glean-cost-tuning

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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".

AI & Automation 2,266 stars 315 forks Updated today MIT

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# Glean Cost Tuning ## Overview 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. ## Cost Breakdown | 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 | ## API Call Reduction ```typescript class GleanIndexFilter { private staleThreshold = 365 * 24 * 60 * 60 * 1000; // 12 months shouldIndex(doc: { status: string; updatedAt: number; title: string; content: string }): boolean { if...

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Author
jeremylongshore
Repository
jeremylongshore/claude-code-plugins-plus-skills
Created
7 months ago
Last Updated
today
Language
Python
License
MIT

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