Archon + Google Gemma
Archon routes managed AI workforce tasks to Google Gemma through Self-hosted weights or Google Cloud endpoint. Agents use lightweight private agents, local inference, domain-tuned workflows, governed by model policy, evals, fallback rules, usage controls, and audit logs.
AI Models
How Archon uses Google Gemma.
Teams use this model layer to route agent work to the right inference environment: frontier APIs for the hardest reasoning, managed model gateways for enterprise controls, and local or private runtimes when data boundaries, latency, or cost require it.
Lightweight private agents
Local inference
Domain-tuned workflows
Secure operating layer
Governed access, by default.
Model access is governed like any other production dependency. Archon scopes model policy, prompt boundaries, logging, fallback behavior, evals, cost controls, and where inference is allowed to run.
Model policy and routing
Archon defines when Google Gemma should run, what context it can receive, which tools it may call, and where fallback models take over.
Evals and release checks
Every production workflow gets quality gates, regression checks, hallucination review, and escalation paths before expansion.
Usage and audit controls
Token use, latency, prompts, retrieval context, model responses, and reviewer decisions are visible in the command center.
Related integrations
More in AI Models.
FAQ
Google Gemma questions.
How does Archon connect to Google Gemma?+
Can Google Gemma run privately or locally?+
How does Archon decide when to use Google Gemma?+
Get started
Put Google Gemma into a governed model routing plan with Archon.
Bring the workload, data boundary, latency target, quality bar, and approved deployment environment. We will map the model route, controls, evals, and first production workflow.