Have You Hit AI Indexing Limits with Large Data Sources?

Has anyone actually hit indexing limits when connecting large systems like OneDrive or Google Drive?

What teams are actually experiencing

Short Answer: Most enterprises don’t hit the limit first—they hit data quality and scope challenges long before that.

Even in environments with tens of millions of files, teams typically run into:

• slow or partial indexing

• permission-based gaps

• inconsistent coverage

• difficulty controlling what should be indexed

So while limits look restrictive on paper, they’re rarely the first blocker in practice.

Why the numbers look more alarming than they are

Raw file counts can be massive—but not all files become indexed objects. Across platforms, indexing is filtered by:

• connector scope (selected sites, folders, repositories)

• supported file types

• user permissions

• configuration choices

This means the actual indexed volume is usually far lower than total file counts.

What happens when limits are approached

Indexing becomes selective—it doesn’t fail all at once. Common behaviors:

• new content stops being indexed

• existing indexed content continues to work

• ingestion slows or pauses

Most platforms today avoid hard failures and instead cap growth quietly.

The real enterprise challenge is not limits

It’s signal versus noise. When organizations try to index everything:

• relevance drops

• duplicate or outdated content surfaces

• permissions fragment results

• user trust declines

This typically becomes a problem well before any formal limit is reached.

How platforms compare

Microsoft (Copilot + Graph):

• Designed to operate across very large M365 environments

• Practical limits are shaped by service boundaries (Graph APIs, search index, permissions)

• Indexing is broad, but not all content is equally retrievable or surfaced in AI experiences

• Large volumes still require scoping to maintain relevance and performance

Atlassian (Teamwork Graph):

• Uses explicit indexed object allowances tied to users and plans

• Encourages intentional scoping through enforced limits

• Prioritizes structured collaboration data over full-content ingestion

• Limits act as a visible constraint that forces prioritization early

Glean (enterprise search layer):

• Aggregates and indexes across many external systems

• Indexing scope depends heavily on each source system’s APIs and permissions

• Focuses on ranking and relevance rather than indexing everything equally

• Practical limits show up as partial coverage or connector constraints, not always hard caps

What this means in practice

Across all three approaches, limits show up differently:

• Explicit limits (e.g., object caps)

• Implicit limits (API access, connector constraints, performance trade-offs)

• Practical limits (relevance, usability, and trust in results)

The pattern is consistent: More data increases complexity faster than it increases value.

What experienced teams are doing instead

Successful teams:

• start with high-value, curated content

• limit connector scope intentionally

• avoid indexing entire drives by default

• refine based on usage and feedback

This improves both performance and adoption.

Where limits still matter

Limits become relevant when:

• large-scale ingestion is attempted without filtering

• multiple large data sources are connected at once

• teams expect full parity with their entire document ecosystem

In these cases, limits act as a forcing function to prioritize.

Takeaway

Enterprises are connecting massive data sources—but the first constraint isn’t the limit.

It’s how well the data is structured, filtered, and governed.

The better question isn’t: “Can we index everything?”

It’s: “What should we index to get useful, trustworthy results?”