RAG API Cost Ranking
Every model ranked by cost for a typical RAG workload: 1,000 queries/day, 3,000 input / 500 output tokens per query.
Top Picks by Scale
Small RAG App (under $100/month)
Gemini 2.5 Flash-Lite$46.80/mo
DeepSeek V4 Flash$58.80/mo
GPT-4o mini$75.00/mo
Production RAG ($150-400/month)
Claude Haiku 4.5$189.00/mo
DeepSeek V4 Pro$180.00/mo
Gemini 2.5 Pro$249.00/mo
Enterprise RAG ($500+/month)
GPT-5$397.50/mo
Claude Sonnet 4.6$495.00/mo
GPT-5.5$1,485.00/mo
Strategy: Context-Aware Routing
RAG queries vary in complexity. Use context-aware routing — route simple lookups to cheap models, complex reasoning to premium models.
Smart RAG Pipeline
60% simple lookup (short context) → Gemini Flash Lite$19.44/mo
30% moderate (multi-chunk) → GPT-4o mini$20.25/mo
10% complex reasoning → Claude Sonnet ($3/$15)$24.75/mo
Total with routing$64.44/mo (vs $495 on Claude Sonnet)
Context-aware routing saves 87% compared to using Claude Sonnet for everything. Most RAG queries are simple fact retrieval — only complex reasoning needs premium models.
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Open Savings Calculator →Key Factors When Choosing a RAG API
- Input token price is critical: RAG is extremely input-heavy — retrieved context (2,000-8,000 tokens) goes into the input. A typical RAG query sends 3-5× more input than output tokens.
- Context window: More retrieved chunks = better answers but higher cost. Models with large context (Gemini: 1M) let you retrieve more without hitting limits.
- Latency: RAG adds latency from retrieval + generation. Budget models are faster, helping offset retrieval overhead. Users expect sub-3-second total response.
- Quality vs cost: Simple Q&A works on budget models. Multi-hop reasoning and synthesis benefit from mid-tier models. Reserve premium for complex analytical queries.
- Caching: Cache frequent queries and their retrieved context. Many RAG systems have 30-50% cache hit rates, cutting costs proportionally.
- Context compression: Summarize or compress retrieved chunks before sending to the LLM. Can reduce input tokens by 40-60% with minimal quality loss.
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