TL;DR & Quick Summary
Adding one well-scoped AI feature to an existing product typically costs roughly $15,000 to $100,000 to build, plus a running cost that scales with how much people use it. The AI is rarely the expensive part. Integration is: getting the right data, enforcing your existing permissions, fitting the feature into your interface, and testing that it behaves.
Summarise or draft inside an existing screen: about $15,000–$40,000
Question answering over your product's data: about $30,000–$70,000
Predictive feature (scores, forecasts, recommendations): about $30,000–$90,000
Assistant that takes actions in the product: about $50,000–$100,000+
Running cost: tokens per use × uses per user × active users × price per token
Key Takeaway: Before asking for quotes, work out which data the feature needs, who is allowed to see it, and what the feature may change. Those three answers set most of the cost.
Get Started: Planning an AI feature for your product? Schedule a strategy call with Cogniq AI for a scoped estimate, or see our AI integration services.
Build Cost Comes From Team, Weeks and Rate
The build cost of an AI feature follows the same formula as any software work:
Cost ≈ engineers × weeks × 40 hours × blended hourly rate
A typical team for one feature is two engineers, with design and product help from your existing team. At a blended rate of $60–$90 per hour:
| Duration | Hours (2 engineers) | Cost at $60/hr | Cost at $90/hr |
|---|---|---|---|
| 3 weeks | 240 | $14,400 | $21,600 |
| 6 weeks | 480 | $28,800 | $43,200 |
| 10 weeks | 800 | $48,000 | $72,000 |
| 14 weeks | 1,120 | $67,200 | $100,800 |
The duration depends on the feature type and your codebase. Rates vary widely by region and provider. Our AI MVP cost guide shows how rate alone can triple a quote.
Cost by Feature Type
Summarise, Draft or Classify Inside an Existing Screen: about $15,000–$40,000
Examples: summarising a long record, drafting a reply, classifying incoming items, extracting fields from an uploaded document. 3–6 weeks.
These are the cheapest because the data is already on the screen, the user reviews the output, and nothing changes without them. The main work is prompt design, structured outputs, an evaluation set, and the interface for accepting or editing the result.
Question Answering Over Your Product's Data: about $30,000–$70,000
Examples: "ask your workspace", searching help content, answering questions across a customer's records. 6–10 weeks.
The cost driver is retrieval with permissions. Content has to be indexed and kept current as it changes, and every answer must respect the product's existing access rules, so users never see another account's or team's data. Getting that right is most of the work.
Predictive Features: about $30,000–$90,000
Examples: lead or churn scores, demand forecasts, anomaly alerts, recommendations. 6–12 weeks.
These use machine learning on your product's historical data rather than a language model. The cost depends on data quality and history. Our guides to forecasting data requirements and lead scoring cover what's needed.
Assistant That Takes Actions: about $50,000–$100,000+
Examples: "create the invoice", "reschedule these appointments", "update these records". 10–14+ weeks.
Acting is more expensive than advising. Each action needs a tool connected to your product's internal API, permission checks, confirmation steps for anything sensitive, audit logs and error handling. The OWASP guidance on excessive agency recommends human approval for high-impact actions. Our guide to human-in-the-loop approval gates covers how to design it.
What Your Codebase Changes
The same feature can cost very different amounts depending on what it's being added to:
| Factor | Cheaper | More expensive |
|---|---|---|
| Data access | Clean internal APIs | Data spread across services or only in the database |
| Permissions | Clear role and tenant model | Ad hoc access rules scattered through the code |
| Codebase | Modern, tested, documented | Old, untested, few people understand it |
| Interface | Component system that's easy to extend | Tightly coupled screens |
| Deployment | Automated, frequent releases | Manual, infrequent releases |
If your product sits on older systems, our guide to AI integration with legacy systems covers the connection options.
