Find Regressions the Moment They Deploy
Always-on production profiling that continuously collects CPU and memory profiles from your running services. Detect performance regressions within minutes of deployment — not after users complain.
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Continuous profiling is always-on production profiling that collects CPU and memory profiles from your running services without requiring manual intervention. Unlike one-off profiling sessions, continuous profiling builds a historical record that enables direct comparison between deployments — making performance regressions immediately visible rather than discovered retrospectively through user complaints.
The key insight is correlation: deployment markers on profiling dashboards make it immediately obvious when a performance change occurred. A CPU flame graph that shows 15% of time newly spent in a JSON serialisation function, correlated with a deploy from 2 hours ago, gives the debugging engineer a precise starting point. Without historical profiling data, the same investigation involves speculation, reproduction attempts, and often days of back-and-forth.
eBPF-based profiling represents a significant advance in production profiling: by operating at the kernel level, it profiles any language running on Linux with sub-1% overhead and without requiring application code changes or language-specific agents. This makes it practical for polyglot architectures where maintaining per-language profiling agents is operationally complex. We select the right profiling approach for your stack and configure it for minimal operational overhead.
Engagement Phases
Platform Selection & Setup
We evaluate your language stack, infrastructure, and existing observability tooling to select the right continuous profiling platform: Pyroscope (open source, self-hosted), Parca (eBPF-based, low overhead), or Datadog Continuous Profiler (managed, integrated with existing APM). We install agents, configure sampling rates, and connect to Grafana or your existing dashboards.
Dashboard & Alert Configuration
We build Grafana dashboards (or equivalent) showing CPU and memory profiles correlated with deployment markers. We configure alerts for profile anomalies: CPU time increases above baseline threshold, heap allocation rate spikes, goroutine/thread count anomalies. We set up diff profiles comparing the current deployment against the previous baseline.
Validation & Team Enablement
We validate profiling accuracy against known performance characteristics and train your team on reading flame graphs, interpreting diff profiles, and acting on profiling alerts. We document the runbook for investigating a profiling alert and integrating profiling into your deployment review process.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| Regression detection time | Days (user reports) | Minutes (automated alert) |
| Profiling overhead | N/A | < 1% CPU |
| Deployment-correlated dashboards | 0 | Live for all services |
Tools We Use
Frequently Asked Questions
How much overhead does continuous profiling add?
Modern continuous profiling platforms use statistical sampling, not instrumentation, which keeps overhead below 1% CPU in production. Pyroscope samples at 100Hz by default, which is negligible for most workloads. eBPF-based profilers like Parca operate at the kernel level with even lower application overhead. We validate overhead in your specific environment before Go-Live.
What languages are supported?
Pyroscope supports Go, Python, Ruby, Node.js, Java, .NET, and Rust. Datadog Continuous Profiler supports Go, Python, Java, Ruby, Node.js, and PHP. eBPF profilers support any language running on Linux. We will confirm support for your specific language and runtime version before starting the engagement.
How do we use profiling data day-to-day?
The primary use case is regression detection at deployment time: compare the new deployment's profile against the previous baseline and investigate any significant CPU or memory increase before it reaches users. The secondary use case is proactive optimisation: reviewing weekly profile snapshots to identify functions that are growing in cost as traffic increases, before they become a problem.
Your P99 Deserves Better
Book a free 30-minute performance scope call with our engineers. We review your latency profile, identify the most impactful optimization target, and scope a sprint to fix it.
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