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Recalld vs Vectorize

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

Recalld compared with Vectorize
Detail RecalldVectorize logo Vectorize
What it doesThe memory layer for AI Agents Agent memory that learns from experience
StageGrowing Startup
FoundedNot given 2024
Based inNot given US
Pricing modelFreemium Freemium
PricingNot given Self-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
Key features
  • Long-term memory for AI agents
  • Automatic fact extraction and reconciliation
  • Curated recall retrieval
  • Raw vector search capability
  • Model Context Protocol integration
  • Regional data residency
  • End-to-end encryption
  • Right to erasure
  • Bring-your-own-key model support
  • Recalld Chat assistant
  • 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
What makes it different
  • Curated recall uses 6.7× fewer tokens than raw search while maintaining accuracy
  • Automatic fact updating and reconciliation without manual deduplication pipelines
  • Verified on open benchmark maintained by independent company
  • Native MCP server requiring no SDK or glue code
  • GDPR-compliant by design with transparent data handling
  • 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
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)7 −1 (down) since last check68 +1 (up) since last check
Trust Flow (Majestic)018
Citation Flow (Majestic)1547
Referring domains (Majestic)101378

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