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Diffsift vs Oxyo

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

Diffsift compared with Oxyo
DetailDiffsift logo DiffsiftOxyo logo Oxyo
What it doesAI code review that runs locally on your machine Cut Claude Code, Codex & Gemini quota burn by 60–72% with persistent AI coding memory and deterministic verification
StageNot given Not given
FoundedNot given Not given
Based inGB RO
Pricing modelPaid Freemium
PricingPersonal: $59 (one-time). Team: $119 per seat (minimum 2 seats, one-time). Renewal: 50% of original price ($29 personal / $59 per team seat) for 12 months of updates. Explorer: free with 50 MCP calls; Oxyo Core: $19/mo (or $15/mo annually) with ~1.9M tokens/week and 300 MCP calls/week; Oxyo Pro: $49/mo (or $39/mo annually) with ~3.8M tokens/week and 1,250 MCP calls/week; Oxyo Max: $99/mo (or $79/mo annually) with ~7.5M tokens/week and 5,000 MCP calls/week. All pl
Key features
  • Proactive review inbox with Windows notifications
  • Multi-pass review for large PRs
  • Evidence-linked findings
  • Review profiles for different focus areas
  • Review history stored locally
  • Supports local and external model providers
  • Azure DevOps and GitHub integration
  • Encrypted credential storage
  • Offline license activation
  • Lifelong persistent memory across sessions retained in an isolated vault
  • Automatic belief revision to eliminate contradicted specifications
  • Deterministic Python sandbox for verified calculations
  • Model Context Protocol integration for IDE connectivity
  • Causal root-cause graph tracking
  • Visual learning from screenshots and diagrams
  • Reusable skill synthesis for algorithmic classes
  • Web console, desktop app, and REST API access
  • Encrypted BYOK vault for Claude, OpenAI, and OpenRouter
What makes it different
  • No code leaves your machine by default with local models
  • One-time purchase with perpetual fallback license
  • First-class Azure DevOps support
  • No telemetry or vendor lock-in
  • Tested with real models at actual performance speeds
  • 60–72% reduction in token quota burn measured on production 112k-token repositories
  • Zero re-sending of context; only ~1k surgical tokens retrieved per turn instead of 50k–200k
  • 100% contradiction-free memory with automatic outdated fact retirement
  • Unified memory across web, browser, and multiple IDEs
  • Model-independent brain; memory portable across AI providers
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)Not checked yet no change since last checkNot checked yet no change since last check
Trust Flow (Majestic)00
Citation Flow (Majestic)140
Referring domains (Majestic)20

6 details filled in for both companies.

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