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📖 The AI Tool Bible

Glean vs Google Agent Development Kit (ADK)

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

 
Glean
Agents
Google Agent Development Kit (ADK)
Agents
TaglineWork AI platform that unifies enterprise knowledge, search, and agentsGoogle's open-source framework for building, evaluating, and deploying production AI agents
CategoryAgentsAgents
PricingEnterprise· Enterprise pricing only; commonly reported in the $40-50/user/month range with a floor typically starting around 100 seats. No public self-serve tier. Contact sales for a quote.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).
ModelModel-agnostic: routes across 35+ LLMs including GPT-4o, Claude 3.5/4 Sonnet, Gemini 1.5/2, Llama 3, Mistral, plus Glean in-house modelsGemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM
Editorial score8.5 / 10—
Use cases
Enterprise search across Slack, Drive, Confluence and JiraCompany-wide AI assistant with citationsNo-code agent building for internal workflowsSales account and prospect research agentsEngineering on-call and incident-summary botsHR and IT helpdesk ticket triageNew-hire onboarding assistantExecutive briefing and meeting-prep summariesPolicy and compliance Q&A over internal docsKnowledge-graph-powered people and expertise finder
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
  • Deep, permissions-aware indexing of 100+ enterprise SaaS and on-prem sources out of the box
  • Model-agnostic: route between 35+ LLMs including OpenAI, Anthropic, Gemini, and open-source
  • No-code Agent Builder plus SDK/API for developers to ship custom agents
  • Strong governance: SOC 2 Type II, ISO 42001, HIPAA, GDPR, full audit trails and observability
  • Native surfaces in Slack, Teams, browser and mobile so adoption doesn't require a new app
  • Reference customers at scale (Booking.com, Zillow, Samsung, Rivian) validating large-enterprise readiness
  • Enterprise-grade knowledge graph goes beyond RAG with people, projects and topic signals
  • 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
  • Enterprise-only pricing and seat minimums put it out of reach for SMBs and individuals
  • No public pricing page - procurement cycles are long and quotes vary widely
  • Initial connector rollout, permission mapping and change-management can take weeks to months
  • Agent quality is bottlenecked by the messiness of the underlying enterprise data
  • Overlaps with newer offerings from Microsoft Copilot, Google Gemini for Workspace and ChatGPT Enterprise, forcing buyers into a platform decision
  • 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
Websitewww.glean.comgoogle.github.io
Pick Glean if
  • ✅ Deep, permissions-aware indexing of 100+ enterprise SaaS and on-prem sources out of the box
  • ✅ Model-agnostic: route between 35+ LLMs including OpenAI, Anthropic, Gemini, and open-source
  • ✅ No-code Agent Builder plus SDK/API for developers to ship custom agents
  • ✅ Strong governance: SOC 2 Type II, ISO 42001, HIPAA, GDPR, full audit trails and observability
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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