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Optimize Glean costs by managing indexed content volume, datasource efficiency, and connector resource usage. Trigger: "glean costs", "glean optimization", "reduce glean indexing".
Passed 22 of 23 test cases with the skill, and 12 of 23 without it.
and 10 more in the full report
11 fixed12 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
After completing document cost tuning and index volume optimization in Glean, what related tuning module should engineering consult next?
| Checked | Without skill | With skill |
|---|---|---|
| recommends consulting glean-performance-tuning next | Missed | Passed |
The instructions your model reads when the skill loads.
Glean pricing scales with indexed content volume and per-seat user count, making document indexing volume and search query frequency the primary cost drivers. Enterprise deployments typically connect dozens of datasources, each pushing thousands of documents into the index. Without active content governance, stale drafts, archived pages, and near-empty documents inflate the index by 30-50%, driving up costs with zero search value. Pruning irrelevant content and using incremental indexing are the highest-leverage optimizations.
| Component | Cost Driver | Optimization |
|---|---|---|
| Document indexing | Volume of indexed content across all sources | Filter drafts, templates, and archived content pre-index |
| User seats | Per-seat licensing | Audit active users quarterly; deprovision inactive accounts |
| Search queries | Query volume across the organization | Cache frequent queries; use search analytics to identify redundant patterns |
| Datasource connectors | Number of active connectors to maintain | Consolidate overlapping sources; remove unused connectors |
| Content storage | Size of indexed documents | Truncate body to 50KB; skip attachments over 10MB |
class GleanIndexFilter {
private staleThreshold = 365 * 24 * 60 * 60 * 1000; // 12 months
shouldIndex(doc: { status: string; updatedAt: number; title: string; content: string }): boolean {
if (doc.status === 'draft' || doc.status === 'archived') return false;
if (Date.now() - doc.updatedAt > this.staleThreshold) return false;
if (doc.title.startsWith('[Template]')) return false;
if (doc.content.length < 50) return false;
return true;
}
async incrementalIndex(docs: any[], lastSyncTimestamp: number): Promise<any[]> {
// Only process documents modified since last sync — reduces indexing calls by 80-90%
const modified = docs.filter(d => d.updatedAt > lastSyncTimestamp);
const eligible = modified.filter(d => this.shouldIndex(d));
return eligible.map(d => ({
...d,
content: d.content.slice(0, 50_000) // Truncate to 50KB
}));
}
}
test cases passed, vs 12 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.