Most observability stacks pay for visibility twice: once in engineering time to instrument every service, and again in storage to keep the data. DeepFlow moves both costs into the kernel — eBPF reads traffic and function calls directly, so a polyglot fleet of microservices, plus the gateways, meshes, databases, and message queues between them, becomes traceable without anyone touching application code.
What Sets It Apart
- Zero-code by construction: because signals come from eBPF rather than SDKs, coverage extends to closed-source components and infrastructure that you could never instrument by hand, leaving no blind spots in the call path.
- AutoTagging correlates raw network and kernel events back to Kubernetes pods, services, and cloud resources automatically — the tedious metadata-stitching most teams do by hand.
- SmartEncoding reduces backend storage by around 10x, and profiling runs below 1% overhead, which is what makes always-on full-stack collection affordable rather than a sampling compromise.
- It can sit under existing tools as a backend for Prometheus, OpenTelemetry, SkyWalking, and Pyroscope, exposing SQL/PromQL/OTLP — so it augments a stack instead of replacing it.
Who It's For
Great fit if you run cloud-native or AI workloads where instrumenting every language and dependency is impractical, and you want tracing plus profiling without per-service agents. Look elsewhere if you're on a small monolith already well-served by a single APM SDK, or if your environment can't run eBPF — it needs a reasonably modern Linux kernel, and the kernel-level approach trades some application-semantic context for breadth.
