deploybase

Fine-Tuning vs RAG: When to Use Which (Cost Analysis)

Deploybase · April 10, 2025 · LLM Guides

Contents

Fine-tuning: weights change, model updates

Fine Tuning vs RAG is the focus of this guide. Fine-tuning trains existing model weights on the data. Start with base model like Llama 3.1 70B. Add the documents. Train for 1-5 epochs. Model learns domain-specific patterns.

Cost structure: GPU hours + data preparation.

GPU hours: depends on model size and data volume.

  • 7B parameter model: $50-200 to fine-tune on 10K examples
  • 70B parameter model: $500-2000 on same data
  • 405B parameter model: $2000-5000+

Data preparation: hiring, labeling, cleaning. Often exceeds GPU costs.

Result: permanent model. Deployed on RunPod, Lambda, or locally. Pay once. No per-query costs (except inference).

RAG: retrieval augmented generation

RAG queries a database before generation. Prompt includes relevant documents. Model generates response using both prompt and documents.

Cost structure: retrieval + generation.

Retrieval: vector database. Pinecone, Weaviate, Milvus. $0.01-0.10 per query typically.

Generation: API call to LLM. $0.001-0.01 per token output.

Example: 100 queries daily for a year.

RAG: 100 × 365 × $0.11 (retrieval + generation) = $4,015/year

No upfront cost. Scales with usage.

Cost analysis: fine-tuning vs RAG

Scenario 1: Small company, 100 customers, 1000 queries/month

Fine-tuning:

  • Model: Llama 3.1 7B ($100)
  • Data prep: $1,000
  • Inference cost: $50/month (assuming owned GPU or cheap API)
  • Total year one: $2,000

RAG:

  • Vector DB: $50/month
  • API calls: 100 queries × $0.11 = $11/query month = $1,100/month
  • Total year one: $14,400

Fine-tuning wins. Upfront cost paid back in 3-4 months.

Scenario 2: Enterprise, 1M queries/month

Fine-tuning:

  • Model: Llama 3.1 70B ($1,500)
  • Data prep: $15,000
  • Inference: $1,000/month (on CoreWeave or owned hardware)
  • Total year one: $43,500

RAG:

  • Vector DB: $500/month
  • API calls: 1M × 30 × $0.11 = $3,300/month
  • Total year one: $45,600

Costs parity. Other factors decide.

As of March 2026, fine-tuning becomes cheaper at 10K+ queries monthly. RAG cheaper for lower volumes.

Quality differences

Fine-tuning: model weights memorize training patterns. Understands domain deeply. Reduces hallucination for specific tasks. Struggles on out-of-domain queries.

RAG: model uses retrieved documents as facts. Grounding reduces hallucination. Struggles if documents don't contain answer. More flexible but slower.

Example: customer support for SaaS product.

Fine-tuning:

  • Training: 5,000 support tickets + responses
  • Result: model knows product deeply. Answers 80% of questions without documents.

RAG:

  • Source: ticket database + documentation
  • Result: model answers using tickets as examples. Requires exact document match.

Fine-tuned model faster, more confident. RAG more flexible to documentation changes.

Speed comparison

Fine-tuning: inference speed identical to base model. Llama 70B inference takes same time whether fine-tuned or not.

RAG: adds retrieval overhead. Vector search takes 50-200ms. Generating with larger context takes longer too.

Anyscale/Groq inference: 5-10 tokens/second. Adding 1-2 second RAG overhead per query slows experience.

Fine-tuned model locally: 20-30 tokens/second on GPU. Faster overall.

For latency-sensitive applications, fine-tuning wins.

When to choose fine-tuning

Fine-tune when:

  • Same query patterns repeat frequently
  • Domain knowledge is clear and consistent
  • Inference latency matters
  • Data privacy requires on-premise models
  • Cost scales predictably (volume is known)

Examples:

  • Product documentation Q&A
  • Customer support for specific service
  • Code generation for the codebase
  • Domain-specific classification

When to choose RAG

RAG wins when:

  • Data changes frequently
  • Query types vary widely
  • No infrastructure for model hosting
  • Cost should scale with usage, not volume
  • Compliance requires audit trails of sources

Examples:

  • Multi-tenant search across many documents
  • Research paper analysis
  • News summarization
  • Medical record retrieval

Hybrid: fine-tune + RAG

Best systems use both.

Fine-tune on: domain understanding, style, format preferences. RAG on: specific facts, recent data, audit requirements.

Cost: fine-tuning one-time + RAG per-query.

Quality: combines fine-tuning's domain knowledge with RAG's freshness.

Example: legal research system.

  • Fine-tune on 10 years of case law
  • RAG new cases, recent rulings
  • Generate legal memo with both

FAQ

Q: How much training data do I need for fine-tuning? Rules of thumb: 100 examples minimum (risky). 1,000 examples reliable. 10,000 examples excellent. Quality matters more than quantity.

Q: Can I fine-tune without labeled data? Yes. Unsupervised fine-tuning on raw documents works but is weaker. Supervised (labeled) fine-tuning stronger but requires labeling.

Q: How long does fine-tuning take? 7B model: 1-2 hours. 70B model: 4-8 hours. 405B model: 16-24 hours. Depends on hardware and data size.

Q: Can I fine-tune continuously as new data arrives? Yes. Incremental fine-tuning works. Start with base model, add new data in batches. Drift happens over time (model forgets old patterns), but practical for 6-12 month windows.

Q: Is RAG really just keyword search with retrieval? No. Modern RAG uses semantic search via embeddings. Finds documents similar in meaning, not just keywords. Much better than keyword search alone.

Q: How often should I retrain a fine-tuned model? Depends on data drift. If domains stable (product docs, company policies), yearly retraining sufficient. If volatile (news, social media), monthly or weekly.

Q: Can I RAG using my own documents on an API? Yes. Upload docs to Anthropic, OpenAI, or open-source tools. All support document retrieval in prompts. Simpler than self-hosting RAG infrastructure.

Sources