AI Security
Cost Per Finding: Griffin AI vs Mythos
Token spend per scan is the wrong metric. Cost per actionable finding is the right one — and it's where engine-plus-LLM economics dominate pure-LLM economics.
Mar 8, 20265 min read
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Token spend per scan is the wrong metric. Cost per actionable finding is the right one — and it's where engine-plus-LLM economics dominate pure-LLM economics.
Scanning bursts when a monorepo merges. We explain why Griffin AI absorbs the spike gracefully while Mythos-class tools degrade into rate-limit queues.
A false positive is not free. It costs engineer attention, trust in the tool, and eventually the security programme's credibility. We price the difference.
One model for every task wastes budget on trivial work. Task-routed architectures match model capability to task requirements — the right lever for security at scale.
A shrinking triage queue is the clearest sign a security programme is working. We explain why Griffin AI shrinks queues and Mythos-class tools grow them.
The real cost of a scanner is not the subscription. It is the engineer hours lost to false positives, bad remediations, and noisy queues. We do the math.
Engine work parallelises cleanly. Model calls do not. We explain why Griffin AI's throughput scales with CPU while Mythos-class tools bottleneck on rate limits.
Prompt caching and engine memoisation combine to make Griffin AI scans repeat-cheap. Pure-LLM tools recompute the same reasoning on every run.
Opus for reasoning, Sonnet for drafting, Haiku for scale. We break down when each tier earns its keep and why single-model architectures cannot compete.
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