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Self-hosting an open model is not a cheaper version of using an API. It is a different set of responsibilities. Here is what you actually take on, what you gain, and how to tell whether the trade is working.
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Run the cheap decisions locally and escalate only the hard ones. Here is the hybrid architecture, the routing question that makes it work, the cost model, and the failure mode that makes it dangerous.
Laya is free and local. Jev is hosted and more accurate out of the box. Both of those statements are true, and the choice between them is not obvious. Here is the comparison with the baselines named, including the benchmarks that are not comparable.
Fine-tuning is the one capability that makes an open decision model genuinely different from a hosted one. Here is how to build a label set from your own review decisions, train on a laptop, and avoid the trap that inflates every published benchmark.
Laya has no per-call cost, but self-hosting is not free. Here is the real cost model: latency by hardware tier, throughput planning, a break-even calculation, and the point where a hosted API is still the better choice.
Laya is a free, open-source decision model you can run on your own laptop. Here is what it does, how to install it, how to wire it into your agent, and the two failure modes that will bite you if you skip the setup.
Laya is a free decision model you can run on your own machine. This is the complete getting-started guide: what it does, how to install it, the eight SEO and GEO jobs it is good at, and the ones it is not.
The vendor benchmark says 67.8%. An independent reranking test says Jev is not a free win. Here is how to read both, why the numbers are not comparable, and how to run your own accuracy test before you trust a threshold.
Three myths that will get your site into trouble: that no hallucination means no errors, that a high confidence score means good work, and that cheap per call means cheap per decision. Here is the guardrail design that actually holds.
Jev is not a better writer. It is a cheap, fast judge that turns repeated SEO and GEO decisions into typed answers with confidence scores. Here is what it does, how to wire it into Codex, Claude Code, Hermes, OpenClaw, and Pi, and where it will quietly mislead you.
In a 2026 survey of 130+ search experts, brand and entity memory outranked llms.txt, chunking, and schema as the strongest driver of AI citations. Here is what that means for your GEO plan.
As more buyers ask AI assistants instead of searching Google, brands that are missing from AI answers disappear from their consideration set. Here is a four-layer GEO framework (technical readability, answer-shaped content, authority ecosystem, monitoring) that keeps your website visible in the conversation.
A practical framework for matching an AI agent platform to the job, evaluating controls, and rolling out production agents safely.
