AI Knowledge Base
Calculate the real monthly cost of an AI-powered knowledge base. Compare providers, see where the money goes, and plan your budget — vendor-neutral, updated Q1 2026.
Why estimate RAG pipeline costs?
- See real infrastructure costs before committing to a RAG architecture
- Compare vector DB, LLM, and embedding costs across providers
- Avoid budget surprises — factor in scaling, reranking, and maintenance
All calculations run locally in your browser. No data is sent to any server.
1 Knowledge Base Configuration
2 Query Patterns
3 Architecture Choices
Your RAG Pipeline Estimate
Cost Breakdown
Key Insight
Optimization Potential
Monthly Cost by Scale
| Queries/day | Est. monthly |
|---|
RAG Component Cost Ranges (2026)
Each RAG pipeline component has a distinct cost structure. The table below shows price ranges based on public vendor pricing from Q1 2026 and CodeFormers implementation analyses.
| Component | Price Range | Example |
|---|---|---|
| Embeddings (API) | $0.02–$0.13/M tokens | 10K docs × 3K tokens = ~€3–€20 one-time |
| Vector database (managed) | €27–€400/mo | Qdrant €27/mo → Pinecone $70+/mo → Weaviate €45–400/mo |
| LLM inference | $0.10–$75/M tokens | DeepSeek $0.28/$0.42 → Claude Sonnet $3/$15 → GPT-5.2 $1.75/$14 |
| Reranking | $0.05/M–$2/1K | Optional. Voyage $0.05/M tokens → Cohere $2/1K queries |
| Application layer | €200–€2,000/mo | Compute, API gateway, monitoring, logging |
| Eval & monitoring | €100–€500/mo | LangSmith, Ragas, custom eval pipeline |
| Build cost (one-time) | €2K–€200K+ | Depends on tier: Basic €2-5K → Advanced €5-15K → Agentic €20-80K → GraphRAG €50-200K |
Source: Public pricing from OpenAI, Anthropic, Google, Cohere, Voyage AI, Pinecone, Weaviate, Qdrant (Q1 2026). Build costs based on 30+ CodeFormers RAG deployments.
Vector Database Comparison — Pricing & Features (Q1 2026)
Vector database choice is one of the key cost drivers in a RAG pipeline. The comparison below covers the most popular managed and self-hosted solutions.
| Database | Pricing | Free tier | Strengths |
|---|---|---|---|
| Pinecone Serverless | $0.33/GB + $16/M reads | 2GB free | Zero-ops, auto-scaling |
| Weaviate Cloud | €45–€400/mo | 14-day trial | Hybrid search, multi-tenant |
| Qdrant Cloud | ~€27/mo (1M vectors) | 1GB free | Lowest entry, Rust performance |
| Milvus / Zilliz Cloud | $0.06/CU-hr | Free tier available | GPU acceleration, billion-scale |
| ChromaDB | Self-hosted: free | Open source | Simplest dev setup |
| pgvector (PostgreSQL) | Free (extension) | Existing PG | No new infra, ACID |
RAG Architecture Tiers — From Basic to GraphRAG
RAG architecture dramatically impacts cost. Choose the tier appropriate for your query complexity — avoid overshooting. Most production use cases fit within Basic or Advanced.
| Tier | What it adds | Build cost | Monthly cost | Typical scale |
|---|---|---|---|---|
| Basic RAG | Retrieve + Generate, simple chunking | €2K–€5K | €50–€300/mo | 1K–10K/day |
| Advanced RAG | + reranking, hybrid search, eval pipeline | €5K–€15K | €200–€1,500/mo | 5K–50K/day |
| Agentic RAG | + multi-step reasoning, tool use, self-correction | €20K–€80K | €500–€5,000/mo | 10K–100K/day |
| GraphRAG | + knowledge graph, relationship extraction, community detection | €50K–€200K+ | €2,000–€20,000+/mo | 50K–1M+/day |
How Much Does RAG Cost Per Month at Different Scales?
RAG costs scale nearly linearly with query volume. Estimates below assume a typical configuration (OpenAI embedding small, Qdrant, GPT-4.1-mini as LLM) without optimizations. Smart routing and caching can reduce these figures by 30-50%.
| Scale | Basic RAG | Advanced RAG | Agentic RAG | GraphRAG |
|---|---|---|---|---|
| 1K queries/day | €50–€150 | €200–€500 | €500–€1,500 | €2,000–€5,000 |
| 10K queries/day | €200–€700 | €700–€2,500 | €2,500–€8,000 | €8,000–€25,000 |
| 100K queries/day | €1,500–€5,000 | €5,000–€15,000 | €15,000–€50,000 | €50,000–€150,000 |
| 1M queries/day | €10,000–€35,000 | €35,000–€100,000 | €100,000–€350,000 | €350,000–€1M+ |
CodeFormers estimates based on 30+ RAG deployments (2024-2026). Actual costs may vary depending on chosen LLM model, vector database, and configuration.
