Glean
Enterprise search with 100+ connectors. Personalised per-user knowledge graph. No governance layer.
At a glance
- Type
- Enterprise search + per-user KG
- Tier
- T1
- Section
- Enterprise-search adjacencies
- Created
- 2019-01
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Enterprise only
- Funding
- $765M total $7.2B val Series F · 2025-06
Taxonomy
- storage
- vector
- retrieval
- similarity
- persistence
- long-term
- update
- extraction
- unit
- document
- governance
- auditable
- conflict
- none
When to use
Optimised for: enterprise connectors + entitlements + governance + RAG-grounding
Anti-fit: not for SMB / consumer use cases
Pros & cons
Pros
Most polished enterprise AI search product — connectors, governance, ranking, and conversational interface tightly integrated; high enterprise NPS.
Cons
Enterprise pricing; closed product so memory primitives aren't exposed to developers building agents.
Claims & capabilities
$200M ARR (December 2025) — doubled in 9 months; >$1M contract segment nearly tripled; avg employee runs 5 queries/day; 40% wDAU/wMAU (>2x SaaS benchmark); customers consume 20T+ tokens/year (doubled in past quarter). Reference customers include Databricks, Canva, Confluent, Duolingo, T-Mobile, Reddit, Sony Electronics
Technical surface
- API surface
- REST, SDK: Python, JS/TS
- Backend storage
- custom
- Deployment
- Both
- Embedding model
- locked
- Multi-tenancy
- Logically isolated single-tenant per customer (data, models, telemetry siloed; no shared vector index); option for fully isolated single-tenant in customer AWS/Azure/GCP
- MCP
- native (first-party) — Glean MCP server
- A2A
- no Google A2A (Agent2Agent) integration documented as of 2026-05.
- OpenTelemetry
- no first-party OpenTelemetry exporter documented; standard logs/metrics typically available.
Compare Glean with…
Similar systems
Other enterprise-search adjacencies in the catalog, ranked by inbound references.
- Algolia (NeuralSearch) T1
NeuralSearch combines vector + keyword via neural hashing — compresses to 1/10th size while retaining 99% info. AI-powered personalisation + recommendations.
- Clarivate T1
Bibliographic metadata curation (Web of Science, Derwent, Cortellis). Human editorial governance + journal-deindexing. Memory-adjacent — included as a curated-knowledge baseline.
- Coveo T1
RAG-as-a-Service for AWS (Dec 2025) via hosted MCP server grounding Amazon Bedrock agents in enterprise knowledge. Passage retrieval + answer generation + ranked search + fetch in one API.
- Lucidworks Conversational Q&A AI Agent T1
Enterprise Q&A agent powered by Luci patent-pending ultra-precise RAG. Embeds on product detail pages; consumes technical PDFs, spec sheets, images, tables, charts, graphs and product manuals. Maintains session history for multi-turn follow-ups; refuses out-of-scope queries via prompt-injection guard.
- Meilisearch T2
Semantic + hybrid search GA (2025). Automatic embedding generation + caching via OpenAI / HuggingFace / Ollama. Multi-modal (text + images); hybrid rank fusion; conversational RAG built in.
- Mindbreeze InSpire T2
Hybrid keyword + vector retrieval with entitlement-aware filtering. Unified enterprise knowledge graph linking documents, tickets, records. RAG prompt orchestration built in.