modulla | Plan Your AI Brain Budget | Boost your performance!
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modulla.ai · EN
**A private AI Brain** is a dedicated artificial intelligence infrastructure operating exclusively on company data, without access by external providers. Unlike public APIs (OpenAI, Claude, Gemini), it offers full control over data, predictable fixed costs, and compliance with regulations: GDPR, the AI Act, and DORA.
## Public API or private AI infrastructure: decision criteria
Experience shows that this question comes up in every serious AI project. [Every request sent to a public API has a cost](https://www.future-processing.com/blog/ai-pricing-is-ai-expensive/), and at small scale that's negligible. The problem appears at 100,000 requests per month, when the bill starts to resemble a premium tool subscription, but with no control over cost growth.
Since 2024, a growing number of companies have been migrating from the SaaS pay-per-token model to their own AI environments. Three reasons recur in every such decision:
- **Data security.** Production, financial, and customer data should not leave the company's infrastructure. Public APIs do not guarantee that information won't feed into future model iterations.
- **The per-seat trap.** Popular platforms such as Microsoft 365 Copilot ($30/user/month) or GitHub Copilot charge per employee. For a 50-person team, that's an additional 4,000–6,000 PLN every month for functionality that private infrastructure delivers at a flat rate.
- **Regulations.** The AI Act, GDPR, and DORA impose specific requirements on where and how sensitive data is processed. A private pipeline is one of the few ways to meet these requirements without compromise.
## Traditional API vs. private AI infrastructure: what sets the two approaches apart?
The choice of AI infrastructure model depends on several variables: request volume, data sensitivity, and regulatory requirements. The table below compares both approaches across key criteria.
| | | |
| --- | --- | --- |
| **Criterion** | **Public API (pay-per-token)** | **Private AI infrastructure** |
| Cost model | Variable, grows with volume | Fixed monthly flat rate |
| Data control | Limited (external provider) | Full (own infrastructure) |
| GDPR, AI Act, DORA compliance | Requires additional agreements and layers | Built-in, air-gap possible |
| Model customization | None or limited | Fine-tuning and configuration for company processes |
| Time to launch | Immediate | 1–4 weeks |
| Cost-effectiveness threshold | Low volume | Approx. 60,000 requests/month |
## When is private AI infrastructure cost-effective, and when does a public API suffice?
Public APIs remain a good choice at low volume (below 60,000 requests per month), for pilot projects, and where the company does not process sensitive data. Above the 60,000-request threshold, the cost difference grows rapidly.
Private infrastructure is justified when a company:
- processes data subject to special protection (medical, financial, legal),
- operates in industries regulated by the AI Act, DORA, or sector-specific requirements,
- plans volumes above 60,000 requests per month, especially in RAG architectures with extended context,
- needs a predictable budget independent of price fluctuations from external providers.
The first step is usually a process and technology stack audit that identifies what will yield the highest ROI from automation (and what is *not* worth automating). The cost of such a diagnosis is 4,000–6,000 PLN as a standalone service. A thorough needs analysis can save tens of thousands of zloty at a later stage of implementation.
## Transparent private AI Brain pricing in 2026
The market rarely publishes actual implementation costs. [Below is a table with ranges](https://innowacje.ai/cennik/) matched to organizational scale, based on current GPU infrastructure prices and open-source platform licenses.
| | | | |
| --- | --- | --- | --- |
| Package | For whom | Implementation cost | Monthly maintenance |
| **AI Brain Light** | SMEs, up to 50 users | 15,000 – 25,000 PLN | 3,700 – 4,300 PLN |
| **AI Brain Professional** | Mid-size companies, 50–500 users | 45,000 – 80,000 PLN | 12,000 – 14,500 PLN |
| **AI Brain Enterprise** | Corporations, 500+ users | from 150,000 PLN | individual quote |
### AI Brain Light: for SMEs that want to start without major risk
[Implementation cost is 15,000–25,000 PLN](https://www.lessmanual.ai/blog/ile-kosztuje-wdrozenie-ai-2026), and monthly maintenance consists of a platform license (2,500 PLN) and GPU environment rental (1,200–1,800 PLN). A total of 3,700–4,300 PLN per month, regardless of the number of requests.
