Kubeshark vs Parca

Kubeshark indexes Kubernetes cluster network traffic at the kernel level using eBPF and makes it searchable with Kubernetes, API, and network context. It can decrypt TLS and service-mesh mTLS traffic without keys, certificates, sidecars, or application changes. Users can capture retrospective traffic snapshots, filter them by time, nodes, workloads, and IPs, then export PCAP files. Its KFL query language combines Kubernetes identity, API context, and network attributes. Kubeshark also provides an identity-aware service map and performance indicators for pods, services, nodes, and namespaces. It supports more than 23 protocols, including HTTP, Kafka, Redis, PostgreSQL, gRPC, and DNS. Cluster network data can be exposed through MCP to AI assistants such as Claude Code, Cursor, and GitHub Copilot. Deployment uses Helm in Kubernetes, and self-hosted air-gapped operation is listed for Enterprise. The free Community plan supports up to 3 nodes or 60 pods and requires internet connectivity. Paid plans start at $30/mo.

Parca is a self-hosted profiling system that collects and stores CPU, memory, I/O, and other profiles for queries over time. Its eBPF-based Parca Agent captures user-space and kernel-space stack traces, while the system also accepts valid pprof-formatted profiles pushed through gRPC or pulled from targets over HTTP. Users can query by labels, compare dimensions such as processes or code versions, and view profiles in the web interface with Icicle Graphs. A Grafana plugin can display profiles stored in Parca. The agent supports automatic profiling for languages including C, C++, Go, Rust, .NET, Deno, Erlang, Java, Julia, Node.js, Wasmtime, PHP, Ruby, and Python. Installation options include binaries, containers, Kubernetes, and Snaps. Parca is open source under the Apache 2 License and is free. The agent requires Linux kernel 4.18 or newer and privileged access to load eBPF programs. Parca says it receives profiling statistics and function-name metadata, not binaries or source code.