Kloudfuse captures CPU, memory, and contention profiles 24/7 in production. Flame graphs live in the same data lake as the rest of your telemetry, queried from the same place.
CPU flame graph — time attributed down to the function
Continuous collection from production, not ad-hoc sessions. Low-overhead sampling profiles every service, every minute, without impacting request latency.
Grafana Alloy pulls standard pprof endpoints. Same configuration patterns your team already knows. No proprietary agents or sidecars.
Profiles are stored and queried alongside the rest of your telemetry rather than in a separate profiling tool. One platform, not three browser tabs.
Kloudfuse captures seven profile types continuously: CPU, memory allocation, mutex contention, block, goroutine, wall-clock, and disk I/O. Each renders as an interactive flame graph that breaks down consumption to the individual function and line number.
CPU flame graph — time attributed down to the function
The profiler gathers samples from production systems continuously, not in ad-hoc sessions triggered during an incident. Low-overhead sampling captures call stacks at configurable intervals without measurable impact on request latency. Every profile is stored for long-term retention.
Add three annotations to your pod spec. Grafana Alloy discovers your services automatically using Kubernetes service discovery in pull mode. No sidecars, no custom agents, no manual registration per service.
# pod-spec — that’s the whole setup metadata: annotations: pyroscope.io/scrape: "true" pyroscope.io/application-name: "example app" pyroscope.io/port: "6060"
The Kloudfuse MCP Server translates natural language into profiling queries. K-Lens ranks the attributes most associated with a profiling anomaly to surface root cause hypotheses, across hundreds of attributes.
# you "Diff CPU profiles for auth-service between v2.3.1 and v2.3.0" # MCP → FuseQL diff( profile="cpu", service="auth-svc", a=deploy:"v2.3.0", b=deploy:"v2.3.1" ) ↳ retry_loop ↑ 19.7× · backoff.exponential ↑ 14.2×
Continuous profiling samples CPU usage, memory allocations, and contention data from production services 24/7, storing every profile for historical analysis. Ad-hoc profiling requires an engineer to manually attach a profiler to a running process, capture a snapshot, then disconnect. The difference: continuous profiling catches the 3 AM memory leak that ad-hoc profiling never sees. Kloudfuse stores profiles in Apache Parquet format with configurable retention on local PVC (50GB default) or long-term on S3/GCS.
Go and Java have first-class support with documented setup guides. Go applications get the deepest coverage with seven profile types including CPU, delta memory, mutex, block, goroutine, wall-clock, and disk I/O. Because Kloudfuse uses the Pyroscope protocol for ingestion, applications instrumented with Pyroscope SDKs for Python, Ruby, Node.js, .NET, and Rust are also compatible.
Kloudfuse uses low-overhead sampling profiler technology designed for 24/7 production use. Sampling-based profiling captures statistical snapshots of call stacks at configurable intervals rather than instrumenting every function call. Developers control data granularity and sampling rates. The collection agent (Grafana Alloy) runs in pull mode, scraping pprof endpoints on a schedule, so application code is not modified for collection.
Profiling data lands in the same unified data lake as all other observability signals. When a trace shows a slow span, you navigate directly to the flame graph for that service and time window. When a metric dashboard shows a CPU spike, you drill into the exact functions consuming cycles. K-Lens, Kloudfuse's AI-assisted analysis engine, ranks the attributes most associated with a profiling anomaly to surface root cause hypotheses.
No. Kloudfuse profiling uses open standards exclusively: applications expose standard pprof endpoints, and Grafana Alloy collects profiles in pull mode using the Pyroscope protocol (pyroscope.scrape and pyroscope.write). For Kubernetes environments, service discovery is automatic via discovery.kubernetes with standard annotations. No proprietary agents, sidecars, or binary instrumentation tools required.
Enable the Profiler Server, configure Alloy for pprof scraping, and start collecting profiles from your Go and Java services.
Read the guidePre-computed aggregations that update automatically — the same engine that powers continuous profiling analysis at scale without sacrificing query speed.
Read the blogPricing model, language support, data residency, deployment. For teams comparing unified vs per-host profiling.
See the comparisonThirty minutes on your telemetry. The cause, before the call ends.