The architecture in this package is based on optimized open-source models in the 7–11B class (Llama, Bielik, Mistral, and their successors). They are sufficient for handling knowledge bases, document automation, and task classification without requiring H100-class GPU cards, provided a precise RAG pipeline and advanced data structuring are applied, which prevents hallucinations when working with smaller models.
### AI Brain Professional: for companies scaling their processes
Implementation cost is 45,000–80,000 PLN. Monthly costs: [license 7,500 PLN plus hosting](https://webyjuice.pl/blog/ile-kosztuje-prywatne-ai-2026) (2 L40S or A100 cards), giving 4,500–7,000 PLN. A total of 12,000–14,500 PLN per month with full budget predictability.
This package supports open-source models in the 70B+ class, enabling deep integrations with ERP, CRM, and RAG (Retrieval-Augmented Generation) pipelines. The central value is a unified organizational knowledge base: documents, procedures, system data, and internal communications become a single, searchable resource available to the entire team in real time.
### AI Brain Enterprise: full isolation for regulated industries
Implementation from 150,000 PLN; monthly costs depend on the GPU cluster architecture. Air-gapped environments, physically isolated from the internet, are the standard for financial and medical firms and public institutions. Pricing details are agreed upon after an infrastructure audit and assessment of the volume of data processed.
## Total cost of ownership (TCO): when does the investment pay off?
[The break-even point for the Professional package](https://stormit.pl/ile-kosztuje-wdrozenie-ai) is approximately 60,000 requests per month. At that point, both models cost about the same: roughly 14,500 PLN per month. Below that threshold, public APIs are cheaper. Above it, the difference grows quickly.
> In RAG pipelines, the main cost is not the number of requests itself, but the context tokens transmitted with each one. Every request retrieves documents, conversation history, and system data, adding 4,000–8,000 input tokens. At 500,000 requests per month, the public API bill reaches approximately 120,000 PLN. The same volume on private infrastructure still costs 14,500 PLN.
| | | | |
| --- | --- | --- | --- |
| Request volume / month | Public API cost\* | AI Brain Professional cost | Savings |
| 25,000 | approx. 6,000 PLN | 14,500 PLN (fixed) | API cheaper |
| 60,000 | approx. 14,500 PLN | 14,500 PLN (fixed) | break-even point |
| 200,000 | approx. 48,000 PLN | 14,500 PLN (fixed) | 33,500 PLN / mo. |
| 500,000 | approx. 120,000 PLN | 14,500 PLN (fixed) | 105,500 PLN / mo. |
*\*API costs estimated based on reference public API pricing ($5/1M input tokens, $15/1M output tokens for previous-generation models; current 2026 rates are typically lower, though the break-even point remains similar), assuming a heavy RAG context: approximately 6,000 input tokens and 600 output tokens per request (6,000 tokens is equivalent to loading roughly 10–15 A4 pages of text into memory with each question asked). For shorter requests without extended context, costs are several times lower. Volume discounts may further reduce pricing for large customers.*
## Hidden costs that vanish from implementation budgets
And here we get to the heart of it: most market quotes omit line items that genuinely affect the final project cost.
- **Data preparation (ETL).** The quality of AI infrastructure depends directly on the quality of the data feeding it. Extracting, transforming, and loading data from ERP, CRM, or SharePoint requires careful engineering. A well-executed ETL can increase implementation cost by 15–30%. It is an investment in making the pipeline work, not an unexpected expense.
- **Access management (IAM).** AI infrastructure must respect who has access to which documents. Integration with Active Directory or Okta is an additional security module that tends to be overlooked in market proposals.
- **Monitoring and updates.** Models evolve, data must be re-indexed, and configurations adjusted. The first 6–12 months after deployment is a stabilization period. A technical support subscription is an insurance policy for the entire investment.
