When an SME decides to integrate a language model into its operations, the same question inevitably arises: should we train the model on our data, or give it real-time access to our documents? The answer determines the project cost, time to first result, and the level of maintenance you will need over the coming years. Fine-tuning and RAG (Retrieval-Augmented Generation) are not competitors; they are different tools designed for different problems. Confusing them is one of the most common mistakes we see in companies approaching AI for the first time.
What fine-tuning is and what problem it solves
Fine-tuning means continuing the training of a base model (GPT-4o, Mistral, Llama 3, Gemini…) with examples from your company: question-and-answer pairs, well-resolved call transcripts, contracts in your format, technical data sheets with the sector's exact terminology. The model adjusts its internal parameters until that style, vocabulary, and logic are «baked in».
The result is a model that speaks like your company without needing long instructions or attached documents. It generates responses in the correct corporate tone, uses internal abbreviations, follows the layout of your delivery note or commercial proposal, and does not ask about things any experienced employee would take for granted.
What fine-tuning does not solve is access to information that changes frequently. If you train a model today on your product catalogue and tomorrow you update 200 price references, the model will keep returning the old prices until you retrain it. Fine-tuning is static knowledge; the business world is dynamic.
What RAG is and what problem it solves
RAG (Retrieval-Augmented Generation) does not modify the model. Instead, it adds a step before each response: the system searches a document base — PDFs, contracts, internal wikis, databases, archived emails — for the most relevant fragments for the user's question and injects them as context into the prompt. The model responds based on those retrieved fragments, not just on what it learned during pretraining.
The advantage is immediate: when you update a document, the next query already receives the new version. There is no retraining, no cycle of months. That is why RAG is the dominant solution for document search, internal knowledge bases, technical support assistants, and any case where information evolves.
The weak point of RAG is that the model remains a generalist. If your sector uses very specific terms the base model does not know, or if you need responses to follow a rigid format that is hard to describe in a prompt, the model may «slip». RAG also requires that the information already exists in some structured, retrievable document; it cannot learn implicit behaviours.
If you want to understand in more detail how the RAG architecture works applied to a company's documents, we recommend reading our article on RAG for businesses.
Comparison table: fine-tuning vs RAG in the SME
| Criterion | Fine-tuning | RAG |
|---|---|---|
| Initial cost | High (labelled data + GPU or training API) | Medium (vector indexing + search infrastructure) |
| Maintenance cost | High if information changes frequently (retraining cycles) | Low (update documents in the document base) |
| Time to first result | Weeks or months (prepare data, train, evaluate) | Days or weeks (index existing documents) |
| Information update | Requires a new training cycle | Immediate when the source document is updated |
| Traceability (cites the source) | No; the model «knows» but does not show where from | Yes; each response can indicate the document and paragraph |
| Own terminology and tone | Excellent; the model internalises them | Depends on the prompt; may require detailed instructions |
| Minimum training data volume | Hundreds or thousands of quality labelled examples | Any document corpus without additional labelling |
| Hallucination risk | The model can fabricate data not present in the examples | Lower: responses are anchored in real retrieved documents |
| GDPR / AI Act compliance | Training data must be anonymised or consented to | Documents remain in your infrastructure; easier to control |
When to choose fine-tuning: three clear cases
1. The model must master a very closed technical jargon
If you manufacture niche industrial components, operate in a sector with very specific regulations (aeronautics, pharmaceuticals, steel) or use vocabulary the base model does not recognise — internal acronyms, proprietary product names, non-standard units of measurement — fine-tuning lets you «teach the dictionary» to the model once and for all. From then on, each prompt can be much shorter and the model makes fewer interpretation errors.
2. The output format is rigid and complex
Suppose you need the model to generate delivery notes in an XML with fields at specific positions, or commercial proposals that follow the corporate template to the millimetre. With RAG you can include the template in the context, but if the format has many conditional rules, the generalist model gets confused. A model fine-tuned on hundreds of correct examples follows the format more robustly and with less supervision.
3. Latency is critical and the context is small
RAG adds a search step before each response: retrieving documents, reranking them, building the extended context. In real-time applications where every millisecond counts — a voice agent, for example — a fine-tuned model responds faster because it does not need that prior step. If the knowledge you need is finite and stable (a 50-page product manual that will not change for two years), baking that knowledge into the model weights may be more efficient in the long run.
When to choose RAG: three clear cases
1. Information changes frequently
Price catalogues, regulatory updates, court rulings, supplier rates, real-time inventory: any corpus that is updated monthly or faster makes fine-tuning unviable as the main mechanism. With RAG, replacing or adding a document is enough. The next query already reflects the change.
2. Traceability is mandatory
In regulated environments — healthcare, legal, banking, insurance — or simply when the team needs to know where an answer comes from in order to validate it, RAG is the only sensible option. Each response can include the exact reference: «According to the master agreement with supplier X, clause 4.2…». Fine-tuning cannot do that; the model «knows» something but cannot show the original paragraph.
