Free plan with 200 credits per month for public repositories and core features; Pro plan at $16/month ($192 billed annually) with 4000 credits per month and private repository support; Enterprise plan with custom pricing and credits as needed
Flexible subscription, term, and perpetual licensing models. Organizations switching from conventional architectures see 60-80% reduction in total infrastructure cost. CPU-based pricing with no incremental ETL costs.
Key features
Connect agents with MCP Server for automatic documentation capture
Self-documenting pull requests
Sync existing documentation from Notion, Confluence, Coda, or repositories
Generate documentation on demand
Custom documentation templates
Shared knowledge layer for team-wide agent learning
Integration with Slack and MS Teams for context requests
Analytics dashboards and observability
AI on Enterprise Data - bring AI directly to governed enterprise data at petabyte scale
Security - built-in controls for access, governance, and compliance
Deployment Options - cloud, on-premises, or hybrid deployment flexibility
Data Ingest - continuously ingest and prepare high-volume data for AI-ready analytics
Query Optimization - run complex analytics on petabytes of data with predictable performance
Semi-Structured Data - give AI richer context with structured and semi-structured data in one platform
Megalane Parallelism - sub-second queries at petabyte scale
Zero Copy Reliability - no data duplication or redundant storage overhead
Compute Adjacent Data Store - computation happens where data lives
Smart Data Pipelines - continuously governed data pipelines replace manual ETL
What makes it different
Reduces agent token consumption by 50% through shared knowledge caching
Automatically maintains up-to-date documentation as code changes
Knowledge stays in human-readable Markdown format and is portable
Improves agent consistency across runs with shared knowledge layer
SOC 2 Type II certified for enterprise security compliance
Petabyte-scale performance without moving or duplicating enterprise data
60-80% reduction in infrastructure costs compared to conventional architectures
Production-grade AI delivery with expert implementation and solutions delivery teams
Flexible licensing models aligned to how enterprise AI programs actually scale
Unified platform for ingest, governance, query execution and optimization