Context vs Glean
Glean pioneered enterprise AI search, but requires your data to live in their cloud. Context delivers the same intelligence with on-premise deployment, a knowledge graph, and full data sovereignty.
FEATURE COMPARISON
Comparison based on publicly available information as of 2026.
WHERE CONTEXT WINS
Why teams choose Context over
Glean
Full Data Sovereignty
Context runs entirely on your infrastructure. No data leaves your network. Deploy on-premise, in your VPC, or in air-gapped environments where Glean simply cannot operate.
Knowledge Graph Intelligence
Context automatically builds a knowledge graph with entity extraction, connecting people, projects, and concepts across your organization. Glean relies on keyword and vector search without graph relationships.
Predictable Costs at Scale
At $50/user/month, Glean costs $600K/year for 1,000 users. Context's infrastructure-based pricing means costs stay flat regardless of how many people search.
GPU Scale-to-Zero
Context scales GPU resources to zero when idle, dramatically reducing infrastructure costs. You only pay for compute when people are actively searching.
SECURITY & COMPLIANCE
Glean vs Context compliance
DEPLOYMENT OPTIONS
Context deploys where Glean cannot
DEPLOYMENT ARCHITECTURE
HONEST ASSESSMENT
Where Glean
fits well
100+ pre-built connectors for popular SaaS tools — fastest time to connect all your existing cloud apps
Mature SaaS platform with a large enterprise customer base and proven reliability at scale
Zero infrastructure management — Glean handles all hosting, updates, and scaling
WHY TEAMS SWITCH
Why teams move from
Glean to Context
Regulated industries (finance, healthcare, defense) need data to stay on-premise — Glean's cloud-only model is a non-starter
Per-seat pricing becomes unsustainable as organizations scale past hundreds of users
Teams want deeper intelligence from a knowledge graph, not just search results
Security teams require air-gapped or VPC deployments that Glean cannot support
Related Articles
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productAnatomy of an Enterprise-Ready AI Deployment
What separates an AI demo from an enterprise deployment? Security boundaries, compliance controls, integration depth, and deployment flexibility. Here is what enterprise-ready actually means.
enterpriseWhy Enterprise Knowledge Graphs Are the Future of Organizational Intelligence
Traditional enterprise search is broken. Knowledge graphs offer a fundamentally different approach to connecting organizational intelligence across teams, tools, and workflows.
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