability platforms work with Kubernetes?
All six alternatives work with Kubernetes. Prometheus is native to Kubernetes and most cloud-native stacks run it by default. Grafana dashboards Kubernetes metrics easily. Datadog, Dynatrace, and New Relic all offer Kubernetes integrations and are commonly deployed as sidecar agents. Sentry can monitor applications running on Kubernetes but is lighter on infrastructure metrics.
How much data retention do I get with observability platforms?
Retention varies widely. Prometheus typically keeps 15 days of metrics by default; you increase this by adding storage capacity. Grafana's retention depends on your data source. Datadog, New Relic, and Dynatrace offer 30 days for metrics on entry tiers, with longer retention at higher prices. Splunk can store indefinitely but charges per gigabyte ingested. Check vendor pricing for your expected data volume.