Saket ShuklaWhat is a custom GPT solution?
A custom GPT solution is an application built on a large language model (LLM) – such as GPT, Claude, Gemini or an open-source model – that is adapted to a company’s own data, workflows and rules, for example an internal knowledge assistant or a customer-support agent. Its cost depends far less on the model itself than on the approach, the data work, the integrations and the ongoing usage and maintenance.
Key takeaways
- Most business cases do not need a model trained from scratch; RAG on a hosted model is usually enough.
- Data preparation, integration and evaluation are often the largest cost items.
- Usage-based model fees scale with traffic, so design for efficiency.
- Budget for monitoring, security and continuous improvement after launch.
Which approach fits your budget?
| Approach | What it involves | Relative cost | Best for |
|---|---|---|---|
| Prompt engineering on a hosted model | Instructions and templates calling a model API | Lowest | Drafting, summarising, simple assistants |
| Retrieval-augmented generation (RAG) | Model answers using your documents via search | Moderate | Knowledge assistants, support, internal search |
| Fine-tuning | Further training a model on your examples | Higher | Consistent style, specialised tasks, classification |
| Agentic workflows | Model plans steps and uses tools and APIs | Moderate to high | Automating multi-step business processes |
| Training a model from scratch | Building a foundation model | Very high | Rarely justified outside AI companies |
Our guide to RAG vs fine-tuning explains the trade-off in detail.
Key figures
- The cost of querying a GPT-3.5-level model fell from $20 to $0.07 per million tokens between November 2022 and October 2024 – a more than 280-fold reduction. (Stanford HAI AI Index, 2025)
- Gartner forecasts worldwide AI spending will total $2.7 trillion in 2026, up 49.5% year over year. (Gartner, 2026)
"The model is rarely the expensive part. Data, integration and keeping answers reliable are."
What are the main cost drivers?
- Discovery and design: use-case selection, success metrics and architecture.
- Data preparation: collecting, cleaning, chunking and securing documents and records.
- Integration: connecting to CRMs, ERPs, ticketing systems and identity providers.
- Evaluation and guardrails: test sets, accuracy measurement, content filters and human review.
- Model usage: per-token API fees or hosting costs for self-managed models.
- Security and compliance: access control, data residency, logging and governance.
- Operations: monitoring quality, cost and latency, plus regular improvements.
How do you keep costs under control?
- Start with a focused proof of concept on real data and a clear success metric.
- Use smaller or cheaper models where they meet the accuracy target.
- Cache frequent answers and keep prompts and retrieved context concise.
- Reuse existing components for authentication, search and analytics.
- Track cost per conversation or per task from day one.
What does the market say?
Gartner has estimated the cost of generative AI deployment approaches at between $5 million and $20 million for large enterprises, and predicted that at least 30% of GenAI projects would be abandoned after proof of concept due to poor data, risk controls, cost or unclear value. Mid-sized companies can start far smaller by scoping tightly and building on hosted models.
Frequently asked questions
Do we need to train our own model?
Almost never. Most business solutions use a hosted or open-source model with RAG, prompt design or light fine-tuning.
What is cheaper, RAG or fine-tuning?
RAG is usually cheaper to start and easier to keep current because you update documents, not the model.
What are the ongoing costs of a GPT solution?
Model usage or hosting fees, monitoring, security, content updates and periodic improvements based on user feedback.
How long does it take to build a custom GPT assistant?
A focused proof of concept often takes a few weeks; a production solution with integrations and governance typically takes a few months.
How Assystant can help
Assystant designs and builds GenAI and RAG solutions grounded in your data, from proof of concept to production – including a small language model for MealsPad and an AI negotiation chatbot for Nutritionplanet. See AI & product engineering or book a free consultation.