AI Integration Cost: What Firms Charge to Add AI to Your App
LLM integration cost for an app you already run: the build quote, the token bill and what firms require
AI integration cost for an existing app runs from about $5,000 to $150,000, but most of that spread is scope, not price. A single API-level feature such as summarization or smart search is quoted at $10,000 to $40,000. A copilot wired into your data and permissions starts near $40,000. As of October 2026, only 10.2% of the 2,742 LLM integration firms listed on Edverise state a $0 minimum, and 38.1% state $10,000 or more.
The mistake most buyers make is comparing quotes as one number. An AI integration quote is two bills: a one-off build, and a running cost per token that the model provider sets and the agency does not control. This post separates them, using listing data for the build and the providers' own price pages for the running cost.
Updated November 2026. Listing figures are as of 28 October 2026; model prices were read on the providers' pages in November 2026.
What firms charge: AI integration cost by feature type
Four vendor guides rank for this query, and every one is written by a firm that sells the work. None cites an outside source for its ranges. They are still useful, because where four sellers independently land on similar numbers, you have a rough market price. Where they disagree, you have found the part of the scope that nobody can price without seeing your app.
| Feature added to an existing app | Biz4Group | Aalpha | RaftLabs | Rapid Developers |
|---|---|---|---|---|
| One generative feature (summarize, draft, classify) | $10,000–$30,000 | $15,000–$40,000 | $15,000–$40,000 | not given |
| AI search over your own content | $10,000–$25,000 | $20,000–$50,000+ | $20,000–$50,000 | $25,000–$60,000 |
| Support chatbot on an API model | $15,000–$35,000 | $5,000–$15,000 | $8,000–$25,000 | $15,000–$35,000 |
| Copilot or assistant with your data | $40,000–$150,000+ | $20,000–$60,000+ | $25,000–$60,000 | not given |
The single-feature row is the most consistent: three sellers put one generative feature somewhere between $10,000 and $40,000. The chatbot row is the least consistent, and the reason is visible in the guides themselves. Aalpha's $5,000 to $15,000 is a basic bot answering from a script or a short prompt. The higher figures assume retrieval-augmented generation (RAG), where the bot looks up your help articles or account data before it answers, which makes it a data project first and an AI feature second.
Two of the guides also contradict themselves. Biz4Group gives chatbot integration as $15,000 to $35,000 in one table and a lower range in another, and RaftLabs gives two different monthly running costs for the same chatbot. Treat any single vendor's table as a sales document with a range in it, not as a market rate.
The LLM integration company minimum budget, from 2,742 listings
The vendor guides tell you what one firm charges. The listing data shows what the firms that do this work say they will accept. As of October 2026, Edverise lists 2,742 firms under LLM Integration & Prompt Engineering, and every one of them states a minimum project size.
| Stated minimum project | Firms listed | Share of the category |
|---|---|---|
| $0 | 281 | 10.2% |
| $1,000 | 695 | 25.3% |
| $5,000 | 722 | 26.3% |
| $10,000 | 573 | 20.9% |
| $25,000 | 291 | 10.6% |
| $50,000 | 130 | 4.7% |
| $75,000 or more | 50 | 1.8% |
Three readings matter for a budget. First, 1,044 firms, or 38.1% of the category, state a floor of $10,000 or more, and 471 firms (17.2%) state $25,000 or more. Second, the $0 band is unusually thin. Across all 1,249 categories on Edverise, the median share of firms stating a $0 floor is 57.4%; here it is 10.2%. LLM integration firms set floors far more often than service firms in general.
Third, the middle is crowded. 1,698 firms, 61.9% of the category, state a floor of $5,000 or less. So an $8,000 budget clears the stated minimum of most listed firms, but the table above shows what it buys: one API-level feature from a small firm. A copilot quote near the top of the vendor ranges is a different market: only 37 listed firms state a floor of $100,000 or more, and quotes at that level usually come from larger shops with a delivery team and an account manager.
A stated minimum is a filter, not a price. A firm with a $5,000 floor may still quote you $30,000 once it sees the scope. The floor is useful for one thing: removing firms that will not take your project before you spend a week on calls. For how floors behave in other categories, see the agency minimum project size distribution.
