Pierre KasparianAI & Data freelancer
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ToolsRAG & LLM Cost Calculator

Free · Real-time calculation

RAG & LLM Cost Calculator

Estimate your LLM architecture budget in seconds. Calculate input tokens, output tokens, and vector indexing costs for GPT, Claude and Mistral.

Project parameters

LLM model

OpenAI

Input $3.50/1M · Output $15.00/1M tokens

Anthropic

Input $3.00/1M · Output $15.00/1M tokens

Mistral AI

Input $1.50/1M · Output $7.50/1M tokens

Custom model

Monthly cost estimate

Real-time calculation based on your parameters and official pricing.

Estimated monthly LLM cost

$112.69

$0.02 per query

Input tokens (question + RAG context)

$56.44

16.1M tokens

Output tokens (generated responses)

$56.25

3.8M tokens

Document indexing cost (one-time)

< $0.01

Computed via OpenAI text-embedding-3-small ($0.02/1M tokens)

Queries / month

8K

Input tokens / query

2K

Output tokens / query: 500

Indicative estimate. Prices may change. Check official pricing grids before budgeting.

FAQ: optimising LLM token costs for enterprises

How can I reduce token costs for a RAG project?

Several levers: optimise chunking (smaller chunks = less injected context), filter low-relevance chunks with a high similarity threshold, compress context via a lightweight LLM before the main model (RAG-Fusion pipeline), and route simple queries to a cheaper model.

GPT-4o or Claude Sonnet 4.6: which is more cost-effective for RAG?

For simple, high-volume queries, Mistral Large 3 or Claude Sonnet 4.6 offer the best quality-to-cost ratio. GPT-4o and Claude Opus 4.6 suit complex tasks (long document analysis, multi-step reasoning). A smart routing strategy can cut your bill by 3 to 5x.

What does a vector database cost?

Pinecone Serverless charges on usage (~$0.096/million vectors/month). Self-hosted Qdrant or Weaviate on OVHcloud or Scaleway cost ~€10-30/month in infrastructure. For GDPR-compliant projects, EU self-hosting is strongly recommended.

Do embeddings need to be recomputed on every corpus update?

No. Only new or modified documents require recomputation. Most RAG architectures implement delta-indexing: only added or changed chunks are re-embedded, drastically reducing recurring indexing costs.

Is on-premise hosting really cheaper than cloud APIs?

Above ~100,000 queries/month, a self-hosted open-source LLM (Mistral, Llama 3) on OVHcloud or Scaleway GPU becomes cheaper than a cloud API. Dual benefit: lower marginal costs and GDPR compliance (data stays outside the US). The break-even point depends on the model and infrastructure chosen.

Understanding RAG project costs in production

A RAG (Retrieval-Augmented Generation) system combines a vector database with a language model to query your internal documents. Total cost breaks down into three components: initial document indexing (embeddings), input tokens consumed per query (question + retrieved context), and output tokens generated by the LLM (response). This calculator estimates all three in real time using official GPT-4o, GPT-5.5, Claude Sonnet 4.6, Claude Opus 4.6 and Mistral pricing.

Which LLM to choose for a GDPR-compliant RAG?

For French and European companies subject to GDPR, choosing an LLM goes beyond cost per token. Mistral AI, a French company, offers open-source models that can be self-hosted on sovereign infrastructure (OVHcloud, Scaleway): data never leaves the EU. Mistral Large 3 at $0.50/1M input tokens is currently the most cost-effective option for high-volume RAG with EU hosting. Claude Sonnet 4.6 and GPT-4o remain relevant for complex use cases via API, but involve data transfer to US servers — which must be contractually framed (DPA, SCC clauses).

How to reduce LLM token costs for an enterprise RAG?

Several techniques can cut your LLM bill by 3x to 10x: adaptive chunking (256 to 512 tokens per chunk depending on document density), cosine similarity threshold filtering to inject only truly relevant passages, context compression via a lightweight model before the main LLM call (RAG-Fusion pipeline), and intelligent routing that sends simple queries to Mistral Large 3 and complex ones to Claude Opus 4.6. A RAG architecture audit typically uncovers 50 to 80% savings without degrading answer quality.

Cost of a RAG chatbot on internal documents: ballpark figures

For a 50-user SMB sending 5 questions per day (roughly 7,500 queries/month), monthly RAG cost with Claude Sonnet 4.6 is around $100 using this calculator's default assumptions. With Mistral Large 3 on sovereign hosting, token API cost drops to about $14 per month, plus GPU infrastructure fees (~€30/month on OVHcloud). Initial indexing cost (embeddings) stays under one cent for a 500-page corpus and is a one-time expense.