One answer for people and agents
Your team asks Wren's GenBI agent. Claude, ChatGPT and your own agents call it over MCP. Same definitions, same permissions, same number, traceable to SQL.
vsWren AI vs. Glean
Glean excels at permission-aware answers across documents, apps and people, and now brings warehouse data into that context by delegating queries to Snowflake Cortex, Databricks Genie or BigQuery under each user's own credentials. It relies on the warehouse's semantic views or Genie spaces rather than a metric layer of its own. Wren AI is that layer: a governed data agent your team asks directly and any AI agent can call over MCP, answering from an open context layer where every number traces back to SQL.
Head to head
Wren AI vs. Glean, factor by factor.
Approach & intelligence
Governed semantic / context layer
Wren AI
MDL context layer plus knowledge (glossary, metric rules, NL-to-SQL pairs): one source of truth for humans and agents
Glean
Knowledge graph of content; metrics come from Snowflake semantic views or Genie spaces
Natural-language to SQL
Wren AI
Core capability across 20+ sources; asks a clarifying question when a request is ambiguous
Glean
Via Snowflake Cortex, Databricks Genie or Google's BigQuery MCP; read-only SQL mode
Agentic reasoning, skills + memory
Wren AI
Agentic Mode (generally available Sept 2026): sandboxed multi-step agent, reusable skills, persistent memory, streamed Agentic Mode API
Glean
Glean Agents: auto mode, triggers, step memory, Skills and a git-based lifecycle
Every answer traceable to SQL
Wren AI
Shows the SQL and a replayable thread trace; benchmarks score answers against ground-truth SQL
Glean
SQL visible in SQL mode; natural-language answers cite engine results
MCP / agent-ready API
Wren AI
Native MCP server: one org-level endpoint, OAuth sign-in, per-user security enforced server-side; listed in the Claude Directory
Glean
Remote MCP server (on by default), MCP gateway, agents as tools
Data & connectivity
Connects to your existing warehouse
Wren AI
BigQuery, Snowflake, Databricks, Redshift, Postgres, ClickHouse, Trino & 20+ more
Glean
Snowflake, Databricks (Genie) and BigQuery via per-user OAuth
Federated queries across sources
Wren AI
Through a federated engine you already run (Trino, Starburst, Athena) as a source; not turnkey cross-source joins
Glean
Combines warehouse results with documents; no cross-warehouse SQL
Queries live data, no copy or cutoff
Wren AI
Runs against live data in place; no extract or ingestion step
Glean
Live via partner engines; content is indexed
Governance & trust
One shared definition for humans + agents
Wren AI
Same MDL resolves every query in the web app, Slack, Teams, embeds, the API and MCP
Glean
Definitions live in each warehouse
Row / column-level security & access
Wren AI
OIDC identity; query-time row- and column-level policies applied per caller, including over MCP
Glean
Per-user warehouse OAuth; inherits source permissions everywhere
Grounded answers, bound to a governed model
Wren AI
Answers must resolve through the model; accuracy is measured with benchmarks and repaired via AI Advisor
Glean
Grounded by warehouse semantic views or Genie spaces; citations
SOC 2 / enterprise compliance
Wren AI
SOC 2 Type II, plus self-host / air-gap for full control
Glean
SOC 2 Type II, ISO 27001, ISO 42001, HIPAA
Openness & deployment
Open source / fully inspectable
Wren AI
Open-source context engine, MDL contract and MCP server; #1 GenBI on GitHub
Glean
Proprietary
Self-host / air-gapped option
Wren AI
OSS self-host, VPC and fully air-gapped on-prem deployments
Glean
Customer Hosted (formerly Cloud-Prem): Glean-managed in your GCP or AWS account
Config as code, git-native and versioned
Wren AI
MDL and knowledge live as YAML/Markdown in a git repo you own (Git Sync): diff, PR review, roll back
Glean
Agents-as-code via git ADLC (beta); platform settings in the console
No platform / ecosystem lock-in
Wren AI
Any warehouse, any model, any agent; clone your repo and leave at any time
Glean
Broad connectors, closed platform
Experience & economics
Built for non-technical business users
Wren AI
Ask in plain language in the web app, Slack or Teams; UI in seven languages
Glean
Assistant for every employee
Generative dashboards / GenBI apps in one prompt
Wren AI
GenBI Apps from one prompt, with dashboard filters and in-place edits; start from a Gallery template
Glean
Assistant generates dashboards, pages and HTML from data; no BI product
Embedded / white-label analytics
Wren AI
Embedded Threads (iframe), white-label AI APIs and MCP on the same context layer
Glean
Web SDK (chat, search) and Client API for internal apps
Transparent / accessible pricing
Wren AI
Usage-based cloud; concurrent-session self-host. No per-seat, no hidden cost
Glean
Quote-only; Core Suite (per seat) or Enterprise Flex (FlexCredits)
No per-seat fees, unlimited usersKey differentiator
Wren AI
Unlimited users; self-host is priced by concurrent sessions, never per seat
Glean
Per-user Core Suite, or credit-based Enterprise Flex
Delivered in Slack & your product
Wren AI
Slack, Microsoft Teams (Marketplace listing), embedded Threads and white-label API
Glean
Slack, Teams, Web SDK, and MCP into ChatGPT, Claude and Copilot
Verified September 25, 2026
Glean marks were checked against Glean's public documentation and pricing pages on September 25, 2026. Vendors ship constantly; if something here is out of date, tell us and we will re-check it.
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Why Wren AI
Why teams and their agents choose Wren AI over Glean.
A context layer you own
Open source, versioned as YAML and Markdown in your own git repo, across 20+ sources. No lock-in to one warehouse, cloud or model.
Runs where your data must stay
Wren Cloud, your VPC, or fully air-gapped on-premises. Regulated teams get agentic analytics without data leaving their walls.
Ships inside your product
White-label the agent in your own app, with row- and column-level security per customer, priced by usage, not by your users' seats.
Buyer questions
Wren AI vs. Glean, answered.
Not quite. Glean routes the question to Snowflake Cortex, Databricks Genie or BigQuery under each user's own credentials and combines the result with document context. Glean itself has no metric model, so consistency depends on the semantic views or Genie spaces your data team maintains. Wren AI is the governed layer across 20+ sources, with every answer traceable to SQL.
They do different jobs. Glean answers knowledge questions over documents and SaaS apps; Wren AI answers data questions over your warehouses with governed metrics. Many enterprises will run both, and because both speak MCP, Glean's agents can call Wren AI as their governed analytics tool.
Yes: SOC 2 Type II, role-based access, and every answer auditable back to SQL and a versioned model. The structural difference is control: Wren AI is open-source with true self-host and air-gapped deployment, while Glean offers SaaS or Customer Hosted (Glean-managed software running in your GCP or AWS account, which you cannot patch or modify yourself) with quote-only pricing, either per user or by FlexCredit consumption.
Compare on your own data.
The fairest benchmark is your data and your questions. Try it free in minutes, or let us run a head-to-head with your team.