AI Features for SaaS: 12 Realistic Options and What Each One Costs to Run

The AI features for SaaS that pay off most often are the unglamorous ones: summaries, classification, pulling structured data out of documents, and search that understands what users mean. Most take days or a few weeks to build, and for most of them the model bill stays under $10 per 1,000 calls. The assistant that can "do anything" sits at the other end: the most expensive to build and the hardest to make reliable.

I'm a freelance full-stack developer based in Denmark, and I build SaaS products and web apps for businesses, so keep that in mind when I suggest bringing in help. The effort and cost figures below are my rough estimates, based on the providers' published API prices in October 2026.

The short answer

Build effort is my estimate for adding the feature to an existing, reasonably clean codebase. Running cost is the raw bill from the AI provider at typical usage.

My rule of thumb: pick the feature that removes the most manual work for your users, not the one that demos best. If your product was built with tools like Lovable or Cursor and isn't running reliably yet, start with my guide to taking a vibe-coded app to production. AI features belong in a product that already works.

How I estimated effort and cost

AI models bill by the token. A token is a chunk of text, and Anthropic's rule of thumb is roughly four characters or three quarters of an English word. German, Danish, Finnish and other European languages usually need a few more tokens for the same meaning. You pay for input tokens (your text and instructions) and output tokens (the model's answer), and output typically costs 4-8 times as much per token.

OpenAI's cheapest text model costs $0.05 per million input tokens and $0.40 per million output tokens, while most of its large models sit at $2-10 for input (OpenAI pricing). At Anthropic, Claude Haiku 4.5 costs $1 in and $5 out, and Claude Sonnet 5.5 costs $2 and $10 (Anthropic pricing). Both give 50% off work that can wait and run as a batch. Prices are in US dollars, so a European SaaS also carries some currency risk.

My ranges run from a small model at the low end to a mid-sized one at the high end. Token counts also differ between models: Anthropic notes that its newer models produce about 30% more tokens for the same text. Treat the numbers as orders of magnitude, not a budget. For a proper forecast with caching and spending caps, see my walkthrough of LLM API cost estimation.

Build effort, roughly: low is a few days to a week, medium is 1-3 weeks, high is several weeks including testing. A messy codebase stretches all of it.

Features that sort and structure data

This is where I'd start almost every time. The tasks have a clear right answer, so you can measure accuracy, and a wrong result is rarely a disaster.

1. Summaries of long text

Long support threads, case histories, account notes and reports can be boiled down to five lines at the top of the page. Anyone picking up a case they haven't followed saves time on every visit.

The build is simple. The real decision is when to generate the summary. Do it on every page view and you pay for the same summary again and again. Store it and regenerate only when the source changes. Summarizing a text of about 1,500 words costs roughly $0.30-8 per 1,000.

The trap is that a summary can drop exactly the detail that matters. Always show the full text right below it.

2. Classification and auto-tagging

The model reads a text and picks from categories you defined: which team a ticket goes to, whether a review is positive or negative, which accounting category an expense belongs to before it syncs to Xero or QuickBooks. It's one of the cheapest features on this list because the answer is a single word. Even a mid-sized model stays under $1 per 1,000, and batch pricing halves that.

I recommend starting with a small model and moving up only if your tests show it misses too often. Let users correct the category and store those corrections, because they become your best test set. If a simple rule can decide the category, such as the sender's domain, skip the AI.

3. Data extraction from documents and email

Invoices, purchase orders, CVs and contracts arrive as PDFs and emails, but your app needs fields: amount, date, supplier, line items. Current models read text and images and return data in a fixed structure your system can save.

Effort is medium because every result needs validation. Do the line items add up to the total? Does the supplier exist? Is the date plausible? Build a review screen where the user approves or corrects the extraction before it's saved. Expect roughly $1-10 per 1,000 documents of a couple of pages, and more for long scanned files.

