Google Agent Development Kit (ADK) vs LLM Gateway

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

	Google Agent Development Kit (ADK) Agents	LLM Gateway Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	One API, 200+ models, transparent pricing, and no vendor lock-in.
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: $0 · Enterprise: Contact us
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Routes to GPT-4o, Claude 3.5 Sonnet, Gemini 1.5, Llama 3.1, Mistral, and 200+ others
Editorial score	—	—
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	Multi-provider LLM routingAutomatic failover between model vendorsPer-model cost and token analyticsA/B testing prompts across GPT and ClaudePrompt-injection and PII guardrailsSelf-hosted AI gateway for regulated dataUnified API key managementVendor-agnostic AI agent backends
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	Single OpenAI-compatible endpoint fronts 40+ providers and 200+ models with a one-line base-URL change Automatic failover between providers keeps AI features up when a single vendor has an outage Real-time cost analytics broken down by model, provider, and route surface spend leaks early Bring-your-own-keys tier means you can adopt the routing and analytics without adding a middleman on billing AGPLv3 self-host option removes vendor lock-in and satisfies teams that need data to stay on their own infra Built-in guardrails (prompt-injection detection, PII filtering) applied uniformly across every provider Only 5% flat markup on the managed credit tier is transparent compared to opaque enterprise gateway pricing
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	Adds a network hop and dependency between your app and the LLM provider, which matters for tight latency budgets AGPLv3 self-host license is copyleft and can be a non-starter for closed-source SaaS teams unwilling to comply Managed credit tier means one more vendor holding a payment relationship and access to your prompt traffic Feature parity with each upstream provider's newest, most exotic parameters can lag behind the native SDKs Deep observability and prompt-engineering tooling is thinner than dedicated LLMOps platforms like Langfuse or Helicone
Website	google.github.io	llmgateway.io

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 LLM Gateway if

✅ Single OpenAI-compatible endpoint fronts 40+ providers and 200+ models with a one-line base-URL change
✅ Automatic failover between providers keeps AI features up when a single vendor has an outage
✅ Real-time cost analytics broken down by model, provider, and route surface spend leaks early
✅ Bring-your-own-keys tier means you can adopt the routing and analytics without adding a middleman on billing

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