How This Estimate Works
The RAG Pipeline Cost Estimator calculates costs based on 5 main components: embeddings, vector database, LLM inference, reranking (optional), and application layer. Each component has its own pricing model.
The embedding model converts documents into numerical vectors. Cost = (document count × average tokens per document × price per million tokens). Chunking splits documents into smaller fragments (256-1024 tokens), increasing vector count but improving search relevance.
The vector database stores and indexes vectors for fast semantic search. Costs depend on data size (GB), read operations (queries), and the vendor's pricing model. Managed services eliminate DevOps costs but have higher operational fees.
LLM inference is typically the largest ongoing cost component. Cost = (queries/day × 30 × average tokens per query × price per million tokens). Query complexity affects token count: simple queries ~500 tokens, agentic ~5,000+ tokens. Optimizations (caching, smart routing, prompt caching) can reduce LLM costs by 30-50%.
Cost multipliers include: industry compliance (1.0-1.75x), multi-tenancy (1.25x), deployment complexity (1.0-2.5x). Optimization discounts: semantic caching (-40%), smart routing (-30%), prompt caching (-20%), up to 90% combined reduction. All calculations happen client-side — your data never leaves the browser.
Get Your RAG Pipeline Cost Report
Full cost model with infrastructure breakdown, provider comparison, and optimization tips.
Includes architecture decision record template
How the RAG pipeline cost estimator works
Define your data
Specify document count, average size, and update frequency for your knowledge base.
Choose architecture
Select embedding model, vector database, and LLM for query processing.
Get cost estimate
See monthly infrastructure costs, per-query pricing, and scaling projections.
Frequently Asked Questions: RAG Pipeline Costs
How much does it cost to build a RAG pipeline?
RAG pipeline build costs range from €2,000-€15,000 one-time for a basic system to €50,000-€200,000+ for an enterprise solution with GraphRAG. Monthly running costs start from €50-200/mo for a simple deployment (1K queries/day) to €5,000-20,000+/mo for a large-scale production system (100K+ queries/day).
What is the cheapest embedding model for RAG?
The cheapest embedding models are OpenAI text-embedding-3-small ($0.02/M tokens) and Voyage AI voyage-3-lite ($0.02/M tokens). For large knowledge bases, the difference between the cheapest and most expensive model (Cohere embed-v4 at $0.12/M) can mean a 6x difference in embedding costs.
Which vector database is cheapest?
Qdrant Cloud offers the lowest entry point (~€27/mo for 1M vectors). Weaviate Serverless starts at €45/mo with pay-as-you-go pricing. Chroma and Milvus Lite are free (self-hosted) but require infrastructure management. Pinecone starts at $0.33/GB + $16/M reads.
How to reduce LLM costs in a RAG pipeline?
Three most effective strategies: (1) Prompt caching — reduce to 20% of costs on repeated prefixes, (2) Smart routing — direct simple queries to cheaper models (DeepSeek V3.2 $0.28/M vs GPT-5.2 $1.75/M), (3) Semantic caching — cache responses to similar queries, reducing LLM volume by 30-40%.
What is reranking and is it worth paying for?
Reranking is an additional step after vector search that improves result relevance. Cohere Rerank 3.5 ($2/1K queries) is most expensive but most accurate. Voyage AI rerank-2 ($0.05/M tokens) offers the best quality-to-cost ratio. Reranking improves RAG accuracy by 10-25% at a cost of €50-500/mo.
How much does RAG cost for 10,000 documents?
For 10K documents (average 5KB) with 1K queries/day: one-time embedding ~€15-50, vector database €27-100/mo, LLM inference €100-500/mo (model-dependent), total ~€200-700/mo. System build is a one-time €5,000-15,000.
How does Basic RAG differ from Agentic RAG in cost?
Basic RAG (retrieve + generate) costs 3-5x less than Agentic RAG. Basic: simple retrieval + one LLM call. Agentic: multi-step reasoning, self-correction, tool use, meaning 3-10x more LLM tokens per query. GraphRAG adds another 2-5x for knowledge graph construction and maintenance.
How do RAG costs scale with increasing query volume?
RAG costs scale nearly linearly with query volume, primarily driven by LLM inference costs. Going from 1K to 10K queries/day, monthly costs increase ~8-10x. Smart routing and caching can reduce this curve by 30-50% by directing simple queries to cheaper models.