4. Smart spreadsheet import

An often overlooked onboarding win. New customers show up with an export from their old system, and none of the columns match yours. The model can suggest which column maps to which field, for example "Customer No." to customer_number, and clean up formats. Dates are the classic trap in Europe, where 03/10/2026 means October 3rd, not March 10th.

You only send the column names and a few sample rows, not the whole file, so the cost is under $0.01 per import. The effort lies in the interface: users need to see the suggested mapping and fix it before the import runs. It's one I suggest often because it removes a real barrier for new customers.

5. Content moderation

If your platform has reviews, comments, profiles or listings, someone has to watch for spam and abuse. OpenAI's moderation model is listed as free on its pricing page and catches the obvious cases. For rules specific to your platform, like no phone numbers in listings, use regular classification.

Let the AI flag and queue content, not delete it. A person should make the final call on borderline cases.

Features that help users find answers

6. Semantic search

Keyword search finds words. Semantic search finds meaning, so "cancel subscription" also finds the article titled "How to stop your plan". Under the hood, each text becomes an embedding (a list of numbers that represents its meaning), and the search compares those numbers.

The cost is close to nothing. OpenAI's smallest embedding model costs $0.02 per million tokens, so indexing 10,000 texts of about 500 tokens each comes to around $0.10. If you run PostgreSQL, the pgvector extension stores and searches embeddings inside your existing database, so you don't need another service.

Effort is medium. Texts need sensible chunking, the index must update when data changes, and users must only ever find their own data. That last part is the one teams forget, and it's one of the most common security holes in AI-generated code.

7. Answers from your own documents

This is what most people mean by "an AI that knows our stuff". The technique is called RAG (retrieval augmented generation): the system finds the most relevant passages with semantic search, then asks a language model to write an answer based on them, ideally citing the source.

Build the search first. If the results are poor, the answers will be too, just more convincingly worded. Each answer usually sends a few thousand tokens of context, so expect roughly $5-30 per 1,000 answers. For comparison, Anthropic's own example puts 10,000 support tickets on Haiku 4.5 at about $37 (just under $4 per 1,000), with conversations averaging around 3,700 tokens. Long conversations cost more because the history is resent each turn. Caching helps: at Anthropic, cached input usually costs 10% of the normal rate.

Effort is medium to high. The model can answer confidently about things it doesn't know, so you need a plan for questions your content can't answer. If the answers belong in a chat widget on your public website rather than inside the product, an off-the-shelf chatbot tool is often the better deal.

8. Plain-language questions about data

"Show me customers who haven't ordered in 90 days" instead of five filters. It sounds simple, but it's one of the hardest features to make safe. Let the model write database queries directly and you risk wrong numbers, slow queries and, at worst, one tenant seeing another tenant's data.

The safe version has the model translate the question into the filters and reports your app already has, then shows those filters so users can see what was actually searched. Running costs are low, roughly $3-10 per 1,000 questions, but the build is heavy. Ask first whether better filters would solve most of the problem.

Features that write, translate and listen

9. Drafts and writing help

Suggested replies to support tickets, product descriptions from a handful of keywords, follow-up emails after a call. Users get a draft to edit instead of a blank page. Effort stays low as long as a person always presses send. Expect roughly $3-10 per 1,000 drafts.

Quality depends mostly on what you feed the model: the customer's history, your company's tone, previous replies. A generic draft gets ignored fast. Track how much users edit. If they rewrite nearly everything, the feature isn't ready.

10. Translation

If you sell across the Nordics or the wider EU, translation can pay for itself quickly: user content, help articles and transactional emails in Swedish, German or Finnish without sending every update to an agency. In my view, quality across the major European languages is good enough for most product text. Costs run roughly $5-15 per 1,000 pages because the output is as long as the input.

Store translations instead of translating the same text on every view. And don't publish terms of service or legal text that a human hasn't reviewed.

11. Transcription and meeting notes

Recorded sales calls, support calls and interviews can become text, and that text can become summaries, tasks and notes on the customer record. Transcription itself costs $0.003-0.006 per minute at OpenAI, so $0.18-0.36 for an hour of audio.

