Free Tool
AI Integration Cost Estimator
How much does an AI integration cost? Get an instant build-cost range, monthly running cost, and timeline for your project. Updates live as you choose.
- Instant estimate
- Runs in your browser
- Keepable PDF report
Project scope summary
Instant estimate. Exact scope & price confirmed in a free consultation.
No spam — we only use this to send your report and follow up about your project.
AI Integration Cost Estimator: Build Cost, Running Cost, and Timeline
"How much would an AI integration cost?" is the question that stalls most AI projects, because the honest answer from any agency is "it depends" followed by a request for a call. Meanwhile you cannot write a business case without a number.
This estimator gives you one before you talk to anyone. Five choices produce a build cost range, a monthly running cost, and a timeline, updating live as you adjust.
- Integration typeSeven to choose from
- ScopeData sources and workflows
- Build optionsCustom model, self-hosted
- Complexity tierStandard, Custom, Enterprise
- Monthly usageLow, medium or high
Build range, monthly cost and timeline, live.
Start With What You Are Building
Seven integration types, and the choice matters more than any other input because the engineering differs fundamentally:
AI ChatbotRAG / Knowledge BaseInternal CopilotDocument ProcessingVoice AIAI Workflow AutomationCustom AI API
A chatbot answering scripted questions and a RAG system searching your entire document library are not variations of the same build. The retrieval layer, the data pipeline, and the accuracy requirements are all different work.
- Scripted questions
- Fixed answers
- Simple flow
- Retrieval layer
- Data pipeline
- Accuracy requirements
Not variations of the same build.
Scope, Build Options, Complexity, Usage
Scope
covers data sources and workflows — the two things that most reliably drive cost upward. Each additional data source means another connection, another format, another set of edge cases.
Build options
include whether you need a custom or fine-tuned model, and whether you want self-hosted or managed infrastructure. Self-hosting changes both the build cost and the ongoing bill, usually in opposite directions.
Complexity tier
runs Standard (proven patterns), Custom (tailored build), or Enterprise (scale and compliance). The Enterprise tier is not a marketing label — audit logging, access controls, and data residency requirements are real engineering.
Monthly usage
— low, medium, or high — drives the running cost, since inference is charged by volume.
Two Numbers, Not One
The output separates one-time build cost from monthly running cost, and that separation is the most useful thing on the page.
Most people asking about AI integration cost are thinking only about the build. The monthly figure is what catches teams out six months later: model inference, hosting, monitoring, and maintenance continue for as long as the system runs.
A project with a modest build cost and a heavy usage profile can cost more over two years than one with a larger build and light usage. You cannot see that from a single number.
You also get a timeline and a scope summary, plus a downloadable PDF for sharing with whoever approves budgets.
Same ballpark total, very different shape.
Example
A Realistic Example: A Knowledge Base for Support
A support team of twelve spends hours daily searching internal documentation for answers. The manager wants to propose a RAG system over their help centre, product docs, and past tickets.
She selects RAG / Knowledge Base, sets three data sources, picks Standard complexity since the pattern is well established, chooses managed infrastructure, and sets usage to medium.
The estimator returns a build range, a monthly figure, and a timeline. She downloads the PDF and takes it to her director with three specific numbers rather than "we should look into AI."
The proposal gets a scoping conversation instead of a shrug — because it read like a project rather than an idea.
- RAG / Knowledge Base
- 3 data sources
- Standard complexity
- Managed infrastructure
- Medium usage
- PDF to the director
Three numbers instead of an idea.
Why the Result Is a Range
Because an honest estimate is a range. "Custom complexity" spans a lot of ground, and every business brings its own legacy systems, data quality problems, and compliance requirements.
The range narrows once someone maps your actual scope. Treat this as the number that tells you whether to have that conversation, not as a quote.
Scoping turns the range into a quote.
Who Uses It
Operations and product leads building a business case.
Founders sizing an AI feature.
IT managers comparing build against buy.
Anyone who needs a figure before a budget meeting.
Pair it with the LLM Token Cost Calculator for inference maths and the AI Automation Cost Calculator for process automation. Our AI integrations service delivers these builds — the Brain AI case study shows an agent built inside an existing platform.
Get Your Numbers Now
You cannot build a business case around "it depends." Set your integration type and scope above, and take a build cost, a monthly cost, and a timeline into your next budget conversation.
Estimate my projectCost drivers
What pushes an AI integration cost up — and what brings it down
Six things move the estimate more than anything else. Knowing them before a scoping call saves a round of back-and-forth.
Legacy systems
No API means custom connectors, and connectors are where build hours go.
Compliance
Audit logs, access controls and data residency are real engineering, not paperwork.
Accuracy requirements
An assistant that drafts is cheaper than one that must be right every time.
One workflow first
Starting narrow keeps scope, testing and risk small — then you expand.
A proven pattern
Chatbots and RAG over documents are well understood. Novel builds are not.
Managed infrastructure
Less to build and host yourself, at the price of a steadier monthly bill.
Build or buy
When an off-the-shelf tool is enough, and when to build
Not every AI need is an integration project. Some are a subscription.
| Your situation | Usually the better fit | Why |
|---|---|---|
| A generic task: meeting notes, grammar, image cleanup | Buy | Dozens of mature tools already do it well and cheaply. |
| Answers must come from your own documents | Build (RAG) | The value is in connecting your data, which no generic tool has. |
| It must act inside your CRM, ERP or internal tools | Build | Integrations and permissions are specific to your stack. |
| You are still testing whether AI helps at all | Buy first | Prove the value cheaply, then build what the tool cannot do. |
Before the budget meeting
Four things to bring with the estimate
The range gets you a meeting. These four turn it into a decision.
The process, in one sentence
What happens today, who does it, and how often.
A baseline number
How long it takes or what it costs now — the figure the build will be measured against.
The data sources, named
Where the information lives and who controls access to it.
A success measure
The one number that tells you, three months in, whether it worked.
Being straight with you
What this estimate is — and isn’t
A range to decide whether to have the conversation, not a quote.
- It is built from typical project patterns. Your legacy systems, data quality and compliance rules can move it either way.
- Internal time is not included. Your team’s hours for reviews, testing and access are real, and not in the figure.
- Model prices change. Inference costs have been falling; the monthly range is today’s view, not a promise.
- It cannot see your stack. A short scoping call is what turns the range into a fixed price.