What It Costs to Add AI Features to Your App

TL;DR: Adding AI to an existing app typically costs between $15,000 and $150,000+ depending on complexity, data readiness, and integration depth. A simple AI-powered feature like smart search or a chatbot sits at the lower end. A custom model, RAG pipeline, or agentic workflow sits at the higher end. The biggest cost driver is not the AI itself. It is the work needed to connect AI to your data and existing systems.

Adding AI to your app costs anywhere from $15,000 for a focused feature to $150,000+ for a full AI platform build. The spread is wide because 'add AI' covers a lot of ground. Here is how to figure out where your project sits.

What actually drives the cost?

Most people assume the AI model is the expensive part. It is not. The model is often the cheapest bit. What costs money is everything around it.

Data access and prep. AI needs to read your data. If your data lives in siloed databases, old formats, or locked behind manual processes, you pay to clean and connect it before any AI feature works.

Integration depth. A chatbot that answers FAQs from a static knowledge base is simple. An AI feature that reads your CRM, writes back to your database, triggers workflows, and sends personalised emails is not.

Custom vs off-the-shelf models. OpenAI, Anthropic, and Google give you capable models at a low per-token cost. Fine-tuning or training a custom model adds significant time and cost. Most apps do not need it.

Ongoing inference costs. Once built, AI features cost money to run. High-volume apps with lots of AI calls can rack up meaningful monthly API bills. Build this into your budget.

If you want to understand how these pieces fit into a full build, the guide on adding AI to existing apps breaks it down step by step.

Typical budget ranges

These are real ranges based on the kinds of projects we see. They are not guarantees. Every build is different.

$15,000 to $30,000

A focused AI feature added to an existing app. Think smart search, AI-generated summaries, a chatbot using your existing content, or a basic classification or tagging feature. Assumes your data is reasonably accessible and the scope is tight.

$30,000 to $70,000

A more complex integration. Retrieval-augmented generation (RAG) so the AI can answer questions using your internal documents. An AI workflow that connects to your CRM or ticketing system. Personalisation features that adapt to individual user behaviour. This range often includes the data pipeline work that a simpler build skips.

$70,000 to $150,000+

A full AI platform feature set or a purpose-built AI product. Agentic workflows where AI takes actions, not just answers. Custom AI models or fine-tuned models on your proprietary data. Real-time AI features at scale. Enterprise-grade security and audit requirements.

For context, NSW Government, Briometrix, Vivid, and Huskee are among the organisations Devwiz has built for. Across 200+ apps since 2015, the biggest cost blowouts come from scope creep mid-build, not from the AI itself.

What you should budget for that most quotes leave out

A lot of AI quotes focus on development time and miss the support costs that follow.

• Prompt engineering and tuning. Getting an AI feature to behave reliably in production takes iteration. Budget for it.
• Evaluation and testing. AI outputs are probabilistic. You need a process to test quality, catch regressions, and monitor for drift.
• Model updates. AI providers update their models. Sometimes that breaks your prompts or changes output format. Someone needs to watch for this.
• API cost monitoring. A feature that works fine at 100 users can get expensive at 10,000. You need visibility into inference costs from day one.

How to get a more accurate number

The fastest way to get a real cost is to define the AI feature precisely before asking for a quote.

Answering these questions before you talk to a developer will cut days off the scoping process:

1. What does the AI feature do? What input goes in, what output comes out?
2. What data does it need access to? Where does that data live today?
3. How many users will use it? How often?
4. What happens when the AI gets it wrong? Who handles that?
5. Does this need to connect to any external systems (CRM, ERP, third-party APIs)?

CTOs planning this kind of work often find it useful to look at the CTO-specific resources on our tech page before briefing a development partner.

Does the type of AI feature change the cost much?

