AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Specify Dataset Size
Enter document count and average tokens per document, or provide total token volume directly.
2. Select Embedding Provider
Choose between OpenAI text-embedding-3-small, text-embedding-3-large, Cohere Embed v3, Voyage-3, or Gemini Text-Embedding-004.
3. Configure Re-Indexing Cadence
Enter projected monthly data churn or new document creation percentage to estimate recurring costs.
4. Compare Provider Rates
Review the dynamic comparative table displaying total cost, dimensions, and memory usage per provider.
Formula or logic used
Embedding Cost & Memory Formulas
Embedding APIs charge purely per input token processed. Storage costs depend directly on the vector dimension and precision format (Float32 = 4 bytes per float).
Total Tokens = Document Count × Average Tokens Per DocumentOne-Time Ingestion Cost = (Total Tokens / 1,000,000) × Price Per 1M TokensMonthly Update Cost = One-Time Cost × (Monthly Update Rate % / 100)Raw Vector Memory (MB) = (Document Count × Dimensions × 4 bytes) / (1024 × 1024)Indexed Memory with HNSW Graph (MB) = Raw Vector Memory × 1.5
Examples
Example 1: 10 Million Token Enterprise Docs Archive
At $0.02 per million tokens, modern small embedding models make indexing massive text repositories remarkably economical.
Example 2: High-Dimensional Research Corpus (100M Tokens)
While API fees remain under $15, the 3,072-dimensional vector memory footprint requires proper database RAM provisioning.
Common use cases
RAG Proof-of-Concept Budgeting
Demonstrate accurate cloud infrastructure forecasts to leadership before vector database migrations.
Embedding Model Selection
Compare cost-to-performance tradeoffs between 1536-dim small models and 3072-dim large models.
E-Commerce Catalog Search Sizing
Calculate re-embedding costs for daily product description updates across 500,000 SKUs.
Database RAM & Tier Sizing
Determine whether your vectors fit within free-tier pgvector instances or require dedicated managed nodes.
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Frequently asked questions
How much does OpenAI text-embedding-3 cost?
OpenAI text-embedding-3-small costs $0.02 per 1 million tokens (1,536 dimensions). The larger high-accuracy model, text-embedding-3-large, costs $0.13 per 1 million tokens (3,072 dimensions).
Can I reduce vector dimensions to save money?
Yes. OpenAI's text-embedding-3 models support dimension reduction via MRL (Matryoshka Representation Learning). You can truncate text-embedding-3-small to 512 dimensions with minimal loss in accuracy, reducing vector database storage by 66%.
Do I pay for embeddings every time a user searches?
Yes, but query embeddings are negligible in cost. A user search query of 20 tokens costs approximately $0.0000004 to embed before querying your vector index. The main cost is the initial corpus indexing.
How does self-hosted embedding compare to OpenAI?
Open-source embedding models (e.g. BAAI/bge-small-en-v1.5) eliminate API charges, but require server GPU/CPU resources. For datasets under 100 million tokens, managed API embeddings are almost always cheaper than hosting a dedicated inference server.
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