What does an Agent project usually cost?

There is no single published standard, only orders of magnitude: industry reporting puts application-type purchases by small and mid-sized banks around RMB 5 million, sometimes split or bundled, while roughly 2,770 application-type large model awards were recorded in the first half of 2026. The practical step is to split a quote into requirement analysis, data access, build, hardware and annual operations, and compare part by part.

Why do quotes vary so widely?

Three reasons: some quotes cover development only, excluding hardware and annual operations, which makes them look cheap; data access varies in difficulty (official APIs versus assembling exported files); and scope differs, with some quoting for one scenario and others bundling several. Align these three before comparing, or you are comparing different things.

How do I judge hardware pricing?

On the public market, appliances and machines for local inference range from a few hundred thousand to several million RMB depending on compute scale and concurrency, and public tenders often list equipment and supporting services separately. To judge, ask three things: how much concurrency and data volume it must carry, whether local deployment is genuinely required, and whether growth will retire it before its three-year depreciation ends.

Which part is most often underestimated?

Annual operations. Definition changes, interface upgrades, server maintenance and metric extensions all require ongoing spend, and the main reason public projects die in year two is that nobody owns this part. Put three-year total cost — hardware, development and annual operations — on one sheet rather than comparing first-year figures alone.

Manufacturing

What an AI agent costs: how a quote is built and how to compare

Updated 2026-10-17All figures from public sources

Asking "what does an AI agent cost" rarely yields a single published price, only orders of magnitude and a quote structure. The useful approach is to split a quote into five parts — requirement analysis, data access, build and configuration, hardware, and annual operations — examine what each contains, then compare totals. A quote that cannot be split is much more likely to grow later.

Five parts of a typical quote

Part	What it covers	Usual basis	Where cost hides
Requirement analysis	Site survey, scenario selection, first draft of metric definitions	One-off	Waived, then buried in development so comparisons break down
Data access	Opening the data path, field verification, scheduled jobs	Per source system or per person-day	Interface licence fees and vendor cooperation billed separately
Build and configuration	Metrics, alerts, presentation, integration testing	Per scenario or per person-day	Rework from definition changes with no agreed cap
Hardware	Machines and spares where local deployment is required	List price, tiered	Depreciation period, expansion and spares
Annual operations	Running, maintenance, updates, metric extensions	Annual	Starts in year two, so invisible in first-year comparisons

Three usable market benchmarks

Three kinds of public figures are available. Use them to sanity-check magnitude, not to price your own project:

Single-project order of magnitude: industry reporting puts application-type AI purchases by small and mid-sized banks around RMB 5 million, sometimes split across tenders or bundled. That is a reported range, not a standard price.
Overall market structure: public statistics record about 2,770 application-type large model awards in China in the first half of 2026, roughly 60% of all large model awards; in the first half of 2025, government-sector awards numbered 328, with RMB 2.56 billion disclosed. The point is that spending goes to applications, not raw compute.
Vendor-side disclosed amounts: one study counts roughly RMB 2.044 billion in publicly disclosed large model award value across five major AI cloud vendors in the first half of 2026. Read these carefully — disclosed award value is not total market spend.

For hardware there is a separate public range: appliances and machines for local inference list from a few hundred thousand to several million RMB, depending on compute scale and concurrency. Whether to deploy locally follows from data sensitivity and existing facilities, not from budget size.

Comparing three quotes: four checks

Is the scope the same? One scenario versus a bundle of several are not comparable. Align on one scenario first.
Do they include hardware and annual operations? Put three-year total cost — hardware, development, operations — on one sheet before comparing.
Is data-access difficulty priced in? An official API and a folder of exported files are very different amounts of work.
How are definition changes handled? Business definitions will change. How they are charged, and how many are included, belongs in the quote.

Four common inflations and omissions

Padding the quote with a compute list. Compute-type and application-type projects have entirely different cost structures; a GPU list in an application quote usually has no bearing on the requirement.
Bundling everything as "intelligence". Folding development, hardware and operations into one total means renegotiating every adjustment.
Omitting interface licences and vendor cooperation. Opening an existing system's interfaces often needs the original vendor, and that third-party cost surfaces after signing.
Omitting operating labour. Definition upkeep and exception review need company staff. It is not on the vendor's quote but belongs in your own budget.

A pragmatic way to decide

With a limited budget, concentrate the first round on one scenario whose value can be counted, plus the data access and operations it needs. Expand after it runs. That route usually costs less in total than opening several scenarios at once and is far easier to explain internally. On choosing a delivery model — local deployment, cloud, or taking over the source code yourself — see our custom delivery and enterprise AI agent deployment pages.

If you have a specific scenario and budget range, send us your site conditions (contact) and we will return a cost-structure view: which parts must be spent and which can wait, at no cost.

In one line: do not ask what an agent costs; ask what the quote consists of, what each part covers, and what the three-year total is. Only quotes that answer those three are worth pursuing.

Sources: 21st Century Business Herald, August 2026, on application-type AI purchases by small and mid-sized banks around RMB 5 million; public statistics (Smart Super Parameters and press coverage) on about 2,770 application-type large model awards in H1 2026 and 328 government-sector awards worth RMB 2.56 billion in H1 2025; China Financial Information Network, July 2026, on roughly RMB 2.044 billion in disclosed awards across five major AI cloud vendors in H1 2026; Cailianshe and 21st Century Business Herald on publicly listed appliance price ranges. This article compiles public price information and quote structures only and names no specific vendor pricing.

FAQ

What does an Agent project usually cost?: There is no single published standard, only orders of magnitude: industry reporting puts application-type purchases by small and mid-sized banks around RMB 5 million, sometimes split or bundled, while roughly 2,770 application-type large model awards were recorded in the first half of 2026. The practical step is to split a quote into requirement analysis, data access, build, hardware and annual operations, and compare part by part.
Why do quotes vary so widely?: Three reasons: some quotes cover development only, excluding hardware and annual operations, which makes them look cheap; data access varies in difficulty (official APIs versus assembling exported files); and scope differs, with some quoting for one scenario and others bundling several. Align these three before comparing, or you are comparing different things.
How do I judge hardware pricing?: On the public market, appliances and machines for local inference range from a few hundred thousand to several million RMB depending on compute scale and concurrency, and public tenders often list equipment and supporting services separately. To judge, ask three things: how much concurrency and data volume it must carry, whether local deployment is genuinely required, and whether growth will retire it before its three-year depreciation ends.
Which part is most often underestimated?: Annual operations. Definition changes, interface upgrades, server maintenance and metric extensions all require ongoing spend, and the main reason public projects die in year two is that nobody owns this part. Put three-year total cost — hardware, development and annual operations — on one sheet rather than comparing first-year figures alone.