Cost-Optimized AI Security Scanning Infrastructure Layer

C6/10June 4, 2026

WhatA platform that intelligently orchestrates AI vulnerability scanning across codebases by managing token budgets, caching results, running incremental diff-only scans, and routing to the cheapest capable model per task.

SignalDevelopers are concerned about the cost of running AI agents for vulnerability discovery — estimating hundreds to thousands of dollars per scan — and are already thinking about strategies like scanning only diffs rather than full codebases to control spend.

Why NowMultiple frontier models now have strong code analysis capabilities, creating an arbitrage opportunity to route tasks to the most cost-effective model, and the reference harness from Anthropic has established a clear architecture pattern to build on.

MarketMid-market engineering teams (500-5000 devs) spending $50K-$500K/year on security tooling; competes with traditional SAST pricing but offers dramatically better detection; ~$2B addressable.

MoatProprietary cost-optimization data from running millions of scans reveals which models and prompting strategies yield best results per vulnerability class at lowest cost — a dataset no competitor can easily replicate.

Anthropic's open-source framework for AI-powered vulnerability discovery View discussion ↗ · Article ↗ · 472 pts · June 4, 2026

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