Why AI integration quotes vary so much for the same feature
A quote for "add AI search to our product" can come back at $8,000 from one firm and five times that from another, and both can be honest. The difference sits in four places, and you can ask about each one directly.
Where your data lives, and how clean it is
Search, summarization over customer records and any assistant that answers from your content all need the data to be fetched, chunked and indexed in a vector database before the model ever sees it. If your content is in one well-structured database, that step is short. If it is spread across a CRM, a ticketing tool and PDFs, the firm is pricing a data pipeline. RaftLabs states that complex integrations with multiple data sources or real-time sync can double project cost, which is the cleanest single explanation for a quote that looks inflated.
How much of your app has to change
Rapid Developers prices backend architecture changes at $10,000 to $30,000 on top of the AI feature itself, and notes that its estimates assume your app already exposes a proper API. If the AI feature needs to call your system to read an order or update a ticket, someone has to build that interface. A firm that quotes low has often assumed it exists. Check that assumption against the API development and integration work your own team would otherwise do.
Testing that the answers are right
The cheap quote rarely includes an evaluation set: a fixed list of real questions with known good answers that the feature is scored against before launch and after every prompt change. It is the line that separates a demo from a feature. In the Stack Overflow 2025 Developer Survey, 66% of developers named "AI solutions that are almost right, but not quite" as a frustration, and 46% said they distrust the accuracy of AI output. Your customers will notice the same thing in your product, and a firm that budgets for evaluation is pricing that risk in.
Guardrails, privacy and compliance
Prompt injection, where a user's text instructs the model to ignore its rules, is the first entry in the OWASP Top 10 for LLM Applications, and sensitive information disclosure is the second. A feature that reads customer data needs controls for both. Rapid Developers prices GDPR or HIPAA compliance work at $10,000 to $25,000. That cost lands hardest in regulated sectors; the LLM integration listings include 1,205 firms that serve medical clients and 1,158 serving financial services, so specialists exist if you need one.
Do I need to rebuild my app to add AI?
No. Adding an LLM feature to an existing app almost never requires a rebuild. The model runs at the provider and your app calls it over an API, so the work is in the backend code that assembles the prompt, fetches your data and handles the response. Rapid Developers' guide is explicit that most modern apps need backend changes only.
The exception is AI integration into legacy software: a system with no API, business logic inside stored procedures, or data you cannot query without a nightly export. Rapid Developers estimates that legacy refactoring adds 30-50% to the timeline. If a firm proposes a rebuild, ask it to separate the rebuild from the AI feature in the quote, because those are two decisions, and the second one does not depend on the first.
Monthly API cost after AI integration, priced from published rates
The running cost is the part buyers can check themselves, because the model providers publish their prices. Vendor guides quote it as a wide band: Biz4Group gives LLM usage as $500 to $25,000+ a month, and Aalpha gives $100 to $800 a month for an early-stage product. Neither shows the arithmetic. You can do it in a few lines.
Take a summarization feature that produces 10,000 summaries a month, each reading about 3,000 tokens and writing 300. That is 30 million input tokens and 3 million output tokens. At Anthropic's published list prices per million tokens, the monthly bill is:
| Model | Input / output per million tokens | Monthly cost for 10,000 summaries |
|---|---|---|
| Claude Haiku 5.5 | $0.10 / $0.50 | $4.50 |
| Claude Sonnet 5.5 | $2 / $10 | $90 |
| Claude Opus 5.5 | $4 / $20 | $180 |
Two things follow. The model you choose moves the running cost by a factor of forty, so ask which model each quote assumes and why. And for a modest feature, the token bill is small next to the build. Anthropic also states that batch processing saves 50%, which suits summaries that can run overnight, and its cached-input rate for Sonnet 5.5 is $0.10 per million against $2 for fresh input, 95% less for a system prompt that repeats on every call.
Search has a different shape. Indexing 200,000 product records of about 500 tokens each is 100 million tokens; with OpenAI's text-embedding-3-small at $0.02 per million, that is $2 once, or $13 with text-embedding-3-large at $0.13. The recurring cost is the vector database: Pinecone's Standard plan carries a $50 monthly minimum. A firm that quotes thousands a month in "AI infrastructure" for a feature of this size should show you the volume behind it.
