Instant Observability

for Cloud & AI
Applications

Zero Code

Full Stack rocket

eBPF & Wasm

Network-Centric Distributed Tracing with DeepFlow: Troubleshooting Your Microservices in Zero Code Download paper >>

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Universal Map for Any Service

DeepFlow provides a universal map with Zero Code by eBPF for production environments, including your services in any language, third-party services without code and all cloud-native infrastructure services. In addition to analyzing common protocols, Wasm plugins are supported for your private protocols. Full Stack golden signals of applications and infrastructures are calculated, pinpointing performance bottlenecks at ease.

Distributed Tracing for Any Request

Zero Code distributed tracing powered by eBPF supports applications in any language and infrastructures including gateways, service meshes, databases, message queues, DNS and NICs, leaving no blind spots. Full Stack network performance metrics and file I/O events are automatically collected for each Span. Distributed tracing enters a new era: Zero Instrumentation.

eBPF Wasm

Continuous Profiling for Any Function

DeepFlow collects profiling data at a cost of below 1% with Zero Code, plots OnCPU/OffCPU/GPU/Memory/Network function call stack flame graphs, locates Full Stack performance bottleneck in business functions, library and framework functions, runtime functions, shared library functions, kernel function, CUDA functions, and automatically relates them to distrubuted tracing data.

eBPF Wasm

Seamless Integration with Popular Stack

DeepFlow can serve as storage backed for Prometheus, OpenTelemetry, SkyWalking and Pyroscope. It also provides SQL, PromQL and OLTP APIs to work as data source in popular observability stacks. It injects meta tags for all obervability signals including cloud resource, K8s container, K8s labels, K8s annotations, CMDB business attributes, etc., eliminating data silos.

Performance 10x ClickHouse

SmartEncoding injects standardized and pre-encoded meta tags into all observability data, reducing storage overhead by 10x compared to ClickHouse String or LowCard method. Custom tags and observability data are stored separately, making tags available for almost unlimited dimensions and cardinalities with uncompromised query experience like BigTable.