Custom AI App: When a Ready Tool Is Not Enough, and What an MVP Costs (2026)
How much does a custom AI app cost in Poland in 2026? A dedicated AI app is a project implementation: simple process automation from €3,500 net, an AI MVP typically €6,000–35,000 net. The price depends on integrations and scope, not the label. You start with a free process scan.
A dedicated custom AI app is a project implementation, not an off-the-shelf purchase. Simple automation of one process starts from €3,500 net, and an AI MVP typically falls in the €6,000–35,000 net range. The price depends on integrations and scope, not the label. You start with a free process scan (€0).
Quick answer
"Custom AI app" has no single price, because the phrase covers different purchases. At Syntalith we price them as separate lines, net:
- automation of one process: from €3,500 net - a system that handles a specific, repeatable process end to end, with integration and an AI model behind it,
- MVP of a dedicated AI app: typically €6,000–35,000 net - a project implementation: one process taken to production with an interface, integrations, and boundaries,
- agent that runs a process: from €6,000 net - an app that performs multi-step tasks in your systems, escalates exceptions, and leaves a trail,
- maintenance: priced individually - hosting, monitoring, SLA, and changes after launch,
- ongoing AI model cost: at typical volumes usually cents per case, calculated on real traffic, not fixed in advance.
The start is free: a process scan (€0) is a 30-minute engineer call plus a written takeaway in two business days. If you want a portable document with architecture and a fixed quote before a bigger decision, the current price of the implementation specification is €1,200 net (higher from August 2026).
The full price list for every line is on the Syntalith pricing page, and the scope of apps themselves on the custom AI apps page.
When is a ready tool (ChatGPT Team, off-the-shelf SaaS) not enough?
Most companies do not need a dedicated app. ChatGPT Team, a ready SaaS, or an off-the-shelf copilot solve the typical case faster and cheaper. You only build a dedicated app when one of three signals appears.
Signal 1: the process lives across several systems at once. Data sits in the ERP, customers in the CRM, requests in the inbox, and the rest in spreadsheets. A ready tool usually sees one of those systems and needs a human to copy data between them. A dedicated app joins them into one flow.
Signal 2: the tool has to PERFORM something, not just answer. A chatbot answers in a chat window. A custom app writes an order into the ERP, creates a document, updates a status in the CRM, or sends a reply by rule. If you need an action in your systems rather than text to copy, the off-the-shelf option runs out.
Signal 3: the data or logic does not fit inside someone else's tool. The data cannot leave your environment, you require a specific DPA and separation, or the process rules are so specific that no SaaS handles them without workarounds. Then you build in-house, within boundaries you set.
If none of these three signals is present, we will say so plainly: buy the ready tool and do not pay for an implementation. How to tell whether you actually need a system that performs work, we explain in the guide on what an AI agent is.
What goes into an AI MVP?
An MVP is one process taken to production, not a complete product with every feature. The goal is to check on real traffic that the system works before you invest in the rest. A typical AI MVP has six layers:
- Input layer: the interface or channel through which a case enters the system (form, inbox, panel, API).
- Model layer: integration with a model via API (OpenAI, Claude), prompts, and, when it has to reach into your documents, a retrieval layer (RAG).
- System integrations: connections to the ERP, CRM, mail, or database, so the system reads and writes where the process actually lives.
- Boundaries, escalation, and trail: rules for what may happen without a human, when a case is escalated, and a record of every decision you can check after the fact.
- Data handling: validation, separation, GDPR compliance, and what happens to sensitive data.
- Hosting and deployment: going live in production, monitoring, and model cost limits.
The first pilot is usually built in 2–6 weeks, depending on the number of integrations and how quickly we get access and data. The order in which we assemble it and what stretches the schedule, we break down in the implementation pricing guide.
What does integration with ERP, CRM, and model APIs look like?
Integrations drive the price more than the model does. Connecting one modern API is different work from wiring into an undocumented legacy system, an inbox, and Excel files.
ERP and CRM integration is easy where the system has a clean, modern API and more expensive where you have to work around its absence, stand up a middleware, or work off exports. This is the most common source of a price gap between two seemingly similar projects.
