Vectorize vs briq

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 briq
Detail	Vectorize	briq
What it does	Agent memory that learns from experience	Cloud sandboxes and services for builders and agents
Stage	Startup	Not given
Founded	2024	Not given
Based in	US	FR
Pricing model	Freemium	Freemium
Pricing	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	Hobby (Free): 5 sandboxes & services at once, 16 GB of memory, pico/micro/nano/small/medium sizes, 10 GB disk. Pro (€20/month): €20 usage included monthly, 50 sandboxes & services, 80 GB memory, 5 members, all sizes, 250 GB disk, always-on briqs. Team (€100/month): €100 usage included monthly, 200 s
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	Linux sandbox with shell, persistent files and development tools Multiple services: Postgres, Redis, DuckDB, custom OCI images MCP integration with Claude, Cursor, VS Code Environment forking with data preservation Idle workload sleep mode with compute billing pause Private networks with public endpoint selection Spend caps, size limits and concurrency controls per credential Audit log for reviewing all actions
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	Built specifically for AI agents with native MCP protocol support Open-source runtime in public development with Rust and Firecracker Integrated services ecosystem with private networking
Free plan	Not given	Not given
Open source	Not given	Not given
Platforms	Not given	Not given
Public API	Not given	Not given
Community votes	0	0
DR (Domain Rating by Ahrefs)	68 +1 (up) since last check	18 −1 (down) since last check
Trust Flow (Majestic)	18	0
Citation Flow (Majestic)	47	20
Referring domains (Majestic)	1378	24

6 details filled in for both companies.

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