Skip to content
    Back to AIOps & Observability
    AIOps & ObservabilityStartupeBPF Obs

    Deepflow

    High-performance observability using eBPF for cloud-native apps

    Mkt Cap / ValOpen Source
    eBPF-based observability eliminates agents and sampling; captures full-fidelity network telemetry for cloud-native infrastructure.
    Analyst take · Competitive edge

    SWOT Analysis

    Strengths
    • eBPF kernel-level visibility; zero instrumentation, captures all traffic without sampling
    • High performance; sub-microsecond overhead; scales to large containerized environments
    • Open-source foundation; appeals to infrastructure teams skeptical of vendor lock-in
    Opportunities
    • Expand to application-layer observability (traces, application metrics) alongside network telemetry
    • Build commercial support and managed SaaS offering for enterprises
    • Become reference architecture for eBPF observability in cloud-native landscape
    Weaknesses
    • eBPF requires Linux kernel 5.8+; not portable to Windows or legacy infrastructure
    • Requires kernel-level access; deployment complexity in restricted/compliance environments
    • Early-stage; limited integration ecosystem compared to mature platforms
    Threats
    • Established observability vendors adding eBPF capabilities to existing platforms
    • Kubernetes and container runtimes evolving; reduces eBPF advantage if observability APIs improve

    User Sentiment

    Synthesized from G2, Gartner Peer Insights, and analyst review data.

    What users love
    • eBPF observability requires no instrumentation or sampling; captures complete picture
    • Low resource footprint vs. traditional agents; scales efficiently in large deployments
    • Open-source; appeals to infrastructure and DevOps teams
    Common complaints
    • Linux-only solution; not viable for Windows or mixed OS environments
    • Kernel-level access requirements complicate deployment in restricted/compliance-sensitive orgs
    • Limited ecosystem and integrations; primarily network-focused telemetry

    Customer Profile

    Who buys this

    Typical segments

    Linux/Kubernetes-first organizations with homogeneous infrastructureCloud-native teams prioritizing network and infrastructure observability

    Typical buyer

    Infrastructure engineer or platform team lead

    Top use cases
    1. 1Network-level observability and traffic analysis in Kubernetes clusters
    2. 2Microservices communication tracing without application instrumentation
    3. 3Incident detection and troubleshooting in large-scale containerized deployments

    Future Focus Areas

    1

    Expand beyond network to full-stack observability—application metrics and traces

    2

    Build managed/SaaS offering with compliance and enterprise support