Combined vs Glean for cross-app business analysis

Compare Combined and Glean for cross-app analysis: SaaS ingestion, warehouse SQL, MCP tools, source permissions and a runnable Salesforce–Stripe evaluation.

By the Combined team · September 10, 2026

Choose Combined when your existing agent needs connected SaaS records and a managed SQL layer. Choose Glean when your organization wants enterprise knowledge, agents and tools in a shared experience, including access to existing warehouse data. Both support MCP; the useful decision is where your business records become queryable and who operates that path.

Choose around the data you have today

Your agent can write an excellent query and still lack the records to answer it. If your first project is “Compare our Salesforce pipeline with Stripe payments,” Combined gives you the connection, managed storage and governed query surface together. You choose the sources and business definitions; Combined handles making the selected records available to your agent.

If those records already live in a governed warehouse, evaluate that existing path. Glean documents Snowflake analysis through direct SQL, semantic views and supported Cortex Agents. It can put warehouse answers alongside the organization's broader knowledge and workflows. See its Snowflake setup.

Who supplies each part of the analysis?
Responsibility	Combined	Glean
Business records	Connect supported sources and maintain selected datasets.	Use connected enterprise content, live tools or an existing warehouse.
SQL execution	Managed read-only SQL over granted logical datasets.	Query configured warehouse resources through supported integrations.
Agent experience	Use the external agent you already prefer.	Use Glean's experience or expose capabilities to external MCP hosts.
Team-owned decisions	Source selection, grants, entity mappings and metric definitions.	Connected resources, tool configuration, permissions and analytical definitions.

The comparison follows Combined's data interface, Glean's MCP configuration and its Salesforce retrieval methods. Focus your evaluation on the path your team will actually use, from source records to a reviewable answer.

Glean supports SQL and MCP; Combined supplies the data layer

Glean can run read-only BigQuery SQL through Google's official MCP server. Users authenticate with their Google accounts, and access follows Google Cloud permissions within the connected project. That is a real structured-data route. It still starts with data prepared in BigQuery. See Glean's BigQuery integration.

Glean's MCP servers can include search, document access, eligible agents and external read/write tools. There is a specific distinction: a Glean agent exported as one MCP tool cannot contain write tools or human-in-the-loop steps, including in sub-agents. External gateway tools have their own surface. See available server tools and exported-agent constraints.

Combined is built for the step before the analytical question: connect the business apps, commit their selected records, and expose discoverable datasets. Your agent can aggregate each source independently and join the results in one bounded SQL statement. You get that query service without separately assembling its ingestion and storage. Explore the connector library and verify the connection route and fields for your apps.

Separate permission to read from the identity used to join

Combined binds access to an Account and explicit Source grants, optionally narrowed to datasets. Separate provider tenants remain separate Sources, and ordinary grants do not inherit future connections. Grant the evaluation agent the intended production sources, leaving sandbox data outside its scope. See Combined's access model.

Glean's access path depends on the resource. Its Salesforce connector distinguishes indexed record sharing from live per-user OAuth retrieval and documents limitations for indexed field-level security. Its Snowflake setup supports user OAuth or a separately configured shared service identity. Review the chosen Salesforce path or warehouse identity.

Neither permission model decides that Stripe customer cus_123 belongs to a particular Salesforce account. Maintain that map explicitly, include the billing account boundary, and surface duplicates or missing mappings. Account names make convenient display labels; a maintained ID relationship makes a dependable join.

Evaluate Salesforce pipeline alongside Stripe billing

Start with our Salesforce–Stripe worked guide. Its SQL fixture and expected result use synthetic, normalized teaching records you can run in DuckDB. They establish the arithmetic to check when you adapt the calculation to discovered datasets.

The example uses USD, current open opportunities, and paid invoices from June 12 inclusive to September 10 exclusive, 2026 UTC. Atlas should have $20,000 pipeline, $3,000 paid on qualifying invoices and $700 outstanding: $500 overdue plus $200 without a due date. Birch retains $6,000 pipeline with zero billing amounts in this complete sample.

Connect Salesforce and Stripe in Combined, verify successful syncs, then discover the granted schemas.
Map customers to accounts and aggregate opportunities and invoices separately before joining.
Keep overdue and undated balances separate. Payments on still-open invoices are outside this example's paid-invoice measure.
For Glean, prepare the same inputs in an authorized warehouse or explicitly designed tool path, then evaluate the same definitions.

Discover my granted CRM and billing datasets and check their freshness.
Show current open pipeline and paid-invoice amounts by account and
currency for an agreed reporting interval. Aggregate the two record
sets before joining. Report outstanding, overdue and undated balances
separately. Return definitions, SQL, mapping exceptions and supporting
records; distinguish missing coverage from a verified zero.

Retain the implementation steps as well as the answer. How much work was needed to connect the sources, prepare the data and expose the query? You can download the platform evaluation worksheet to record those decisions. Keep the calculation fixed while you compare the setup work and supporting evidence from each implementation.

Common evaluation questions

Does a Glean connector make all its records available for cross-app SQL?

Choose an explicit SQL resource and verify its inputs. Glean's documented warehouse integrations query configured warehouse data; the presence of an indexed SaaS connector does not establish that the same records have become relational tables there. See the BigQuery query path.

Should a team with a working warehouse move its data?

First test the existing route. Combined is particularly useful when your next sources still need a maintained analytical connection and your team wants to keep using its current agent. Compare the work remaining for the actual question.

Does Combined host the agent or perform application writes?

Combined supplies managed data and read-only agent access. Your chosen agent handles reasoning; a separate authorized tool handles record changes. See the MCP reference.

Give your agent a working business-data connection

Open the Salesforce and Stripe plan to get the records, identity checks and agent assignment together. Then start with Combined, connect the relevant sources and make your first successful query. The initial 5 million MAR allowance needs no card.

Sources and further reading

Explore the documentation behind this guide. Product details checked on September 10, 2026.