AI Security
Griffin AI vs Gemini On-Device: Developer Tools
Gemini on-device models are fast and cheap. For the developer-tool layer, they're useful. For the engine-plus-LLM layer, on-device is not the right fit.
Mar 20, 20262 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.
Gemini on-device models are fast and cheap. For the developer-tool layer, they're useful. For the engine-plus-LLM layer, on-device is not the right fit.
Gemini has FedRAMP-authorised deployment options. Griffin AI builds on FedRAMP-aligned infrastructure. The comparison is about what the customer has to build.
Gemini's pricing table favours long-context workloads. Security scans have long-context structure. The question is how much context fits into the architecture.
Gemini's multimodal capabilities are genuinely useful for some security workflows. For most security workflows, the modality is code and text, not images.
Vertex AI Safety is Google's approach to enterprise AI controls. For security-specific workflows, Griffin AI adds grounding the Safety layer doesn't.
Gemini's function calling is strong and flexible. Griffin AI's tool layer is narrow and opinionated. For security workflows, the opinionated approach wins.
Gemini's million-token context window is a genuinely new capability. For security analysis of large codebases, is it enough on its own?
Gemini Code Assist makes developers faster. But faster is not safer. Here's how Griffin AI layers a security engine onto the same developer workflow.
Gemini Ultra sets a high bar on complex reasoning benchmarks. But security reasoning is not benchmark reasoning. Here's how Griffin AI's engine-first approach changes the outcome.
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