Adding AI to your product is one of the most common requests from founders and product managers in 2026. The question that rarely gets a straight answer upfront is: what does it actually cost?
The cost of building an AI feature has four components: development, infrastructure, API usage, and maintenance. Each is frequently underestimated.
Development cost
Building an AI feature is not adding a button that calls an API. It involves:
Defining the use case precisely. What will the AI do? Under what conditions? What inputs does it receive? What outputs does it produce? What happens when the AI is wrong? This scoping work takes time and is often underestimated.
Prompt engineering. Getting the AI to produce reliable, accurate, appropriately-formatted outputs for your specific use case requires experimentation. A simple feature might need one to two weeks of prompt development and testing. A complex feature might need four to six weeks.
Integration work. Connecting the AI to your existing data, your UI, and your business logic is software engineering work. For a straightforward integration (user pastes text, AI responds), this is two to four weeks. For a feature that reads from your database, processes the data, and writes results back, it is four to eight weeks.
Testing. AI features have a different failure mode from traditional software. You cannot write a test that checks "is this response correct?" in the same way you check "does this function return the right value?" Testing requires building evaluation datasets: examples of inputs with known good outputs that the AI should match. This takes time to build.
Security and data handling. If the AI feature processes customer data, you need to review what data is sent to third-party AI APIs and ensure it is handled appropriately. Some customer data cannot be sent to third-party services. This constraint needs to be designed around from the start.
Rough development cost estimate:
- Simple AI feature (text summarisation, content generation with fixed inputs): $8,000-$20,000
- Moderate AI feature (document Q&A, classification, multi-step workflow): $20,000-$50,000
- Complex AI feature (agentic workflows, retrieval-augmented generation with custom data): $50,000-$150,000
Infrastructure cost
If you are using a third-party AI API (OpenAI, Anthropic, Google), your infrastructure cost has two components: the API itself and the infrastructure that supports it (servers, databases, caching).
For most features, the API cost dominates. See the API usage section below.
If you are running a self-hosted model (for data privacy reasons or to avoid per-query costs at scale), your infrastructure cost includes: GPU compute, model storage, and the engineering time to run and maintain the model.
Self-hosted model costs for a small production deployment: $500-$2,000 per month on cloud GPU infrastructure, plus engineering time to maintain.
API usage cost
This is the cost that surprises most founders. AI APIs charge per token. A token is roughly 0.75 words.
Example calculation:
- 10,000 user queries per month
- Each query: 500 tokens input, 500 tokens output = 1,000 tokens per query
- At OpenAI GPT-4o pricing: approximately $0.005 per 1,000 tokens input, $0.015 per 1,000 tokens output
- Monthly API cost: 10,000 queries x 500 input tokens x $0.000005 = $25, plus 10,000 queries x 500 output tokens x $0.000015 = $75 = $100 per month total
This is manageable. Now scale:
- 1,000,000 user queries per month: $10,000 per month in API costs
At scale, API costs become a significant line item. This is why products that succeed with AI need to build API cost management into their architecture from the beginning: caching common responses, using cheaper models for simpler tasks, and designing prompts to minimise unnecessary token usage.
Maintenance cost
AI features require ongoing maintenance for several reasons:
Model updates. AI providers update their models. New models sometimes behave differently from the ones you built for. Prompts that worked perfectly on an older model may need adjustment.
Prompt drift. Edge cases accumulate. Users find inputs that produce bad outputs. Each one needs investigation and potentially prompt engineering to address.
Data quality. Features that use your own data (RAG applications, fine-tuned models) need their data sources maintained. Stale data produces poor outputs.
Evaluation monitoring. You should be sampling AI outputs regularly and checking quality. This is not automatic; it requires a process.
Ongoing AI feature maintenance runs 10-20% of the initial development cost per year, as a rough estimate.
What founders get wrong
Treating the API as the entire cost. API costs are usually the smallest part of building an AI feature correctly.
Underestimating prompt engineering. Getting reliable, accurate AI outputs for a specific use case is harder than it looks. The first working prompt is not the final prompt.
Not planning for quality degradation. AI features degrade over time without maintenance. The feature that was 90% accurate at launch may be 70% accurate six months later without attention.
Ignoring failure cases. What does the user see when the AI is wrong? What happens when the API is down? These need to be designed, not discovered at launch.
Getting an accurate estimate
The cost of your specific AI feature depends on its complexity and your existing architecture. Our AI automation team can give you a realistic estimate after a 30-minute scoping conversation.
Use the AI feasibility checker to assess your use case, then get in touch for an estimate that covers all four cost components.