Effort is medium because audio is heavy. Files need uploading, background processing and secure storage. Call recordings almost always contain personal data, and GDPR's transparency rules generally mean people must be told they're being recorded. Accuracy can drop for smaller languages like Danish or Finnish and for poor audio, so test with real recordings before you promise anything.

The hard one: an assistant that takes actions

12. Assistant that takes actions

An assistant that doesn't just answer, but creates orders, changes settings or sends emails on the user's behalf. Technically, you give the model tools: functions in your app it's allowed to call. One task often takes 5-10 round trips, and each one resends instructions and history. That puts the cost at roughly $0.05-0.50 per task.

Security is the hard part. Prompt injection, where text in a document or email makes the model do something you didn't intend, ranks first on the OWASP Top 10 for LLM applications (2025). OWASP's mitigations include giving the model the fewest permissions possible and requiring human approval for high-risk actions.

My advice is to hold off until your simpler features are live. Start with an assistant that can only read and suggest, and have users confirm every action.

What every AI feature needs before launch

There are a few things I always build alongside any AI feature. This is usually where the development time goes, not the model call itself.

Personal data is the item most teams skip. I cover what you can send and what the contracts need to say in my guide to GDPR and LLM APIs. For the technical side (queues, retries, keys and error handling), see how to integrate an LLM API into an existing app.

When you shouldn't add AI features

Every feature you add needs maintaining. I usually advise against it in these cases:

• When a simple rule does the job. If you can write the logic as "if the sender is X, the category is Y", a rule is cheaper, faster and gives the same answer every time.
• When you haven't launched yet. AI features are rarely why your first customers pay. Focus on the core product, and see my list of features your MVP can do without.
• When a mistake is costly and nobody will catch it. Decisions about money, hiring or health shouldn't be made by a language model without a person in the loop.
• When a ready-made tool exists. A support chat on your marketing site or a meeting recorder can usually be bought for a monthly fee. Build it yourself only if the feature needs data from inside your product.

There are also cases where you need someone other than me. If AI is the product itself, for example because you want to train your own models or do advanced computer vision, you need a machine learning specialist. I'm a full-stack developer who builds AI features into products using existing models. I don't develop new ones.

Next steps: pick one feature and test it on real data

1. Find the task your users spend the most manual time on inside your app today.
2. Pick the simplest feature on this list that solves it.
3. Collect 30-50 real examples and test two or three models on them before building any interface.
4. Build the feature with a fallback, a spending cap and logging from day one.
5. Release it to a small group of customers and measure whether they use it and how much they edit the output.

If you'd like help building it into your product, here's how I approach SaaS development, from a fixed-price discovery phase to production.

Frequently asked questions

Do I need to fine-tune or train my own model?+

Almost never. Off-the-shelf models from OpenAI, Anthropic and others handle nearly every feature on this list well, provided they get clear instructions and the right data. Training or fine-tuning your own model takes a large amount of high-quality data and a specialist to do it. Start with an existing model and only consider custom training if your tests show it genuinely can't do the job.

Should I use OpenAI or Claude for my SaaS?+

Test both on your own examples and pick the one that performs best for the price. The difference depends more on the task than on the provider. Build your integration so you can switch models without rewriting the feature, because prices and models change quickly. Also check each provider's data processing terms, especially if you handle personal data from EU customers.

Should I charge extra for AI features?+

Often, yes, especially when the feature has a noticeable running cost. Common approaches are putting AI features in a higher plan, including a monthly allowance of AI actions with paid top-ups, or charging per use. Whichever you choose, set a usage cap per customer so a single heavy user can't eat your margin.

What happens when a provider retires the model I use?+

You move to a newer model and test again. Providers retire older models regularly, and Anthropic's pricing page already lists several as retired. Pin a specific model version rather than "latest", and rerun your test set whenever you switch. A new model can be better on average and still worse at your particular task.