- **Team training.** The most expensive implementation mistake is purchasing a pipeline that no one uses. Dedicated training and SOPs (standard operating procedures) are a mandatory element of a successful deployment.
## Where does private AI infrastructure deliver the greatest return?
Documented deployments across several industries show concrete, measurable results.
### Commercial real estate
A five-person team managing a portfolio of over 100 real estate funds deployed private AI infrastructure for 50,000 PLN. The pipeline handles tenant relations, loan tracking, investor reporting, and regulatory compliance. After deployment, the time to generate investor reports dropped by 60%, and weekly savings on compliance document analysis exceeded 15 hours. Return on investment: 4 months.
### Law firms
Law firms use private AI to analyze contracts, trust structures, and legal precedents while maintaining absolute client data confidentiality. Implementation cost: 15,000–50,000 PLN. Amortization occurs within 2–3 months through faster document audits and risk analysis.
### Healthcare and strictly regulated industries
Deployments compatible with medical confidentiality requirements (implementation cost 38,000 PLN, 1,400 PLN monthly maintenance) process medical interview transcripts and diagnostic images without violating GDPR. Return on investment: 6–12 months through minimizing diagnostic errors and accelerating product certification.
### E-commerce and back-office operations in SMEs
A holding company comprising a construction firm, two restaurants, and a consulting firm reported a reduction in daily administrative workload from 14 hours to 5 hours after deployment. Users report a 70% reduction in information retrieval time and 16% of customer inquiries handled automatically, without human intervention.
## PARP grants: up to 75% of implementation costs from public funds
This is information worth considering before making a decision. In the current EU financial perspective, Polish SMEs can apply for digitalization co-financing from the FENG programs (European Funds for a Modern Economy) and the SMART pathway run by PARP, dedicated to innovation deployments including AI. Co-financing can cover up to 75% of total implementation costs.
In practice: an AI Brain Light package at 20,000 PLN may cost the company as little as 5,000 PLN in own contribution. The Professional package at 60,000 PLN: own contribution of 15,000 PLN. The condition is meeting the program's eligibility criteria and preparing the appropriate application documentation.
In summary: private AI infrastructure makes financial sense at the right scale and data profile. For companies meeting these criteria, with access to PARP co-financing, the actual cost of entry is significantly lower than the list price suggests. [Analyze your company's processes with us and let's check with hard numbers when your own infrastructure will start paying for itself.](https://modulla.ai/contact)
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## FAQ: Private AI Brain in practice
### How does a private AI Brain differ from using ChatGPT for Business or Microsoft Copilot?
ChatGPT for Business and Copilot are ready-made SaaS products with per-seat and per-token charges, running on external provider infrastructure. A private AI Brain runs on company servers or in a dedicated single-tenant cloud, with open-source models configured for the organization's data and processes. In an air-gapped architecture, no data leaves the company's infrastructure, and costs are fixed regardless of usage volume.
### At what request volume does a private AI Brain become financially viable?
The break-even point for the Professional package is approximately 60,000 requests per month, calculated at standard public API pricing (reference-class models such as GPT-4o, without volume discounts). Below that value, a public API may be the cheaper choice. Above it, especially at 200,000 or more requests per month, private infrastructure generates an increasingly clear cost advantage. For companies where data security and regulatory compliance are priorities, this threshold drops considerably lower.
### Can private AI infrastructure be deployed independently, without an external partner?
Technically, yes. In practice, DIY attempts consume 40–200 hours of work, generate technical debt, and typically end up at MVP level rather than a production-ready pipeline. A professional deployment shortens the launch time to 1–4 weeks, delivers an environment ready for real workloads, and eliminates the risk of security gaps that can be costly when company data is involved.
### How can I apply for PARP co-financing for AI Brain deployment?
PARP programs and European funds from the current EU perspective provide co-financing of up to 75% of AI implementation costs for SMEs. A company with 20 employees can deploy a full AI infrastructure at a fraction of the list price. The condition is meeting the eligibility criteria and preparing the application documentation.