Google Agent Development Kit (ADK) vs Kong AI Gateway

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Google Agent Development Kit (ADK) Agents	Kong AI Gateway Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Enterprise API gateway extended to route, govern, and observe LLM and agent traffic across providers.
Category	Agents	Agents
Pricing	Free· Framework itself is free and open-source (Apache 2.0). Costs come from the underlying model provider (e.g. Gemini API / Vertex AI usage) and any hosting infrastructure (Cloud Run, GKE, Agent Engine).	Freemium· Free trial: $0 · Plus: Charged per Gateway · Enterprise: Custom pricing
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Multi-model
Editorial score	—	7.3 / 10
Use cases	Multi-agent research assistantCustomer support triage agentRAG chatbot backed by Vertex AI SearchCode review and refactoring agentBigQuery natural-language analytics agentDocument processing pipelineVoice/streaming conversational agentInternal tool-use agent orchestrating APIsEvaluation and regression testing of LLM workflowsEnterprise workflow automation on Agent Engine	llm-gatewaymulti-llm-routingai-observabilityprompt-guardrailsmcp-governancetoken-quotas
Pros	Genuinely open-source (Apache 2.0) with active Google engineering behind it, not a hosted-only product Multi-language: first-class Python, Java, and Go SDKs — rare among agent frameworks that are usually Python-only Built-in dev UI (`adk web`) with trace inspection, event stream, and session replay speeds up debugging enormously Model-agnostic via LiteLLM — Gemini is default but Claude, GPT, and local models plug in cleanly Rich multi-agent primitives out of the box: SequentialAgent, ParallelAgent, LoopAgent, and hierarchical sub-agents Tight Google Cloud integration for deployment (Cloud Run, GKE, Agent Engine) plus native BigQuery/Vertex Search tools Evaluation harness with trajectory-level scoring is included, not a separate paid add-on First-class MCP (Model Context Protocol) client and server support	Unified proxy for 20+ LLM providers with drop-in OpenAI-compatible API Mature gateway runtime with rate-limiting, caching, auth, and observability baked in Self-hostable OSS core; no hard dependency on a SaaS control plane First-class MCP and agent-to-agent traffic management Plugs into existing Kong API governance and SSO/RBAC
Cons	Documentation and examples lean heavily on Gemini + Google Cloud; non-Google paths work but feel like second-class citizens API surface is still evolving — breaking changes between minor versions have been common through 2025-2026 Multi-agent orchestration primitives are powerful but the graph/callback model has a real learning curve compared to a plain prompt loop Agent Engine deployment is convenient but locks you into GCP billing and quotas TypeScript/Kotlin support lags the Python SDK in features and community examples	Operational overhead of running Kong is overkill for small teams Enterprise features (FIPS, advanced analytics) gated behind sales Documentation spread across konghq.com, docs, and developer portal
Website	google.github.io	konghq.com

Pick Google Agent Development Kit (ADK) if

✅ Genuinely open-source (Apache 2.0) with active Google engineering behind it, not a hosted-only product
✅ Multi-language: first-class Python, Java, and Go SDKs — rare among agent frameworks that are usually Python-only
✅ Built-in dev UI (`adk web`) with trace inspection, event stream, and session replay speeds up debugging enormously
✅ Model-agnostic via LiteLLM — Gemini is default but Claude, GPT, and local models plug in cleanly

Pick Kong AI Gateway if

✅ Unified proxy for 20+ LLM providers with drop-in OpenAI-compatible API
✅ Mature gateway runtime with rate-limiting, caching, auth, and observability baked in
✅ Self-hostable OSS core; no hard dependency on a SaaS control plane
✅ First-class MCP and agent-to-agent traffic management

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