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Pyroscope

Open Source
★ 11,466 • Updated Aug 15, 2026, 09:50 PM

Pyroscope is an open-source continuous profiling platform that enables developers to debug performance issues down to a single line of code.

Use Cases ▼
01

Identify CPU bottlenecks in production services

02

Analyze memory allocations to optimize garbage collection

03

Compare performance before and after code deployments

04

Profile applications running in Kubernetes clusters

05

Trace latency issues to specific functions

06

Monitor multi-language microservices with unified profiling

What Problem It Solves ▼

Pyroscope solves the challenge of identifying performance bottlenecks in production applications. Traditional profiling is often manual and intermittent, but Pyroscope provides continuous, always-on profiling with low overhead, allowing developers to pinpoint CPU, memory, and I/O issues down to the exact line of code causing slowdowns.

Key Features ▼
✓

Low overhead continuous profiling

✓

Intuitive flamegraph visualizations

✓

Supports multiple programming languages

✓

Integrates with Grafana ecosystem

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Open source and actively maintained

Overview

Pyroscope is an open-source continuous profiling platform that enables developers to debug performance issues down to a single line of code. Originally developed by Pyroscope Inc. and later acquired by Grafana Labs, it is now part of the Grafana observability stack. The platform supports profiling of applications written in multiple languages including Go, Java, Python, Ruby, .NET, and more, using language-specific SDKs or low-overhead agents. It collects CPU, memory, and I/O profiles continuously with minimal performance impact. Pyroscope provides a rich UI with flamegraphs, comparison views, and tagging to analyze performance data over time. It can be deployed as a single binary, in Docker containers, or on Kubernetes, and integrates with Grafana for unified observability.

Pros & Cons

Pros

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Low overhead continuous profiling

+

Intuitive flamegraph visualizations

+

Supports multiple programming languages

+

Integrates with Grafana ecosystem

+

Open source and actively maintained

Cons

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Requires self-hosting and operational overhead

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Storage and retention of profiling data can be resource-intensive

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Configuration may require familiarity with profiling concepts

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Limited to profiling, not a full APM solution

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