3. You do not have enough labelled data
Preparing quality training data for fine-tuning requires human effort: selecting representative examples, correcting bad responses, balancing categories. For many SMEs that effort is prohibitive. RAG, on the other hand, can get started with the documents you already have — manuals, contracts, internal FAQs, archived emails — without any need for manual labelling.
The hybrid combination: when to use both at once
In mature projects, the optimal answer is not one technique or the other but both in layers. A common scheme: the base model is fine-tuned to acquire the company's tone, terminology, and formats, and is then connected to a RAG system that provides up-to-date information for each query. The result is a model that «speaks like the company» and «knows what is in today's documents».
This hybrid architecture makes sense when the interaction volume is high (justifying the investment in fine-tuning) and at the same time the information the system handles is dynamic (justifying RAG). For example: an internal assistant for a sales team of 80 people that queries the CRM in real time but always responds in the style and format the sales management expects.
If your company is exploring which AI architecture fits its current situation, our fine-tuning for SMEs service includes a diagnostic phase in which we analyse the use case, the volume of available data, and the total cost of each alternative before recommending the path forward.
Real market costs in 2025-2026
Giving exact figures without knowing the case is irresponsible, but publicly available market ranges help to size the decision:
- Fine-tuning via provider API (OpenAI, Google, Anthropic): published ranges through 2025 were between $0.003 and $0.025 per 1,000 training tokens depending on the chosen base model. As an indicative figure, a dataset of 100,000 examples with an average length of 500 tokens could cost between $150 and $1,250 in training compute alone. Note: from May 2026, OpenAI has restricted self-serve fine-tuning access to existing organisations; check the provider's official documentation before planning. Add to this the cost of preparing and cleaning the data (the human cost typically triples or quadruples the computational cost).
- Fine-tuning on own infrastructure (rented GPU): open-source models such as Llama 3 or Mistral can be fine-tuned on an 80 GB A100 for between $1.50 and $4 per hour. A training cycle of 4-8 hours costs between $6 and $32, but environment setup, evaluation, and iterations multiply that figure in practice.
- RAG with managed vector index (Pinecone, Weaviate Cloud, Azure AI Search): billing models vary — Pinecone adopted a per-write/read-unit model plus storage cost (approximately $3.60/GB/month) in 2025-2026. Exact figures depend on query volume; check the provider's current pricing page. As a reference, SME projects with up to 500,000 documents typically see costs in the $50–$150/month range on managed cloud.
- Self-hosted RAG (pgvector, Qdrant, Chroma on own server): infrastructure cost between €50 and €200/month for SME volumes (up to 500,000 documents), with the advantage of data sovereignty.
Reference sources: OpenAI Fine-tuning Pricing (2025), Google Vertex AI fine-tuning docs (2025), Pinecone Pricing (2026).
The GDPR and AI Act factor in the decision
The choice between fine-tuning and RAG is not purely technical; it has direct regulatory implications. If training data contains personal information about customers or employees, fine-tuning requires a solid legal basis under the GDPR (explicit consent or documented legitimate interest) and a Data Protection Impact Assessment (DPIA) when the processing is high risk. Under the AI Act, a model fine-tuned for internal use in sensitive categories may require additional technical documentation.
RAG, on the other hand, does not modify the model; it only controls which documents are shown to it. If those documents are already in your legal custody and do not contain special categories of data, the regulatory profile is simpler to manage. Data sovereignty is also clearer: documents remain in your infrastructure and the model never permanently «absorbs» the information.
Frequently asked questions
How many examples do I need for quality fine-tuning?
The practical rule used by major providers is a minimum of 50-100 high-quality examples to start seeing differences compared to the base model, and between 500 and 2,000 for robust results in specialised tasks. More important than quantity is consistency: contradictory or poorly labelled examples degrade the model. If you do not have that volume available, RAG is almost always the right starting point.
Can an SME without an internal technical team deploy RAG?
Yes, although it requires external support in the design phase. The most critical part is not technical but conceptual: deciding which documents to index, how to structure them, and how to evaluate that the responses are correct. A specialist consultant can have a working pilot within 2-4 weeks if the documents are well organised. Subsequent maintenance — updating documents, adjusting relevance — can be handled by the internal team without programming knowledge.
Does fine-tuning eliminate hallucinations?
No. Fine-tuning can reduce errors in the specific training domain, but the model remains susceptible to fabricating information outside that domain, or even within it if the training data contained errors. To minimise hallucinations, the most effective combination is RAG (the model responds based on real retrieved documents) together with an automated evaluation layer that detects responses without documentary support.
What about open-source models vs paid models?
For fine-tuning, open-source models (Llama 3, Mistral, Qwen) allow you to work on your own infrastructure with data that never leaves the corporate perimeter, which resolves confidentiality concerns at the root. Compute costs are comparable to or lower than those of paid models via API. The trade-off is that more technical capacity is needed to configure the training environment, carry out evaluation, and deploy the resulting model. For RAG, the choice of embeddings model (which converts documents into vectors) also influences retrieval quality: models specialised in Spanish such as those from the multilingual-e5 family typically outperform generic embeddings on Spanish-language corpora.