AI & AutomationFree quote

AI Integration Cost — Adding AI to Your App

Add OpenAI, vision AI or automation to your existing app — real costs, no fluff.

Price range4 000 – 30 000 €Indicative estimate — depends on scope
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What affects the price

  • AI model used — GPT-4o, Claude, Mistral (different API costs per request)
  • Volume of prompts and tokens (low traffic vs enterprise-scale usage)
  • Custom model fine-tuning vs prompt engineering on a foundation model
  • Data pipeline — do you need document ingestion, vector search (RAG)?
  • Frontend integration complexity (chat widget, internal tool, API endpoint)

What's included in our price

AI architecture design (model selection, prompt strategy, RAG if needed)
Backend integration (API wrapper, rate limiting, logging)
Frontend interface (chat UI or API endpoint)
Prompt testing and optimization (accuracy, tone, edge cases)
Guardrails and safety filters
Documentation and handover

How we bill

Fixed price

A single agreed price for a clearly defined scope. Best for projects with stable requirements.

Time & Material

Billed by actual hours worked. Best for iterative projects or when scope evolves.

Monthly retainer

A fixed monthly budget for ongoing development, maintenance and evolution. Predictable costs.

Tuplify Studio vs other options

OptionAdvantagesLimitations
Tuplify Studio Senior team, full accountability, trilingual support, production delivery Higher than solo freelance, minimum 5k€ engagement
Solo freelancer Lower hourly rate Availability risk, no team backup, limited expertise breadth
Local SSII / large agency Established processes, contractual guarantees 2–4x higher cost, slow onboarding, less flexibility

Frequently asked questions

Are there ongoing costs after the AI is built?

Yes — the main ongoing cost is the AI model API usage (OpenAI, Anthropic, etc.), which is billed per token. We help you design for cost efficiency. Our development fee is one-time.

What is RAG and do I need it?

RAG (Retrieval-Augmented Generation) lets the AI answer questions based on YOUR documents, not just its training data. You need it when accuracy on your specific data matters — documentation chatbots, internal knowledge bases, contract analysis.

How do you prevent the AI from giving wrong or harmful answers?

We implement system prompts with strict guardrails, output validation, topic filtering, and human-in-the-loop escalation for high-stakes queries. Testing covers edge cases before production.

Can I use a local AI model instead of OpenAI to save on API costs?

Yes — we work with local models (Ollama, Llama, Mistral) for privacy-sensitive or high-volume use cases. The tradeoff is lower performance vs GPT-4. We help you pick the right model for your accuracy/cost balance.

What if the AI quality is not satisfactory after delivery?

We define measurable acceptance criteria before the project starts (accuracy rate, topic coverage). The 30-day post-launch support is specifically designed to iterate on model performance based on real usage.

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