Apache SkyWalking

Apache SkyWalking is a self-hosted observability platform for cloud-native systems and AI workloads. It is best suited to teams that want traces, metrics, logs, and profiling in one place across services and infrastructure. Its breadth is compelling, but it asks teams to run and manage the platform themselves.

Overview

SkyWalking brings tracing, metrics, logs, and profiling together to help teams investigate service behavior. Its cloud-native view spans services, Kubernetes, and infrastructure, with topology, telemetry, profiles, and alerts. That makes it a broader operational choice than a tracing-only tool, particularly when teams need to connect service relationships with multiple kinds of telemetry.

AI observability is another notable part of the platform: teams can inspect model and MCP call latency and token rates, and follow agent conversations, tool calls, and recorded file changes. The project is an Apache Software Foundation project, and its source releases are official Apache artifacts; it recommends PGP or SHA512 verification for downloads.

Key features

• Unified telemetry: Traces, metrics, logs, and profiling share a platform, while distributed and transaction tracing, dependency mapping, real user monitoring, and log correlation cover several common investigation needs.
• Broad agent coverage: Agents are available for Java, Python, NodeJS, Go, Rust, PHP, Ruby, Nginx LUA, Kong, and client-side JavaScript. This range helps when a system spans several languages, though teams still need to choose and operate the appropriate instrumentation.
• Connected ecosystem: Integrations include OpenTelemetry OTLP, Istio, Envoy, Kubernetes, Grafana, and MCP. BanyanDB is the project's observability database for ingesting, analyzing, and storing metrics, tracing, and logging data.
• AI workload inspection: Model and MCP call latency and token rates, as well as agent conversations, tool calls, and recorded file changes, give teams visibility into AI activity alongside service telemetry.
• Security and release practices: Horizon UI documents content security policy, strict session cookies, no-store API responses, and optional login auditing. The project recommends verifying source downloads with PGP or SHA512 signatures.

Horizon UI compatibility differs by SkyWalking version: native support begins with v11, while v10 support is partial. The GraalVM distribution is explicitly experimental, so it is not the conservative choice for teams seeking a mature distribution path.

Pricing

Apache SkyWalking is free and open source. Its Apache SkyWalking plan costs nothing and is an open-source observability platform for self-hosted deployment. There is no paid hosted plan in this offer; the trade-off is that the team runs the deployment rather than buying a managed service.

Platforms

SkyWalking supports API, Linux, macOS, self-hosted, web, and Windows environments. Its getting-started instructions cover Docker-based installation on Linux, macOS, WSL, and Windows PowerShell, giving teams several routes to a self-managed deployment.

Who it's for

SkyWalking is a strong fit for engineering teams operating cloud-native services that want broad telemetry, dependency visibility, and a path to inspecting AI workloads in one platform. Its varied agents and integrations suit mixed-language systems. It is less suited to teams that want a managed service or prefer not to take responsibility for deployment and storage.

Pros and cons

• Pro: Traces, metrics, logs, profiling, and several tracing and monitoring capabilities come together, reducing the need to treat each signal as a separate observability destination.
• Pro: AI call metrics and agent activity are inspectable alongside cloud-native telemetry, useful for teams whose operational concerns include AI workloads.
• Pro: The project supports a wide range of language agents and ecosystem integrations, including OpenTelemetry OTLP, Kubernetes, and MCP.
• Con: Self-hosting makes deployment and platform operation the user's responsibility.
• Con: Horizon UI is only partially compatible with v10, and the GraalVM distribution remains experimental; teams should account for both limits when choosing a version or distribution.

Alternatives

Jaeger is a reasonable alternative for teams that want a free, open-source, cloud-native self-hosted option rather than SkyWalking's wider combination of observability signals and AI inspection.

SigNoz is worth considering for teams open to a freemium product with a free trial and both self-hosted and web platforms.

Uptrace may suit teams that prefer a cloud plan with a stated 50 GB free monthly allowance, unlimited users, services, and hosts, and a $0.075/GB rate beyond the allowance.

OpenZipkin is another free, self-hosted web option.

Tracekit is an option for teams seeking a freemium API and web service; its free plan includes 200K traces per month, seven-day retention, one team member, and three alert rules.

Grafana Tempo is an alternative if a Linux platform is the deciding requirement.

Honeycomb may fit teams looking for a freemium service with a free tier capped at 20M events and 100M metrics data points per month, plus two triggers.

Grafana Cloud Traces is a cloud alternative with a free plan that includes 50 GB ingested per month and 14-day retention.

For category comparisons, see Distributed Tracing Tools and Application Performance Monitoring.

Verdict

Choose Apache SkyWalking if your team can self-host and wants one platform for broad cloud-native telemetry and AI workload inspection. Its strongest reason to choose is that breadth; its clearest reason to look elsewhere is the operational burden of running it yourself.