RAG Pipeline Cost · for RAG builders & CTOs

Full RAG stack cost - in one calculator

Embeddings + vector DB + rerank + generation, with prompt-cache savings modeled. Pick a preset or build your own stack across 9 embedding models, 8 vector DBs, and 132 generation models.

Pricing verified: 2026-07-22 5-stage cost model Cache-aware
What this calculator does

End-to-end cost of a RAG stack — embeddings + vector DB + rerank + generation + prompt cache — in one place.

Why use it
  • See which of the 5 stages actually dominates your bill (usually generation)
  • Compare 4 pre-built vendor stacks (Budget, Balanced, Premium, Self-host) at your workload
  • Avoid the classic RAG cost traps: top-K bloat, missing prompt cache, aggressive re-indexing
  • Get a shareable URL that captures every input — send to your team as a decision artifact

New to this calculator? Start with the ⚡ Playground — a few sliders, instant ballpark. Then switch to the 🧮 Calculator for your exact number.

Two ways to use this: visualize in the Playground, then get your number in the Calculator.

▶Playground A quick, visual way to see which factors move your result the most. Open the playground → ▤Calculator Enter your real workload for a precise result you can apply to your own usage. Go to the calculator →
RAG Pipeline Playground
Drag the sliders for an instant estimate.
What will a RAG pipeline cost?

Query-time LLM calls dominate; indexing is a small one-time slice. Balanced tier, ~30 days assumed.

Estimated monthly cost
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Change the input sliders below to see new estimates.

How we got this estimate
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💡Cost = queries × (retrieved + output tokens) × rate × ~30 days. More retrieved context means more to read every single query.

A RAG stack has many cost layers — embeddings, storage, retrieval, generation. Enter your numbers below to see the full monthly picture.

RAG Pipeline Calculator

Enter your exact numbers for a precise result.
📊 Not sure of a value? Fields with a ▾ Typical pill offer broad industry ballparks (sourced typical ranges; Doc sizes: tech docs ~5K tokens, legal ~31K, long reports ~80K; dense pages ~260-520 tokens.) so you can move forward now — your result gets more accurate as you replace them with your own measured numbers. Values marked * are rough estimates.
🎮 Interactive Guide & Calculator Playground →
🎛 CALCULATOR
🧩 Your RAG workload

Pick your use case, then a tool stack — tweak anything after.

1 · What's your RAG use case? (sets your workload)

2 · Pick a tool stack (sets the models)
Hint: Searches per month that hit your docs.
Hint: Lookups per question. Simple Q&A = 1; agent-style = 2-3.
Hint: How many documents are in your set. A round number is fine.
Hint: Length of a typical doc, in words. A one-pager ~600.
Hint: How many times a year you rebuild the whole index.
Hint: How often the same context repeats across questions. 0 if every query is fresh.
Hint: The model that turns docs into searchable form.
Hint: Which search-index service stores the vectors.
Hint: The model that writes the final answer.

Results

📈 RESULTS
Monthly cost for this stack
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📥
Ingest
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🗄️
Vector DB
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🔎
Query embed
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🎯
Rerank
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🧠
Generation
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💡 Recommendations
    📋 Compare all 4 preset stacks at your workload

    Same queries, same corpus - vendor mix varies. Green row = cheapest, gold = your current config.

    Stack Components Per query Monthly Annual
    Vector DB deep-dive → Embedding model comparison → RAG vs Fine-Tuning → Get a RAG architecture review →
    🎯 Use this result to
    • 📚 Budget your RAG pipeline — Embedding + retrieval + completion + reranking. Full TCO not just LLM cost.
    • 🔍 Find your cost bottleneck — Usually one stage dominates. Calc surfaces which and how much it costs.
    • 📈 Project at scale — Linear at first, non-linear above 10K queries/day. See your scaling curve.
    • 🔌 Integrate with your AI agents — MCP available for agentic workflow integration. Cost-aware RAG routing.
    📅 Schedule a call to apply this to your workload
    📋 What now?
    • Generation usually dominates — at scale the LLM answer call is most of the bill, so routing the generation model (or raising cache hit rate) moves cost far more than tweaking chunking.
    • Embeddings are mostly one-time — indexing is paid once; only re-indexing and query embeddings recur, so a big corpus is cheaper to run than it looks.
    • Top-K and rerank are quality/cost dials — higher top-K and a reranker improve recall but add per-query cost; tune against real answer quality.
    📅 Book a working session to apply this to your workload →

    Go deeper

    Our playbooks on cutting this number.

