AI automation agency pricing: what we charge and why (real numbers)

AI automation agency pricing comes down to two things: the number and the billing model. Our number is public: an audit at $1,900, a content engine at $4,900, build sprints from $9,500, a fractional AI officer at $2,900 a month. The model is fixed price and fixed scope, so the invoice you approve is the invoice you pay.

Ask ten AI agencies what they charge and you will get ten discovery calls. Here is our answer in plain text instead, plus how to read anyone else’s quote.

Our prices, as of July 2026

Our full price list is below. Every item is fixed price in USD, written scope first, no deposits followed by surprises. Retainers bill monthly and cancel monthly. Anything that does not fit a package gets a written fixed-price proposal after you describe it, usually within one business day.

• AI Operations Audit: $1,900 fixed, 10 working days. Refund if we find nothing worth building.
• Content Engine Install: $4,900 setup, optional $990/mo for us to run it.
• AI Workforce Sprint: from $9,500, 4 to 6 weeks, one working system on your infrastructure.
• Fractional AI Officer: $2,900/mo, cancel monthly, one automation shipped every month.
• Custom work: written fixed-price proposal after you describe it in the intake form at the end of this page.

What does the market charge for AI automation?

Published and quoted 2026 rates cluster in bands: audits, single builds, retainers and enterprise programs each sit at a different level, and the spread inside each band is wide. The bands below come from our own survey of quoted prices, not from a research-firm dataset.

Surveying published and quoted 2026 rates: AI readiness audits run $5k to $15k. Single custom builds run $3k to $15k depending on scope. SMB retainers run $1k to $3.5k per month; mid-market retainers $4k to $10k per month. Enterprise internal software programs get quoted at $450k to $4M with 12 to 24 month timelines.

We found no independent survey of agency rates from a research firm or statistics agency that we could open and verify, and vendor blogs with rate cards are selling something. Treat any band, ours included, as a starting point for your own quotes.

Our launch prices sit deliberately under those brackets. Not because the work is lighter, but because we are buying proof: this studio’s own thesis is that inbound plus published pricing converts better than sales theater, and underpriced early engagements are how we test it. The plan is to revisit prices at five paying clients. Early clients keep their pricing.

Why does hourly billing misalign incentives?

Hourly billing pays the agency more when the work takes longer, so the agency has no financial reason to finish early and the buyer carries all the overrun risk. A fixed price reverses it: the agency keeps any efficiency it finds, and you know the total before work starts.

This is not a startup opinion. The Federal Acquisition Regulation, section 16.601, which governs how the US government buys, states that a time-and-materials contract provides “no positive profit incentive to the contractor for cost control or labor efficiency”. The same section requires government surveillance of contractor performance to compensate. That is the regulation’s own text, read on acquisition.gov in October 2026.

A private buyer rarely has a surveillance team. The quote has to carry the discipline.

AI work sharpens the problem. Faster tools mean the same deliverable takes fewer hours, so an hourly agency loses revenue for doing better work. A fixed-scope agency has no such conflict.

It also matters who is buying. The US Census Bureau’s Business Trends and Outlook Survey found 23.8% of US businesses used AI in at least one business function in the two weeks ending 6 September 2026, published in September 2026. In the two weeks ending 28 December 2025 the figure was 17.7%. Many of those buyers are new to this and cannot judge an hourly estimate. A fixed number is easier to check.

How do the common AI agency pricing models compare?

There are four common models: hourly, monthly retainer, fixed-price project, and outcome-based. Each moves risk differently. Hourly leaves it with you, retainers trade predictability for open-ended scope, and a fixed-price project puts estimation risk on the agency. Outcome-based sounds fair but needs a measurable result.

Model	Who carries the overrun	Best for	Watch for
Hourly (time and materials)	You	Exploratory work with no definable scope	No natural stopping point, estimates that drift
Monthly retainer	Shared, scope is open-ended	Ongoing improvement work	Unused hours, vague deliverables
Fixed-price project	The agency	Defined builds with a written scope	Scope disputes if the spec is loose
Outcome-based	The agency, until the result is measured	Work with one clean, auditable metric	Arguments over how the metric is counted

We use two of the four on purpose. The audit and the sprint are fixed price. The fractional AI officer is a retainer with a defined output, one automation shipped every month, and you can cancel monthly. Our guide to fixed-price AI development goes deeper on where that model breaks and how scoping prevents it.

What should a good AI automation quote include?

A good quote names the deliverables, the acceptance test, the timeline, the price, what happens when scope changes, and what you own at the end. If any of those six is missing, the missing item is where the surprise invoice will come from. Ask for them in writing before you sign.

Check these:

• Deliverables as nouns. “A working intake agent on your accounts” beats “AI transformation support”.
• A stated change rule. Ours is simple: a change is a new order, not an invoice surprise.
• Handover terms. You own the delivered code, data and documentation, and you can rotate the keys the day after handover.
• A way out. The audit carries a refund clause. The retainer cancels monthly.
• Named exclusions. Hosting, API usage and software subscriptions should be listed, not implied.

If the answer to any of these is “we will discuss on a call”, you have learned how the engagement will run. Our AI development contract guide covers the clauses worth reading line by line, and the audit cost breakdown shows what a fixed price should buy at the front end.

Why publish prices at all?

We publish prices because hidden pricing favors whoever negotiates for a living, because a public number forces us to scope precisely, and because buyers now ask AI engines what things cost. Three reasons, and each one holds us accountable more than it helps us sell.

First, hidden pricing is a negotiation tactic, and negotiation rewards whoever does it professionally. That is the agency, not you.

Second, published prices force scope discipline on us. A fixed $9,500 sprint cannot absorb vague requirements, so our scoping is written and precise, which protects both sides.

Third, buyers now ask AI engines what things cost. Concrete prices in plain HTML are what those engines can cite. Most agencies hide them; that is their problem.

What do the prices not include?

Our prices do not include your infrastructure bills: hosting, API usage and software subscriptions. Everything we build runs on your own accounts, so you pay those providers directly, see the raw costs, and face no percentage markup from us. You also keep control the day the work is handed over.

Think your GitHub, your Supabase, your hosting, your API keys, rotated by you the day after handover.

Those costs are public, which is the point. Anthropic’s pricing page, read in October 2026, lists its Sonnet 4.6 model at $3 per million input tokens and $15 per million output tokens. A well-scoped agent’s monthly usage is something you can estimate from a price list, then watch on your own invoice. If a quote buries usage costs inside a bigger monthly fee, ask what the pass-through is.

Where should you start?

If you want to know which of these fits your situation, the honest starting point for most businesses is the $1,900 audit: it produces the ranked build list that makes every later decision cheaper. The method behind the scoring is the one we run on our own 41-agent operation.

Start async: describe what you want automated or built. A human reads every submission and you get a written reply within one business day. No call gets scheduled, because none is needed to read a form.