Exabase
Gives AI agents a real memory and a private workspace so they stop forgetting everything between conversations and re-reading the same documents over and over.
🔗 Visit Exabase
Description
Ask an AI agent something today, and by tomorrow it's forgotten everything you told it — every preference, every fact, every correction — unless a developer has built a custom system to store and retrieve that context, which usually means re-processing the same documents over and over and paying for the same tokens repeatedly. Exabase is infrastructure that solves that specific problem: it gives AI agents a persistent memory that extracts facts, preferences and events automatically, plus a private cloud workspace of files and notes the agent can search and reuse, so the same context doesn't have to be rebuilt every session.
Developers integrate it via SDKs, REST or CLI. Its memory engine builds what it calls a "living ontology" — tracking relationships between concepts and consolidating knowledge over time rather than just storing flat logs — with semantic and keyword search, sub-300ms response times, and automatic resolution of contradictory facts. Each customer gets isolated, versioned storage with rollback, and the whole thing is built for production use with AES-256 encryption at rest, SSL in transit, and a 99.9% uptime SLA. The company's own case study claims an 81% reduction in token costs (from $14,900/month to $2,800/month) by not re-sending the same context repeatedly.
💬 Our review
The short version: if you're building an AI agent that's supposed to remember things — a support bot, a coding assistant, a sales copilot — and you're currently duct-taping that with your own vector database, this is a purpose-built layer for exactly that job, with a genuinely compelling cost argument.
Against raw vector databases like Pinecone, Weaviate or Qdrant, Exabase's pitch isn't storage — it's the memory logic on top: fact extraction, contradiction resolution, and an evolving ontology instead of you writing that layer yourself on top of a bare vector store. That's real engineering time saved if you're building from scratch, but it also means more vendor lock-in than a generic database, since the memory logic itself lives inside Exabase rather than your own code. Pricing transparency is the weak point — beyond a free tier, exact costs aren't published, so the advertised 81% savings case study is hard to independently verify against your own workload. Worth evaluating if you're past the prototype stage and token costs from re-sent context are becoming a real budget line item.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Palier gratuit disponible ; tarifs détaillés non publics
Pros
Mémoire persistante avec ontologie évolutive
Réponse sub-300ms
Isolation et versioning par client
Cons
Tarifs non publics
Vendor lock-in sur la logique mémoire
Cas d'usage difficile à vérifier indépendamment