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warm-rust vs Exabase

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

warm-rust compared with Exabase
Detailwarm-rust logo warm-rustExabase logo Exabase
What it doesHybrid search, graph indexing, and vector-symbolic memory for AI agents Context infrastructure for AI agents
StageStartup Startup
Founded2026 Not given
Based inUS GB
Pricing modelOpen source Freemium
PricingOpen source under Apache-2.0 license. WARM Cloud is in development with usage-based pricing for sync, backup and verification; details to be published at launch. Not given
Key features
  • Hybrid search combining BM25 lexical and vector scoring
  • Vertex graph vector index with 2.29-2.75x throughput vs hnswlib
  • Vector-symbolic fact memory with holographic reduced representations
  • AES-256-GCM encryption at rest with Argon2id key derivation
  • Signed audit log with Ed25519 and W3C DID identity
  • Secure purge that overwrites deleted data with zeros
  • Crash-safe append-only log with checksumming
  • Native Rust API with Python HTTP client and Node.js bindings
  • MCP server integration for editors and AI agents
  • WARM Cloud sync, backup and verification without server access to keys or documents
  • 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
  • Runs entirely locally in your process with no network calls or telemetry
  • Encryption at rest built into the open-source engine itself, not an enterprise add-on
  • Significantly faster vector search than hnswlib with graph-indexed vertex retrieval
  • Signed provenance and verified audit trail using C2PA, COSE and SCITT standards
  • Embedded library model eliminates dependency on external services or vendor lock-in
  • 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)26 no change since last check29 no change since last check
Trust Flow (Majestic)1211
Citation Flow (Majestic)2432
Referring domains (Majestic)56122

6 details filled in for both companies.

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