Why OpenAI API pricing is a product strategy, not a footnote

Timeline

1. Jun 17, 2026Build semantic search with OpenSearch vectors

   A step-by-step guide to set up OpenSearch vector search for semantic retrieval.

2. Jun 2, 2026AWS Adds Native Vector Support Across Databases

   AWS now supports vectors across Aurora PostgreSQL, MemoryDB, Bedrock Knowledge Bases, and Neptune for RAG and GraphRAG apps.

3. Jun 2, 2026Why Amazon S3 Vectors matters more as storage than search

   Amazon S3 Vectors is a storage-layer win, not a search-layer replacement, and AWS is right to position it that way.

4. May 16, 2026Vector Databases: How AWS Explains Them

   AWS explains how vector databases store embeddings, power similarity search, and support Bedrock apps with OpenSearch Service.

5. May 16, 2026How to Choose a Vector Database in 2026

   A practical guide to selecting a 2026 vector database by scale, pricing, and architecture.

6. May 15, 2026Why Gemini API pricing is cheaper than it looks

   Gemini’s sticker price is low, but the real cost is integration, caching, and model choice.

7. May 14, 2026Why OpenAI API pricing is a product strategy, not a footnote seed

   OpenAI API pricing is a product strategy, not a footnote, and teams should treat it that way.

8. May 5, 2026AWS Bedrock Knowledge Bases simplifies RAG

   Amazon Bedrock Knowledge Bases helps teams build RAG apps with managed ingestion, retrieval, citations, and structured-data queries.