Glean vs SearchUnify
Glean vs SearchUnify: side-by-side comparison of two enterprise-search adjacencies systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Where they differ (8)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Glean | SearchUnify | |
|---|---|---|
| Type | Enterprise search + per-user KG | Agentic enterprise search + KCS |
| Created | 2019-01 | 2007 |
| Funding | $765M total $7.2B val Series F · 2025-06 | SearchUnify (a Grazitti Interactive product) — privately held; funding undisclosed; ~200 staff per LinkedIn. |
| Backend storage | custom | searched not found |
| Deployment | Both | Managed-only |
| API surface | REST, SDK: Python, JS/TS | REST |
| 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 | hard-isolation |
| MCP | native (first-party) — Glean MCP server | yes — official SearchUnify MCP server (su-mcp) exposing search and analytics tools |
At a glance
| Glean | SearchUnify | |
|---|---|---|
| Section | Enterprise-search adjacencies | Enterprise-search adjacencies |
| Tier | T1 | T1 |
| Type | Enterprise search + per-user KG | Agentic enterprise search + KCS |
| Created | 2019-01 | 2007 |
| Pricing | Enterprise only | Enterprise only |
| Funding | $765M total $7.2B val Series F · 2025-06 | SearchUnify (a Grazitti Interactive product) — privately held; funding undisclosed; ~200 staff per LinkedIn. |
| Backend storage | custom | searched not found |
| Deployment | Both | Managed-only |
| API surface | REST, SDK: Python, JS/TS | REST |
| Embedding | locked | 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 | hard-isolation |
| MCP | native (first-party) — Glean MCP server | yes — official SearchUnify MCP server (su-mcp) exposing search and analytics tools |
| A2A | no Google A2A (Agent2Agent) integration documented as of 2026-05. | no Google A2A (Agent2Agent) integration documented as of 2026-05. |
| OpenTelemetry | no first-party OpenTelemetry exporter documented; standard logs/metrics typically available. | no first-party OpenTelemetry exporter documented; standard logs/metrics typically available. |
| Optimised for | enterprise connectors + entitlements + governance + RAG-grounding | enterprise connectors + entitlements + governance + RAG-grounding |
| Anti-fit | not for SMB / consumer use cases | not for SMB / consumer use cases |
Pros & cons
Glean
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.
SearchUnify
Pros: Strongest in customer support / KCS workflows — memory tuned for case deflection and resolution-knowledge feedback loops.
Cons: Customer-support focused; less applicable to general agent memory; smaller mind-share than Glean.