Yes. Here is a rough breakdown by feature type.

| Feature type | Typical range | Why |

|---|---|---|

| AI chatbot (FAQ / docs) | $15k to $30k | Simple RAG, low integration |

| Smart search / semantic search | $20k to $40k | Embedding pipeline + UI |

| AI content generation | $15k to $35k | Prompt engineering + review workflow |

| Document analysis / extraction | $30k to $60k | Parser + validation layer |

| AI recommendations | $40k to $80k | Needs user behaviour data |

| Agentic AI workflows | $60k to $150k+ | Multi-step, multi-system integration |

| Custom / fine-tuned model | $80k to $200k+ | Data prep + training + eval |

For teams evaluating AI vendors and wanting to audit what is actually available, AILED is a useful directory of AI tools and services that can help you benchmark what is on the market.

Build it right the first time

The cost to add AI to your app is real, but so is the return. Apps with well-built AI features automate manual work, surface insights faster, and give users experiences that older software cannot match.

The teams that get the best value do not start with the biggest possible scope. They pick one high-impact feature, build it properly, measure what it does, and expand from there.

If you want to talk through what AI would actually cost for your app, start a conversation with the Devwiz team. We have been building apps since 2015 and AI is now central to almost every project we take on.

FAQ

Q: Can I add a basic AI chatbot to my app for under $20,000?

Yes, if the scope is tight and your data is accessible. A chatbot that answers questions using your existing documentation or knowledge base can be built in that range. The cost climbs when you need it to connect to live databases, take actions, or handle complex multi-turn conversations.

Q: Do I need to pay for AI model costs on top of development fees?

Yes. Development fees cover the build. Ongoing AI model usage is billed by the API provider (OpenAI, Anthropic, Google, etc.) based on tokens processed. For most apps these costs are manageable, but high-volume features need to be architected with cost efficiency in mind from the start.

Q: How long does it take to add AI features to an existing app?

A focused feature takes six to twelve weeks from scoping to production. More complex integrations, especially those involving data pipelines or agentic workflows, take three to six months. The timeline depends heavily on how quickly your team can provide data access and sign off on design decisions.

Q: Is it better to build AI features in-house or bring in a specialist?

For most teams, a specialist is faster and cheaper. Building AI capability in-house takes time to hire, ramp up, and iterate. A team that has shipped multiple AI integrations already knows the failure modes. That said, if AI is core to your product long-term, building some internal capability alongside the external build is worth the investment.

Q: What is the biggest mistake companies make when budgeting for AI features?

Underestimating data readiness. Most businesses assume their data is ready to use. In practice, getting AI to reliably read and reason over internal data almost always requires cleaning, restructuring, or building a data pipeline first. Get a data audit done before you finalise your AI build budget.

Frequently asked questions

Can I add a basic AI chatbot to my app for under $20,000?

Yes, if the scope is tight and your data is accessible. A chatbot that answers questions using your existing documentation or knowledge base can be built in that range. The cost climbs when you need it to connect to live databases, take actions, or handle complex multi-turn conversations.

Do I need to pay for AI model costs on top of development fees?

Yes. Development fees cover the build. Ongoing AI model usage is billed by the API provider based on tokens processed. For most apps these costs are manageable, but high-volume features need to be architected with cost efficiency in mind from the start.

How long does it take to add AI features to an existing app?

A focused feature takes six to twelve weeks from scoping to production. More complex integrations, especially those involving data pipelines or agentic workflows, take three to six months. The timeline depends heavily on how quickly your team can provide data access and sign off on design decisions.

Is it better to build AI features in-house or bring in a specialist?

For most teams, a specialist is faster and cheaper. Building AI capability in-house takes time to hire, ramp up, and iterate. A team that has shipped multiple AI integrations already knows the failure modes. That said, if AI is core to your product long-term, building some internal capability alongside the external build is worth the investment.

What is the biggest mistake companies make when budgeting for AI features?

Underestimating data readiness. Most businesses assume their data is ready to use. In practice, getting AI to reliably read and reason over internal data almost always requires cleaning, restructuring, or building a data pipeline first. Get a data audit done before you finalise your AI build budget.