| What it does | Open-source memory platform for AI agents
| Production AI at enterprise scale
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| Stage | Not given
| Established
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| Founded | Not given
| 2016
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| Based in | DE
| US
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| Pricing model | Not given
| Contact for pricing
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| Pricing | Free: $0/month with 1M tokens and 1 workspace (no card required). Standard: $1.00 per 1M tokens plus $5 per additional workspace. Enterprise: contact for pricing with bring-your-own-cloud deployment. Open-source engine available free to self-host.
| 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.
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| Key features | - Unified vector search, knowledge graphs, and relational storage
- Automatic knowledge graph generation from data sources
- Multi-tenant support with isolated workspaces
- Data source integrations (Slack, Notion, Google Drive, GitHub, etc.)
- Self-improving memory that learns from use
- Multiple deployment options (cloud, self-hosted, air-gapped)
- Agent framework integrations (LangGraph, CrewAI, OpenAI SDK, MCP)
- Support for multiple vector and graph databases
- GDPR compliance and encryption at rest and in transit
- Built-in graph visualization of company brain
| - 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
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| What makes it different | - Combines vectors with graph relationships and generated data models for connected context retrieval
- State of the art performance on BEAM benchmark (79% vs 73.4% at 100k tokens)
- Keeps memory costs flat as data grows compared to baseline LLM approaches
- Fully open-source with no database lock-in
- Updates memory as source data changes, not just static snapshots
| - 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
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| Free plan | Not given
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| Open source | Not given
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| Platforms | Not given
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| Public API | Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 70 ● no change since last check | 54 ● no change since last check |
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| Trust Flow (Majestic) | 14 | 16 |
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| Citation Flow (Majestic) | 43 | 38 |
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| Referring domains (Majestic) | 1089 | 361 |
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Ocient