Where the token bill does grow is when one user action triggers many model calls, as it does in an agent that plans, calls tools and retries. That is a different cost model, covered in the post on AI agent development cost. If your feature is one request in and one answer out, the arithmetic above is the right one.
Fixed price vs hourly for AI integration, and what belongs in the contract
A fixed price works for a single feature with a clear input and output, such as summarizing a ticket thread. It works badly for anything judged on answer quality, because "good enough" is not defined until you test it. The common middle path is a short paid proof of concept on a sample of your real data, priced fixed, followed by a build priced against what the proof of concept found.
Whatever the model, write three things into the statement of work. The evaluation set and the pass rate that counts as done. Who pays the API bill during development and after launch, and on whose account the keys sit. And the maintenance retainer: Biz4Group puts yearly maintenance at 15% to 25% of the original budget, so a $40,000 build implies $6,000 to $10,000 a year for prompt updates, model version changes and monitoring. The trade-offs between contract types are set out in fixed price vs time and material vs outcome-based contracts.
How to compare AI integration quotes line by line
Put every quote into the same five lines before you compare totals. A quote that cannot be split this way is not ready to compare.
- Data work. Which sources are connected, and is indexing included or assumed?
- App changes. Which endpoints in your system will be built or changed, and by whom?
- Model and prompt work. Which model, which prompt engineering, and whether fine-tuning is proposed. For a first feature it rarely should be.
- Evaluation and guardrails. How many test questions, what pass rate, and which injection and data-leak controls.
- Running and maintenance. Estimated monthly tokens at your volume, the vector database, and the retainer.
Expect upselling too. In a sample of 50 firms in the category, 33 also list AI strategy and roadmap consulting, so a strategy phase may appear as a line before any build. It can be worth paying for if you do not yet know which feature to add first; if you do, ask for it to be removed. The general method for quotes that differ several times over is in how to compare software development quotes.
Frequently asked questions
How much does AI integration cost?
AI integration cost for an existing app typically runs from $10,000 to $40,000 for one API-level feature such as summarization or search, and from about $40,000 for a copilot that works with your data and permissions. Vendor guides give an outer range of $5,000 to $150,000. Running costs are separate and set by the model provider's token prices.
How much does it cost to add ChatGPT to an app?
Adding ChatGPT-style features to an app through OpenAI's API costs the same as any LLM integration: a one-off build, usually $10,000 to $40,000 for one feature, plus usage. At OpenAI's published rates, gpt-6.1-sol costs $2 per million input tokens and $10 per million output tokens, so a feature producing 10,000 short summaries a month costs about $90.
What are the ongoing costs after integrating an LLM?
The ongoing costs after integrating an LLM are token usage, any vector database, and maintenance. Token usage for a modest feature can be under $200 a month on mid-priced models. Pinecone's Standard plan has a $50 monthly minimum. Biz4Group puts yearly maintenance at 15% to 25% of the build, covering prompt and model updates.
What is the minimum budget for an AI integration firm?
The minimum budget most AI integration firms state is $5,000 or less. As of October 2026, 61.9% of the 2,742 LLM integration firms listed on Edverise state a floor of $5,000 or lower, 38.1% state $10,000 or more, and 17.2% state $25,000 or more. A stated floor filters firms; it is not a quote.
Why do AI integration quotes vary so much?
AI integration quotes vary mainly because firms assume different scope: how many data sources must be connected, how much of your app must change, whether an evaluation set is included, and what guardrails and compliance work is priced in. RaftLabs states that multiple data sources or real-time sync can double project cost on their own.
Is AI integration cost lower for a small business?
AI integration cost for a small business is lower mainly because the scope is narrower, not because firms discount. A single feature on a cheap model, with data from one source, is the cheapest shape. Aalpha's guide puts early-stage integrations at $5,000 to $25,000, and about one in three listed LLM integration firms states a floor of $1,000 or less.
Build your shortlist from the firms whose floor fits
Start from the feature and the budget, then filter. If your budget is under $10,000, scope one feature on one data source and look at firms stating a $5,000 floor or below. If you are buying a copilot, expect $40,000 or more and shortlist from the 471 firms stating $25,000 or higher. Either way, ask every firm to price the five lines above so the quotes can be compared. You can filter the LLM integration and prompt engineering firms on Edverise by minimum project size, city and industry before you send a single brief.