Running Costs: Work Them Out Per Use
Language-model features are billed per token, roughly a word fragment, for both the text sent to the model (input) and the text it returns (output). Estimate the monthly cost like this:
Monthly cost ≈ (input tokens × input price + output tokens × output price) × uses per user × active users
For illustration only, assume a model priced at $3 per million input tokens and $15 per million output tokens. Real prices vary by provider and model and change often, so check the current rates, for example on Anthropic's pricing page.
- A request sends 4,000 tokens and receives 500: $0.012 + $0.0075 ≈ $0.02 per use
- 20 uses per user per month × 1,000 active users = 20,000 uses ≈ $390 per month
The biggest variables are how much context you send and how often people use the feature. Sending a whole document or long history with every request can multiply cost by ten. Measure tokens per request during development, not after launch.
Ways to keep running costs predictable:
- Send only the context the request needs
- Cache results that don't change
- Use smaller, cheaper models for simple steps such as classification
- Set usage limits per user or per plan
- Include AI in a higher pricing tier so cost grows with revenue
Costs Teams Forget
- An evaluation set. Real examples with known good answers, used to check quality before every release. Without it, nobody can say whether a change made the feature better or worse.
- Failure handling. What the interface shows when the model is slow, unavailable or unsure.
- Data and privacy review. What customer data is sent to the model provider, under what terms, and whether customers need to be told or asked.
- Support and documentation. Customers will ask what the feature does with their data and why it gave a particular answer.
- Maintenance. Model versions change, prompts drift and data changes. Budget engineering time every month.
A Worked Example: Q&A in a Customer Portal
Here's how the numbers come together for a typical request. A B2B software company wants customers to ask questions about their own account data and the help centre from inside the existing portal.
Scope: index help articles and each customer's account records; answer questions with citations; respect the existing rule that users only see their own company's data; show a "was this helpful?" control; escalate to support when the answer isn't found.
Build: two engineers for about 8 weeks:
- Weeks 1–2: data access, permission design, a test set of 80 real customer questions
- Weeks 3–5: indexing pipeline, retrieval with permission filters, answer generation with citations
- Weeks 6–7: interface in the existing portal, escalation path, failure handling
- Week 8: evaluation against the test set, security review, staged release to a few customers
At $60–$90 per hour, 640 hours comes to about $38,000–$58,000, inside the question-answering range above.
Running cost: with the illustrative prices above and 2 cents per question, 300 active customer users asking 15 questions a month each is 4,500 questions, about $90 a month in model usage, plus indexing and hosting.
The largest risk in this example isn't the AI. It's the permission filter. A single bug that shows one customer another customer's data costs far more than the whole project, so that's where most of the testing goes.
Build In-House or With a Partner?
If your engineering team has capacity and someone who has shipped an AI feature before, building in-house keeps the knowledge in your team. If not, a partner can usually ship the first feature faster and set up the evaluation and monitoring practices your team will reuse for later features. We compare the two in AI agent development company vs in-house team.
How to Scope It Before Asking for Quotes
Answer these questions in writing:
- What exactly will the feature do? Describe one workflow, step by step, from the user's point of view.
- Which data does it need, and where does that data live?
- Who is allowed to see that data? How does the product enforce that today?
- Will it only suggest, or will it change things?
- How will you know it works? Gather 50–100 real examples with the result you'd expect.
- How many users will use it, and how often?
With those answers, a partner can give you a quote based on real scope rather than a guess. You'll also be able to compare quotes by team, timeline and what's excluded. If you're deciding between building the feature and buying an AI add-on from a vendor, the economics in custom AI agent vs SaaS apply here too.
Conclusion
Adding AI to an existing product usually costs $15,000 to $100,000 per feature to build, with running costs you can estimate from tokens before launch. The feature type sets the range. Your data access, permission model and codebase set where you land within it. Scope the data, the permissions and whether the feature acts or advises before asking for quotes.
Schedule a strategy call with Cogniq AI to scope an AI feature for your product, or explore our custom AI development services.
