Google Agent Development Kit (ADK) vs Palantir AIP

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

	Google Agent Development Kit (ADK) Agents	Palantir AIP Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Enterprise AI platform that grounds LLMs in your operational data and runs agents against real business systems.
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· Contact sales; typically bundled with Foundry
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Multi-model (GPT, Claude, Llama, customer-hosted)
Editorial score	—	8.4 / 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	enterprise-agentsoperational-aidefense-and-intelsupply-chainhuman-in-the-loop-automation
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	Grounds LLMs in a governed Ontology of real enterprise data and actions Model-agnostic; supports air-gapped and classified deployments Strong human-in-the-loop, permissioning, and audit controls AIP Logic and Agents make multi-step operational workflows tractable Battle-tested in defense, industrials, and Fortune 500 ops
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	Enterprise-only pricing, no self-serve tier Requires Foundry/Ontology investment to unlock real value Long procurement and implementation cycle Overkill for teams that just need a chatbot or RAG prototype
Website	google.github.io	www.palantir.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 Palantir AIP if

✅ Grounds LLMs in a governed Ontology of real enterprise data and actions
✅ Model-agnostic; supports air-gapped and classified deployments
✅ Strong human-in-the-loop, permissioning, and audit controls
✅ AIP Logic and Agents make multi-step operational workflows tractable

Compare a different pair →