New Relic vs. Timescale: A data-backed comparison
Explore New Relic and Timescale’s features, pricing, adoption trends, and ideal use cases to help you determine which tool best fits your team.
New Relic vs. Timescale at a glance
New Relic offers advanced analytics and distributed tracing for proactive performance management. Timescale is a high-performance time-series database built on PostgreSQL, optimized for storing and analyzing large volumes of time-stamped data.
New Relic suits teams needing unified monitoring, deep analytics, and proactive alerting. Timescale is the right fit for organizations that require scalable, flexible storage and analysis of time-series data.
Metrics | New Relic | Timescale |
|---|---|---|
Relative cost | 56% lower cost than category average | 12% lower cost than category average |
Adoption trend | 5% QoQ adoption growth | 7% QoQ adoption growth |
Primary user segment | – | – |
Best for | Small and medium-sized businesses that need comprehensive application performance monitoring without the complexity of enterprise-level observability platforms. | Micro businesses that need time-series database capabilities without the complexity of enterprise-level data infrastructure systems. |
New Relic overview
New Relic provides unified monitoring across applications, infrastructure, and logs, along with distributed tracing and customizable dashboards. It’s ideal for teams wanting a managed observability solution with deep analytics, proactive troubleshooting, and full-stack visibility.
Recent innovations include AI-powered Predictions for forecasting issues before they occur, Response Intelligence for automated causal analysis and remediation, and agentic integrations that automate workflows across popular ITSM and SDLC tools.
New Relic key features
Features | Description |
|---|---|
Full-stack monitoring | Monitors applications, infrastructure, and user experience in one platform. |
Application performance monitoring | Tracks code-level performance and error rates. |
Distributed tracing | Visualizes requests and dependencies across microservices. |
Log management | Centralizes and analyzes logs in real time. |
Proactive alerting and AI | Provides automated, intelligent incident response. |
Timescale overview
Timescale is a purpose-built time-series database, fully compatible with PostgreSQL, designed for scalable storage and fast analysis of time-stamped event data. Advanced features, such as hypertables, native compression, and continuous aggregates, enable efficient storage, faster queries, and automated data retention for both real-time and historical analytics.
It’s well-suited for engineering teams managing large telemetry, IoT, or observability workloads and needing flexible analytics.
Timescale key features
Features | Description |
|---|---|
Time-series data management | Stores and manages high-volume time-stamped data. |
PostgreSQL compatibility | Extends Postgres with time-series optimizations and SQL support. |
Advanced compression | Reduces storage costs for long-term data retention. |
Continuous aggregation | Speeds up analytics with pre-computed aggregates. |
Automated data retention policies | Simplifies data lifecycle and compliance management. |
Pros and cons
Tool | Pros | Cons |
|---|---|---|
New Relic |
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Timescale |
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Which tool is better?
New Relic is better for teams needing managed, unified observability with rich analytics and alerting. Timescale is the better fit for organizations focused on advanced time-series data storage and analytics.
When New Relic is the better choice
- Your team needs unified, managed monitoring across the full stack.
- Your team needs deep analytics, dashboards, and alerting in one solution.
- Your team needs distributed tracing for application troubleshooting.
- Your team needs to operate in complex or hybrid environments and needs broad visibility.
When Timescale is the better choice
- Your team needs scalable, high-performance time-series data storage.
- Your team needs SQL support with full PostgreSQL compatibility.
- Your team needs to manage large-scale telemetry or IoT data.
- Your team needs advanced analytics on time-stamped events.