Google Agent Development Kit (ADK) vs Relevance AI

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

	Google Agent Development Kit (ADK) Agents	Relevance AI Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Build and deploy an AI workforce of specialized agents across your business tools
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).	Enterprise· Free trial available via the app. Paid tiers are quote-based (Enterprise): custom actions, unlimited agents/tools/users, dedicated account manager. Reported customer benchmarks cite an average cost of ~$0.09 per task at scale; no fixed public tier pricing.
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Multi-model: Claude (Opus/Sonnet/Haiku), OpenAI GPT, Google Gemini, plus open-weight options (Kimi K2, GLM)
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	Outbound prospect research and personalisationMeeting prep and CRM hygieneSales call summarisation and follow-upDeal-review and pipeline QA copilotsCustomer support ticket triageInternal knowledge-base RAG assistantsApplicant screening and recruiter workflowsCompetitive intelligence briefingsMarketing content and campaign operationsCross-SaaS data reconciliation
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	No-code visual builder gets non-engineers shipping usable agents quickly Model-agnostic routing across Claude, GPT, Gemini and open-weight models lets you optimise cost vs. quality per step Very large integration catalogue (1,000+ apps) including Salesforce, HubSpot, Slack, Gmail Built-in evaluation and cost-monitoring framework — rare in this category and important for production use Enterprise controls (SOC 2, GDPR, data residency, audit logs, RBAC) are actually present, not roadmap items Strong library of pre-built agent templates for sales, CS, and ops workflows Sub-agent and tool-composition model supports non-trivial multi-step automations
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	Pricing is opaque and sales-gated; hard to budget without a demo call Positioning and templates lean heavily toward GTM/enterprise use cases — less obvious value for solo devs or hobbyists Deep customisation still benefits from a technical operator; fully non-technical users hit ceilings on complex flows Runtime cost can escalate quickly if agents fan out across expensive frontier models without careful routing As a hosted platform, you cede orchestration and observability to a third party rather than owning the stack
Website	google.github.io	relevanceai.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 Relevance AI if

✅ No-code visual builder gets non-engineers shipping usable agents quickly
✅ Model-agnostic routing across Claude, GPT, Gemini and open-weight models lets you optimise cost vs. quality per step
✅ Very large integration catalogue (1,000+ apps) including Salesforce, HubSpot, Slack, Gmail
✅ Built-in evaluation and cost-monitoring framework — rare in this category and important for production use

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