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Vectorize vs Exabase

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Vectorize compared with Exabase
DetailVectorize logo VectorizeExabase logo Exabase
What it doesAgent memory that learns from experience Context infrastructure for AI agents
StageStartup Startup
Founded2024 Not given
Based inUS GB
Pricing modelFreemium Freemium
PricingSelf-hosted: Free, MIT licensed. Hindsight Cloud: Pay-as-you-go with free credits to start. Retain (store memories) $10.00 per million tokens, Recall (retrieve memories) $0.75 per million tokens, Reflect (synthesize patterns) $0.05 per call, Iris Extract (structured extraction) $7.50 per million tok Not given
Key features
  • Per-user persistent memory with preferences, history and decisions kept separate
  • Cross-session persistence allows context to survive weeks between interactions
  • Fast parallel retrieval in under 100ms through four strategies running simultaneously
  • Works with any LLM without losing learned context when swapping models
  • Learns from mistakes and corrects behavior on repeated problems
  • Automatic pattern detection synthesizes individual facts into consolidated knowledge
  • Conflict detection preserves both old and new states with timestamps when facts change
  • Integrates with 20+ coding agents including Claude Code, Cursor, and Copilot
  • Memory system with self-managing advanced memory
  • Isolated per-tenant instances with version rollback
  • Portable context server with files, notes and links
  • Sub-document multi-modal hybrid search
  • Structured data extraction from PDFs, websites and images
  • Autonomous agents for knowledge base automation
  • 99.9% uptime SLA
  • AES-256 encryption at rest and SSL in transit
  • TypeScript SDK and REST API
  • Model-agnostic and framework-agnostic
What makes it different
  • 94.6% retrieval accuracy on LongMemEval-S, beating next-best system at 74.0%
  • MIT licensed open source with no usage limits, telemetry or restrictions
  • Reflection layer automatically builds judgment and mental models over time without manual tagging
  • Entity resolution handles hundreds of entities with 99.5% query reduction
  • Cut token spend by up to 81% through persistent agent memory instead of full context window stuffing
  • State-of-the-art on two leading AI memory benchmarks
  • Full-stack data layer eliminating need for multiple services
  • Production-tested at scale powering Fabric platform
  • Self-improving memory that tracks relationships and resolves contradictions
Free planNot given Not given
Open sourceNot given Not given
PlatformsNot given Not given
Public APINot given Not given
Community votes00
DR (Domain Rating by Ahrefs)68 +1 (up) since last check29 no change since last check
Trust Flow (Majestic)1811
Citation Flow (Majestic)4732
Referring domains (Majestic)1378122

6 details filled in for both companies.

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