How Much Does LLMO / AI Search Optimization Cost in Japan? (2026)
“We want to invest in LLMO (AI search optimization), but we can’t tell what it should cost.” This is the question we hear most from marketing managers who hold the budget. The category is young, few vendors publish rates, and even after you collect quotes it’s hard to judge whether a number is fair.
This article maps the 2026 cost benchmarks for LLMO / AIO optimization by engagement type, using publicly available pricing. It then covers what drives the price, what you should actually receive at each level, and the traps to avoid — written from the perspective of a team that diagnoses and implements AI visibility as day-to-day work, not just measures it. For the terminology (how AEO, GEO and LLMO differ), see our AIO glossary.
The short answer: the cost picture up front
Before the breakdown, here’s the benchmark at a glance.
- One-off diagnostic / audit: roughly ¥100,000–500,000 (one-time)
- Monthly consulting (retainer): ¥150,000–800,000 per month for most engagements (report-only plans at the low end; full strategy-and-direction consulting can exceed ¥3M/month)
- Full program with implementation included: ¥500,000–1,000,000+ per month, or project-based quotes
And the first decision rule is simple: don’t sign a monthly retainer before you’ve paid for a one-off diagnostic of where you stand. With LLMO, the right moves depend entirely on your site’s current state — so a retainer without a diagnosis is paying every month for tactics that may or may not fit. Here’s the reasoning.
Cost benchmarks by engagement type (2026)
Organized by how you engage a vendor, the public data looks like this. Figures are guides based on published rates and vary by industry, site size and the number of target queries.
| Engagement type | Benchmark cost | What’s typically included |
|---|---|---|
| One-off diagnostic / spot audit | ¥100,000–500,000 (one-time) | Current AI-search visibility, competitor comparison, a direction-setting report |
| Monthly consulting (retainer) | ¥150,000–800,000 / month | Strategy, recommendations, monthly measurement — usually excludes implementation |
| Full program with implementation | ¥500,000–1,000,000+ / month, or per-project | The above, plus content production and technical implementation done for you |
| Large / enterprise | Several million yen scale | Cross-department, multi-brand, high-query-volume ongoing support |
For sources: Speee’s cost guide puts spot audits at ¥300K+, monthly consulting at ¥150K–3M+, implementation support at ¥300K–1M+, and enterprise projects “in the several-million-yen range.” Faber Company’s pricing guide (mieru-ca) shows nearly the same bands — spot diagnostics at ¥100K–300K, retainers at ¥100K–500K/month, and comprehensive support at ¥500K–1M+/month.
Note that many larger firms keep pricing private (“contact us”). PLAN-B’s LLMO consulting, for instance, is listed as price-on-request even on comparison sites, while their own article pegs the market at “¥500K+/month” (PLAN-B’s LLMO vendor comparison). Private pricing is common — but when you compare quotes, always align the assumptions first: which engines, how many prompts, implementation included or not.
What drives the price
The same “LLMO service” can vary several-fold in quote. Three factors do most of the work.
- Scope of coverage — the number of prompts (buyer questions) measured, and the number of competitors compared. Tracking 10 prompts on one engine is a completely different job from 100 prompts across four engines (ChatGPT, Claude, Gemini, Google AI Overviews).
- Whether implementation is included — “recommendations (a report) only” versus “changes shipped to your site.” This is the single biggest fork in price, because implementation maps directly to work on your site.
- Volume of primary information and industry competitiveness — accuracy-critical fields (medical, finance) and industries where competitors are already investing raise the effort required.
So when you compare quotes, matching the money isn’t enough — you have to match which engines, how many prompts, and implementation-or-not, or you’re not comparing against the benchmark at all.
What you should actually receive at each price level
Whether a price is fair is easier to judge by the deliverable than by the number.
- A diagnostic (one-time) should give you: your current visibility across the major AI engines, an analysis of which competitors are being recommended and why, and improvement instructions you can implement as-is. The minimum bar is a prioritized roadmap, not a pile of screenshots.
- A monthly retainer should give you: monthly measurement (share of voice versus named competitors) and the next moves to make — including why a number moved, not just that it went up or down. See how to measure AI visibility for what that measurement contains.
- A full program should give you: all of the above, plus the actual site changes (structured data, content, external signals) shipped — so the report doesn’t die in a drawer and the numbers actually move.
Traps to avoid before you buy
- A report with no path to implementation. Most LLMO improvement is changes to the site itself. A report with no one assigned to implement it won’t pay off. “Who ships the fixes after the report?” is the first question to ask.
- Guaranteed-results claims. “Up X% in Y weeks” guarantees are shaky, because no one controls AI models’ update cycles from the outside. Honest vendors talk in ranges by industry and competition (as a guide: 1–3 months for the fastest surfaces, 3–6 months for meaningful share-of-voice change, 6–12 months for model-level recognition).
- Measurement tooling billed as premium consulting. Measuring visibility is largely automatable. What’s worth paying for is the judgment to read that data and the implementation to act on it — not the raw measurements themselves.
How we think about it — and our pricing
Our stance is explicit: we don’t sell you a retainer up front. Start with a diagnostic. And what’s worth paying for is judgment and implementation, not measurement.
We hand improvements over as instructions your team or existing vendor can ship as-is (with our own implementation available as an option). The goal is to leave you self-sufficient, so we hand the know-how to your team rather than keeping it locked up.
Our pricing publishes the floor and quotes by scope from there:
- AI Visibility Diagnostic: ¥300,000 (one-time) — current visibility, competitor analysis and an improvement roadmap across ChatGPT, Claude, Gemini and Google AI Overviews.
- AIO Program: from ¥500,000 / month — ongoing implementation plus a monthly report of your share of voice versus competitors.
Within the market, the diagnostic sits in the mid-to-upper range of one-off audits, and the program at the entry of comprehensive support. Starting with the diagnostic is the least wasteful path — see our pricing page for the full structure.
Learn more about the AI Visibility Diagnostic →
FAQ
How much does LLMO optimization cost?
On 2026 public data, a one-off diagnostic runs ¥100,000–500,000, monthly consulting sits at ¥150,000–800,000 (full consulting can exceed ¥3M), and a full program with implementation is ¥500,000–1,000,000+ per month. The number swings widely with the number of engines, prompts and whether implementation is included, so compare vendors on the same assumptions.
What’s the cheapest way to start?
Rather than signing a retainer immediately, start with a one-off diagnostic (market rate: ¥100,000–500,000) to see where you stand. It’s the least wasteful path: once you know which fixes actually matter for your case, you can decide whether to move to a monthly engagement. Our AI Visibility Diagnostic is designed on exactly this logic. Another way to keep costs down is to run the in-house steps yourself — see The Complete LLMO Playbook: 12 steps you can do in-house.
Is this a separate cost from SEO?
LLMO / AIO overlaps SEO in places (structured data, quality content), but it targets being cited and recommended inside AI answers, so the KPIs and tactics differ. Most companies add it on top of existing SEO — and the efficient first step is a diagnostic that shows how much of your current SEO asset is already working in AI search. See the AIO glossary for the distinctions and LLMO optimization for optimizing the models themselves.
What should I check first in a quote?
Three things: which engines are covered (ChatGPT, Claude, Gemini, AI Overviews, etc.), how many prompts are measured, and whether it’s report-only or includes implementation. Without aligning these you can’t compare a price to the benchmark — and “who does the implementation” is the factor that most decides your return.