    🗄️
    Vector DB Cost
    Deeper dive into just the DB layer
    🧬
    Embedding Cost
    Pick the right model to pair
    ⚖️
    RAG vs Fine-Tune
    When is a DB even needed?
    💾
    Prompt Cache ROI
    Is caching worth turning on?

    Need help using this calculator for your workloads?

    AICost.ai has 50+ calculators and playbooks. Schedule an AvatarVA meeting and we'll work through your real cost scenarios across AI & Cloud: visibility, cost reduction, optimization, forecasting and capacity planning, without sacrificing accuracy or performance.

    📅 Schedule an AvatarVA meeting →
    📖 Data sources & methodology 163 text models · 9 embeddings · 37 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-07-28

    Methodology

    • All prices are USD per 1 million tokens, current as of 2026-07-28.
    • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
    • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
    • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
    • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
    • Long-context pricing tiers apply when input exceeds model threshold.
    • Embedding prices are input-only (no output tokens generated).

    Primary sources

    Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

    Anthropic
    2026-07-28
    https://www.anthropic.com/pricing
    Daily snapshot since Sep 2023 · 631 days captured
    Anthropic Docs
    2026-07-28
    https://platform.claude.com/docs/en/about-claude/pricing
    Daily snapshot since Sep 2023 · 631 days captured
    OpenAI
    2026-07-28
    https://openai.com/api/pricing/
    Daily snapshot since Sep 2023 · 632 days captured
    Google AI
    2026-07-28
    https://ai.google.dev/gemini-api/docs/pricing
    Daily snapshot since Dec 2023 · 607 days captured
    Google Vertex
    2026-07-28
    https://cloud.google.com/vertex-ai/generative-ai/pricing
    Daily snapshot since Dec 2023 · 607 days captured
    DeepSeek
    2026-07-28
    https://api-docs.deepseek.com/quick_start/pricing
    Daily snapshot since May 2024 · 546 days captured
    xAI
    2026-07-28
    https://x.ai/api
    Daily snapshot since Nov 2024 · 464 days captured
    Mistral
    2026-07-28
    https://mistral.ai/pricing
    Daily snapshot since Dec 2023 · 605 days captured
    Cohere
    2026-07-28
    https://cohere.com/pricing
    Daily snapshot since Sep 2023 · 631 days captured
    Voyage AI
    2026-07-28
    https://docs.voyageai.com/docs/pricing

    Inferred values (marked with * in calculator tables)

    Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

    Vendor / Model Field Why it’s inferred
    Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
    Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
    Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
    Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
    Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
    OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
    OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
    OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
    OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
    OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
    OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
    OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
    OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
    OpenAI — GPT-5.2 batchInput Derived at 50% of input.
    OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
    OpenAI — GPT-5 cachedInput Derived at 10% of input.
    OpenAI — GPT-5 batchInput Derived at 50% of input.
    OpenAI — GPT-5 batchOutput Derived at 50% of output.
    OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
    OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
    OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
    OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
    OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
    OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
    OpenAI — GPT-5.1 batchInput Derived at 50% of input.
    OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
    OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
    OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
    OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
    OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
    OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
    Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
    Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
    Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
    Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
    Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
    Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
    Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
    Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
    Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
    Google — Gemini 2.0 Flash cachedInput Derived at 25% of input per Google 2.0 family caching rates.
    Google — Gemini 2.0 Flash batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
    Google — Gemini 2.0 Flash batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
    Google — Gemini 2.0 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
    Google — Gemini 2.0 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
    Google — Gemini 2.0 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
    xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

    Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →