Embedding Cost Calculator
Compare embedding costs across providers: OpenAI, Cohere, Voyage AI, and open-source alternatives.
How It Works
- Estimate your volume: Indicate the number of tokens to process per month.
- Compare providers: Visualize costs from each embedding provider.
- Choose the best: Identify the optimal model based on your criteria: price, quality, dimensions.
Frequently Asked Questions
- Which embedding model should I choose for my RAG?
- To start, OpenAI's text-embedding-3-small offers the best value. For maximum performance, text-embedding-3-large or Voyage AI. For free self-hosted, BGE or Nomic.
- Are free embeddings as good as paid ones?
- Open-source models (BGE, Nomic, E5) rival paid models on MTEB benchmarks. The difference is in ease of integration and multilingual support.
- How can I reduce my embedding costs?
- 1) Cache embeddings to avoid recalculating. 2) Use longer chunks. 3) Switch to a cheaper model. 4) Filter redundant documents before embedding.
- What's the difference between 1536 and 3072 dimensions?
- More dimensions = more semantic nuances captured, but also more storage and compute. For most cases, 1536 dimensions suffice. 3072 is useful for very complex queries.
- Can I change embedding model later?
- Yes, but you'll need to recalculate all embeddings as vectors aren't compatible between models. Plan this migration with a versioning system.
- Voyage AI vs OpenAI: which is better?
- Voyage AI slightly outperforms OpenAI on MTEB 2024 benchmarks, especially for code and technical documents. OpenAI remains easier to integrate with better multilingual coverage.
