AWS Bedrock vs Google Agent Development Kit (ADK)

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

	AWS Bedrock Agents	Google Agent Development Kit (ADK) Agents
Tagline	Build and scale generative AI applications with foundation models	Google's open-source framework for building, evaluating, and deploying production AI agents
Category	Agents	Agents
Pricing	Paid· Pay-as-you-go per 1K input/output tokens per model; on-demand, batch, and provisioned throughput tiers. New AWS accounts get up to $200 in credits. Enterprise agreements via AWS.	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).
Model	Multi-model: Anthropic Claude, Meta Llama, Mistral, Cohere, AI21, Amazon Nova/Titan, DeepSeek, Stability, OpenAI GPT	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM
Editorial score	8.6 / 10	—
Use cases	Enterprise RAG chatbot over private documentsMulti-step tool-using agents via AgentCoreDocument summarisation and extraction pipelinesCompliant virtual assistants for regulated industriesModel routing between cheap and premium LLMsFine-tuned domain-specific copilotsContent moderation with GuardrailsBatch inference for large document backlogsImage generation with Stability and Nova CanvasWorkflow automation with Bedrock Flows	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
Pros	Single API for hundreds of foundation models across Anthropic, Meta, Mistral, Cohere, AI21, Amazon, DeepSeek and OpenAI Data stays inside the customer's AWS account, never used to train base models — a hard requirement for regulated industries First-class managed RAG (Knowledge Bases) and agent orchestration (AgentCore) without needing LangChain-style glue code Deep AWS-native integration with IAM, VPC endpoints, KMS, CloudWatch, CloudTrail, Lambda and SageMaker Guardrails for content filtering, PII redaction and contextual grounding that plug into any model behind the API Provisioned throughput and Model Distillation give predictable latency and material cost reductions at scale HIPAA, SOC, FedRAMP, ISO and GDPR compliance out of the box	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
Cons	Pricing is complex and varies per model, per region and per throughput mode — surprise bills are easy without CloudWatch cost alarms Frontier model availability lags direct vendor APIs; the newest Claude/GPT/Gemini versions can take weeks to reach Bedrock and specific regions Steep learning curve if you are not already fluent in IAM, VPC networking and the wider AWS console Agent, Knowledge Base and Guardrail configuration is verbose compared to lighter frameworks like LangChain, LlamaIndex or the OpenAI Assistants API Regional model coverage is uneven — some models are US-East-1 only, complicating EU and APAC data-residency deployments Vendor lock-in: prompts, agents, Knowledge Bases and Flows are not portable to Azure AI Foundry or Google Vertex without rework	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
Website	aws.amazon.com	google.github.io

Pick AWS Bedrock if

✅ Single API for hundreds of foundation models across Anthropic, Meta, Mistral, Cohere, AI21, Amazon, DeepSeek and OpenAI
✅ Data stays inside the customer's AWS account, never used to train base models — a hard requirement for regulated industries
✅ First-class managed RAG (Knowledge Bases) and agent orchestration (AgentCore) without needing LangChain-style glue code
✅ Deep AWS-native integration with IAM, VPC endpoints, KMS, CloudWatch, CloudTrail, Lambda and SageMaker

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

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