AI Security
Griffin AI vs Self-Hosted Llama: Real Costs
Self-hosting Llama looks cheap on paper. The real costs — GPUs, operations, engineering — make the comparison less obvious than the list price suggests.
Mar 15, 20263 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.
Self-hosting Llama looks cheap on paper. The real costs — GPUs, operations, engineering — make the comparison less obvious than the list price suggests.
Open-weight models let you run everything locally. The tradeoff is quality, cost, and operational overhead. Griffin AI provides a different answer to the same on-prem need.
Fine-tuning an open-weight model sounds like a shortcut to a custom SecOps copilot. In practice, it is one step of a much longer journey.
Gemma is built for efficiency. Can a small open-weight model replace Griffin AI for lightweight scanning workflows, or does the engine still matter?
DeepSeek Coder has become a favourite for code-focused workloads. This is how it compares to Griffin AI when the job is security review, not code generation.
Qwen's open-weight models have strong code benchmarks. We dig into how they compare to Griffin AI when the workflow is real code security, not just leetcode.
Mistral Large is a strong reasoning model, but remediation is more than generating a diff. We look at what Griffin AI adds for production fix workflows.
Llama 3 is a powerful open-weight foundation model, but security workflows demand more than raw inference. Here is how Griffin AI compares.
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