Choosing Between RAG and Fine-Tuning: How to Evaluate the Right Fit for Your Workload
Retrieval-Augmented Generation (RAG) and fine-tuning are two powerful approaches for building domain-aware GenAI applications — and while they both offer compelling value, choosing the right one can be the difference between a scalable, cost-effective solution and a long-term maintenance burden.
As enterprise teams explore GenAI integrations, one common decision point arises early: Should we ground a foundation model using RAG, or invest in fine-tuning a model on our domain data? Let’s explore how to make that call — with a look at how Oracle’s evolving AI stack, including Oracle Database 23ai, supports both paths.
Understanding the Trade-Offs
The general wisdom is that RAG is easier and more flexible, while fine-tuning provides lower latency and cost per query at scale. That’s mostly true — but the right decision depends on the nature of the workload.
Here’s how we break it down:
Use RAG when:
Your domain knowledge changes frequently (e.g., new documentation, regulations)
You want to show source citations for trust and traceability
Your documents are large or complex (e.g., spreadsheets, PDFs, contracts)
You need fast time-to-market and low operational overhead
Consider fine-tuning when:
Your domain is relatively static
The inference volume is high, and latency matters
Your prompts and completions are structured and repeatable (e.g., classification, decision support)
You want to optimize for smaller model sizes and deploy on edge or cost-sensitive environments
Real-World Example:
RAG provides a low-friction entry point into GenAI for domain-specific applications. But as with many enterprise systems, what begins as a test workload often evolves into a high-demand production workload. As usage scales, performance bottlenecks can emerge — and GenAI applications are no exception.
Take this example of an Oracle GenAI workload: a RAG-based solution is implemented to serve a stable corpus of product specifications and policies. RAG is chosen for its flexibility and lower barrier to entry compared to fine-tuning. However, as adoption grows and the application matures, latency issues begin to surface — particularly during embedding lookups and retrieval across thousands of documents.
Given the static nature of the source content, fine-tuning a smaller domain-specific model using Oracle's native capabilities becomes the logical next step. The goal: a targeted 10x improvement in inference speed without compromising answer quality — all while reducing dependency on real-time document retrieval.
Oracle’s GenAI Stack: RAG and Fine-Tuning Together
Oracle has made significant strides in supporting both RAG and fine-tuning within their AI ecosystem:
Oracle 23ai + RAG
With Oracle Database 23ai, customers can:
Store and manage embeddings natively in the database
Use vector search directly in SQL, including hybrid semantic + keyword search
Integrate retrieved documents into prompts, enabling RAG flows with minimal external infrastructure
This allows you to keep data governance tight while still leveraging powerful GenAI workflows. If you're already an Oracle customer, this is a game-changer for AI-enabling your structured and unstructured data.
Oracle + Fine-Tuning Support
For more specialized workloads, Oracle also supports fine-tuning of open-source models, including Cohere models. You can:
Train on your domain-specific data using Oracle’s GPU-backed infrastructure
Deploy custom fine-tuned models using Oracle AI services
Fine-tuning is particularly useful when latency, offline inference (i.e. when live retrieval via RAG is not needed), or domain compression (i.e. when the model internalizes domain-specific content) is critical — and Oracle provides both the tooling and the security model to do this responsibly.
Blended Strategy: Best of Both Worlds
Many of our enterprise workloads ultimately adopt a hybrid architecture:
Use RAG for evolving, citation-sensitive queries
Deploy fine-tuned models for fast, high-volume, and stable tasks
With Oracle’s AI ecosystem — especially 23ai’s vector-native capabilities and fine-tuning support — it’s increasingly easy to manage both within the same data platform.
Decision Factors: RAG vs. Fine-tuning
The following table summarizes several important decision factors when evaluating GenAI workloads for RAG vs. fine-tuning.
Final Thought
There’s no one-size-fits-all answer — but choosing between RAG and fine-tuning doesn’t have to be a shot in the dark. By assessing the stability of your domain, your performance targets, and how your workload will scale, you can architect a GenAI solution that’s truly fit for purpose.
Oracle’s GenAI ecosystem supports both approaches — offering the flexibility to move fast today and the efficiency to scale intelligently tomorrow. And as your workload evolves, Oracle evolves with you.