Minimal and predictable overhead
eBPF attaches lightweight kernel hooks so you can profile production without slowing it down.
Faster fixes, no slowdown
Continuous profiling
Debug faster, improve application performance and lower your cloud costs, without slowing down production.
Continuous profiling
Identify the most resource-intensive parts of your code
Vendor-neutral OTel integration
Ship data and prevent vendor lock-in with the emerging OTel profiling format for effortless, future-proof deployment.
Unified and continuous observability
Profiles, logs, metrics, and traces land in one platform for instant cross-signal correlation and faster root-cause analysis.
Capabilities
Actionable insights and comprehensive coverage
Always on
Always-on, low-overhead profiling
Continuously monitor application performance across various languages and runtimes in production environment with negligible overhead. Gain full visibility into real-world application behavior without ever compromising performance or stability.
Read the profiling introduction
Flame graphs
Pinpoint performance bottlenecks
Analyze code execution across your production environment to reveal the precise functions and methods consuming excessive resources, by transforming millions of raw stack trace samples into crystal-clear interactive flamegraphs.
Read the CPU monitoring docs
Compare
Optimize resource utilization & prove impact
Gain deep visibility into resource consumption patterns of your code and compare performance before and after optimizations or deployments, clearly demonstrating the impact of your changes and validating resource efficiency improvements.
Read the CPU monitoring docs
Setup
Developer-friendly deployment
Integrate Continuous Profiling seamlessly into your Kubernetes environments with minimal effort. Our agent deploys easily without requiring any code changes or complex instrumentation, allowing developers to start gaining insights quickly and focus on building features, not observability tooling.
Read the setup guide
Explain Flame Graph
AI-analyzed code optimization
Eliminate the burden of performance analysis by navigating millions of stack traces to identify bottlenecks and provide code-level recommendations that reduce CPU cycles and cloud costs with Explain Flame Graph.
Read the setup docs
Platform
Scalable observability for your systems
In-stream analysis
Continuous, real-time monitoring of AI interactions, detecting risks and performance issues before they impact users.
Infinite retention
Archives all system logs indefinitely, enabling deep historical audits and preventing data gaps without ballooning storage costs.
DataPrime engine
Transforms any incoming data for advanced querying, revealing hidden patterns without manual preparation or complexity.
Remote, index-free querying
Enables rapid searches across your infrastructure data, eliminating indexing overhead and cutting operational expenses instantly.
Guides
Read more about Application Performance Monitoring
APM13 min read
Top Application Performance Monitoring Tools (2026)
Application Performance Monitoring (APM) tools track throughput, response times, and error rates. They follow each request through distributed tracing and connect those signals to logs and real user...
Read now
APM13 min read
Application Performance Monitoring: Components, Metrics, and Practices
Why Is APM Important? The primary purpose of application performance monitoring is to provide real-time...
Read now
APM11 min read
Application Logging Best Practices: A Field Guide for 2026
The fastest postmortems usually trace back to one small thing: a log line that already...
Read now
AI ready at scale
Observability that was built for AI,
Observability that was built for AI,
not repackaged for it
Run your agents and teams on the right foundation, so they don't approximate, they know.
14 days, all features, no credit card required.
<1msAlert detection time
99.9%Platform uptime
10xData ingestion capacity