Count vs Google Agent Development Kit (ADK)

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

	Count Agents	Google Agent Development Kit (ADK) Agents
Tagline	Collaborative AI-powered data canvas that blends SQL, Python, and natural-language agents for team analytics.	Google's open-source framework for building, evaluating, and deploying production AI agents
Category	Agents	Agents
Pricing	Freemium· Free; Pro $49/editor/mo; Scale $69/editor/mo (15-seat min); Enterprise custom	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, OpenAI, Google)	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM
Editorial score	6.9 / 10	—
Use cases	collaborative-analyticsai-data-explorationself-serve-bimetric-modelingsql-notebooks	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	Canvas model keeps AI work auditable rather than locked in chat threads Unlimited viewer seats make org-wide rollout affordable MCP and API support for connecting warehouses and external tools Mixes SQL, Python, and agent prompts in a single workspace	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	Closed source and cloud-only, no self-host option Per-editor pricing escalates quickly for larger analyst teams Scale tier requires a 15-seat minimum commitment	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	count.co	google.github.io

Pick Count if

✅ Canvas model keeps AI work auditable rather than locked in chat threads
✅ Unlimited viewer seats make org-wide rollout affordable
✅ MCP and API support for connecting warehouses and external tools
✅ Mixes SQL, Python, and agent prompts in a single workspace

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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