Glean vs Google

The Knowledge Layer vs. the Full Stack

The most important distinction between Glean and Google DeepMind is scope. Glean solves one problem exceptionally well: making an organization's scattered knowledge accessible, searchable, and actionable through AI. Google DeepMind operates across the entire AI stack — from custom silicon (TPUs) through foundation models (Gemini) to consumer-facing products (Search, Workspace) and open protocols (A2A). This makes them less direct competitors than complementary forces. Glean frequently runs on top of Google's models and infrastructure, while Google's enterprise offering (Gemini Enterprise) competes with Glean for the same procurement budgets.

Enterprise Search: Depth vs. Ecosystem

In enterprise search specifically, Glean holds a meaningful advantage in cross-platform integration. With 100+ connectors and real-time indexing, Glean can unify knowledge from Slack, Confluence, Salesforce, Jira, and dozens of other tools into a single permission-aware search experience. Google's Gemini Enterprise now supports blended search across third-party sources like Microsoft 365 and ServiceNow, but its natural strength remains within the Google Workspace ecosystem. For organizations deeply embedded in Google's tools, Gemini Enterprise offers tighter native integration. For heterogeneous tool environments — which describes most large enterprises — Glean's connector breadth is a significant differentiator.

Model Strategy: Opinionated vs. Agnostic

Google builds its own frontier models and designs custom chips to train them. Glean deliberately avoids this, positioning itself as a model-agnostic abstraction layer. Enterprises using Glean can switch between or combine OpenAI, Google, and Anthropic models without re-architecting their knowledge infrastructure. This is a powerful hedge against model commoditization — and a key reason Glean's $200M ARR has grown so quickly. Google's approach, by contrast, creates deep vertical integration: TPU-trained models served on GCP, embedded in Workspace, and connected via A2A. The trade-off is lock-in for performance optimization.

Agents and the Agentic Economy

Both companies are investing heavily in AI agents, but from different starting points. Glean's agents are grounded in enterprise knowledge — they can answer questions, draft documents, and automate workflows using the full context of an organization's data. Google's agent strategy is more infrastructural: the A2A protocol enables inter-agent communication across platforms, while the ADK provides the framework for building multi-step agents. Google's AI co-scientist — a multi-agent system that can accelerate hypothesis development from years to days — represents a category of agent capability that Glean does not attempt. In the agentic economy, Google provides the protocols and infrastructure; Glean provides the enterprise knowledge that makes agents actually useful in a business context.

Scale, Resources, and Competitive Moats

Google DeepMind has resources that no startup can match: YouTube as a training data corpus, custom TPU infrastructure, and distribution through products used by billions. Glean's moat is different — it's the permission-aware knowledge graph built from an enterprise's specific data. Once Glean is deeply integrated into an organization's tool stack with 100+ connectors indexed and agents deployed, switching costs become substantial. Glean's $200M ARR (doubling in nine months) and 3x growth in $1M+ contracts suggest that this moat is real and deepening. Google's competitive advantage is breadth and vertical integration; Glean's is depth and specificity within the enterprise knowledge layer.

The Interdependence Question

Perhaps the most interesting dynamic is how these two companies depend on each other. Glean uses Google's Gemini models (among others) as its inference engine. Google's A2A protocol could become the communication layer that Glean's agents use to interact with other agent systems. As the agentic economy matures, this relationship may deepen: Google builds the roads and the engines, while Glean builds the enterprise-specific navigation system. The risk for Glean is that Google's Gemini Enterprise continues to expand its third-party connector support, eventually matching Glean's integration breadth. The risk for Google is that the model-agnostic abstraction layer becomes more valuable than any single model — and Glean owns that layer for the enterprise.