Google Agent Development Kit (ADK) vs Seldon

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

	Google Agent Development Kit (ADK) Agents	Seldon Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Kubernetes-native MLOps platform for deploying and orchestrating ML and generative AI models in production.
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· Basic: $10 · Pro: $20 · Enterprise: Contact sales
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Multi-model (bring your own)
Editorial score	—	7.3 / 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	model-servinginference-pipelinesab-testingdrift-detectionllm-deploymentmlops
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	Mature Kubernetes-native serving with real-time pipelines Open-source core (Seldon Core 2, MLServer, Alibi) on GitHub Multi-model serving with memory overcommit cuts infra cost Strong observability, explainability, and drift-detection tooling Handles both classical ML and generative AI on one platform
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	Steep learning curve - assumes Kubernetes fluency Enterprise pricing is opaque and quote-only Overkill for single-model or small-team deployments Recent TrueFoundry consolidation muddies the product roadmap
Website	google.github.io	seldon.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 Seldon if

✅ Mature Kubernetes-native serving with real-time pipelines
✅ Open-source core (Seldon Core 2, MLServer, Alibi) on GitHub
✅ Multi-model serving with memory overcommit cuts infra cost
✅ Strong observability, explainability, and drift-detection tooling

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