Estimate the cost of generating and storing vector embeddings for RAG applications.
Total Tokens to Embed: 5.00 Million
* This is the cost to run your data through the API once.
* Vector DB pricing is complex and depends on pod type, region, and availability (High Availability doubles cost).
Embeddings are vector representations of text. They turn sentences into lists of numbers (vectors) that computers can compare. If two vectors are close together, the text meanings are similar. This is the backbone of Semantic Search and RAG.
Pinecone is a fully managed, closed-source service known for ease of use and reliability. Weaviate is open-source and can be self-hosted, but also offers a cloud service. Weaviate typically offers more flexibility with hybrid search (keyword + vector).
The number of dimensions (e.g., 1536 for OpenAI) determines how much storage you need. Higher dimensions capture more nuance but require more RAM and disk space in your Vector DB, directly increasing monthly costs.
1. Use smaller or quantized models (e.g. text-embedding-3-small). 2. Filter data before embedding (don't embed noise). 3. Use a serverless vector DB (like Pinecone Serverless) that charges by "Read/Write units" rather than hourly pod time if your traffic is low.
Estimate embedding generation cost and compare stored vector-database plan examples.