Integration with model APIs (OpenAI, Claude) is technically simple but has a variable cost. Providers bill usage by the number of input and output tokens, so you calculate the model cost on volume, not up front. The app can also reach for a more expensive model only on harder cases and a cheaper one for routine work. We settle this before launch, together with a daily limit and the behavior when it is exceeded.
The rule is simple: the model is the cheapest piece of the puzzle. You pay for the app to read and write safely in the systems where your company actually works.
Ready tool or custom AI app?
This is not a table of the whole market, just a way to read the decision. An off-the-shelf tool and a custom app are two different purchases, not two points on a price scale.
| Criterion | Ready tool (ChatGPT Team, off-the-shelf SaaS) | Custom AI app (MVP to order) |
|---|---|---|
| Time to start | minutes to days | 2–6 weeks to the first pilot |
| Cost | monthly subscription per user | project implementation: automation from €3,500, MVP €6,000–35,000 net |
| Fit | to the typical case, configured within the tool's limits | to your process, rules, and data |
| Integrations | ready connectors, supported systems only | ERP, CRM, legacy, mail, and model APIs on your stack |
| Owner | the SaaS vendor, you are a tenant | you: code, data, and decisions stay on your side |
| When to choose | typical process, data may sit in the vendor's cloud, an answer or draft is enough | non-typical process, several systems, the app must perform work with boundaries and a trail |
How much does a custom AI app cost, and what drives the price up?
The price rises with the scope of responsibility, not the length of the feature list. The range is wide because "AI app" covers both a simple flow and a multi-system implementation. Four things raise the quote:
- Number and quality of integrations. One process with one modern API is the lower bound. Several systems, including legacy without an API, is the upper one.
- What the app may do on its own. An app that drafts something for approval is cheaper and lower risk than one that changes ERP data or sends decisions to customers by itself.
- Data and security. Personal data, financial information, environment separation, SSO, or a DPA are conditions for going into production and a real part of the cost, not decorations.
- Volume. An app that runs 50 times a day can be simple. One that runs thousands of times a day has to be observable: queues, limits, monitoring, and error handling.
For context: in the "State of AI Development Costs 2026" report (Pharos Production, June 2026), the median cost of an AI MVP was $42,000 and the 90th percentile $180,000, across a sample of more than 25 production systems. That is a different market and dollars, so treat the figure as a reference point, not a Polish price list. Our €6,000–35,000 net range follows the same logic: the dominant cost is integration complexity, not the model itself.
Buyer arithmetic: build or stay on a subscription
Before you decide, calculate both sides on your own numbers. This is your substitution, not our promise:
Annual cost of the ready tool =
number of users
x price per user per month
x 12
+ cost of workarounds and manual work the tool does not remove
Annual cost of a custom app =
build cost / amortization period (e.g. 2-3 years)
+ maintenance
+ model cost (tokens) at your volumeA subscription wins at small scale and a typical process. A custom app starts to win where the cost of workarounds and manual data copying rises with volume, because that cost is hidden: it does not show on the SaaS invoice, only in the team's hours. If, after you substitute your numbers, the annual cost of the ready tool with its workarounds is lower than the build, we will advise against building.
When NOT to build an AI app
Honestly: a dedicated app is often a bad purchase, however fashionable it is.
- A ready tool is enough. If the process is typical and well served by an off-the-shelf SaaS, there is no point rebuilding it from scratch. ChatGPT Team at a fraction of the price solves many "we want to speed up our people" cases.
- You only want to speed up one person. If you need drafts, research, and document order, that is not a custom app but a personal AI assistant from €1,200.
- An unstable process or low volume. If the rules change every week and live in someone's head, write the process down on paper first. If the process happens rarely, the build will not pay back.
If any of these fits, we will say so at the scan, before you spend anything.
How to start
The cheapest sensible first step is to count the process, not to order an app.
- Book a free process scan and show one process that lives across several systems.
- Prepare: which systems are in the path, how many cases per month, what the app would perform on its own, and where the exceptions appear.
- After the call you get a recommendation: automation of one process, an MVP of a dedicated app, an implementation specification, or an honest "buy the ready tool for now."
Book a free process scan | Custom AI apps | See pricing