Keploy vs Agent Cloud
Detailed side-by-side comparison to help you choose the right tool
Keploy
AI Knowledge Tools
Open-source, AI-powered testing agent that automatically generates test cases, dependency mocks, and production-like sandboxes from real user traffic using eBPF. Helps developers achieve 90% test coverage in minutes with zero code changes.
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CustomAgent Cloud
🔴DeveloperAI Knowledge Tools
Open-source platform for building private AI apps with RAG pipelines, multi-agent automation, and 260+ data source integrations — fully self-hosted for complete data sovereignty.
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CustomFeature Comparison
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Keploy - Pros & Cons
Pros
- ✓Completely free and open-source with 15,600+ GitHub stars and 1.2M+ downloads, proving strong community trust
- ✓Achieves up to 90% test coverage within 2 minutes without requiring any code changes to the application
- ✓Uses eBPF for kernel-level traffic capture, which is more accurate and less invasive than SDK-based instrumentation
- ✓Auto-generates dependency mocks (200M+ mocks created), eliminating manual mock authoring for databases and external services
- ✓Supports multiple backend languages including Go, Python, Java, and Node.js, making it broadly applicable
- ✓Deterministic replay in CI creates production-like sandboxes for reliable regression testing
Cons
- ✗eBPF requires Linux kernel support, limiting native use on Windows and some macOS configurations
- ✗Primarily focused on backend API testing — not suited for frontend UI or end-to-end browser testing
- ✗Record-and-replay approach may miss edge cases that don't appear in captured production traffic
- ✗Learning curve for teams unfamiliar with eBPF concepts and traffic-based test generation
- ✗Cloud/enterprise pricing is not publicly listed, requiring a demo booking for teams needing managed features
Agent Cloud - Pros & Cons
Pros
- ✓Fully open-source under AGPL 3.0 with a self-hosted community edition that includes the entire platform — no feature gating between free and paid tiers for core RAG and agent capabilities.
- ✓260+ pre-built data connectors out of the box, covering relational databases, document stores, SaaS apps, and file formats, eliminating the need to write custom ETL for most enterprise sources.
- ✓LLM-agnostic architecture supports OpenAI, Anthropic, and locally hosted open-source models (Llama, Mistral), so sensitive workloads can stay entirely on-premise.
- ✓Built-in multi-agent orchestration with CrewAI-style role-based agents that can call third-party APIs and collaborate on multi-step tasks, rather than just single-turn chat.
- ✓Strong data sovereignty story with VPC deployment, SSO/SAML, and audit logging in the Enterprise tier — well-suited to regulated industries that cannot use hosted RAG services.
- ✓Permissioning model lets admins scope specific agents to specific user groups, preventing accidental cross-team data exposure inside a single deployment.
Cons
- ✗Self-hosting assumes Kubernetes and DevOps expertise — not a fit for teams that want a one-click hosted chatbot with minimal infrastructure work.
- ✗AGPL 3.0 licensing is more restrictive than MIT/Apache and can complicate embedding Agent Cloud into proprietary commercial products without a commercial license.
- ✗Smaller ecosystem and community compared to Langflow, Flowise, or Dify, which means fewer third-party tutorials, templates, and Stack Overflow answers.
- ✗Managed Cloud and Enterprise pricing is sales-gated rather than published, making upfront cost comparison difficult for procurement teams — expect to budget $500–$2,000+/month for Managed Cloud and $25,000–$100,000+/year for Enterprise based on comparable platforms.
- ✗The platform is broad in scope (ingestion + vector + agents + UI), so debugging issues that span multiple layers can require deeper system understanding than narrower tools.
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