OmniXAI vs Vertex AI Vector Search

OmniXAI has no write-up on Specifiction yet; its figures are above.

Vertex AI Vector Search is a cloud vector search engine for semantic search, recommendations, and generative AI applications. It uses Google Research’s ScaNN algorithm and supports dense embeddings, sparse embeddings, and hybrid search combining semantic and keyword methods. Numeric and text attribute filters can restrict results. Indexes can be deployed to public or private endpoints, including Private Service Connect, which provides private access from multiple user VPC networks. Documented integrations include RAG Engine, Cloud Storage, Cloud Run, TensorFlow Recommenders, and LlamaIndex. Google documents Python and Java clients, a Python SDK, and REST workflows. Security controls include VPC Service Controls and customer-managed encryption keys. Listed uses include retrieval-augmented generation, chatbots, recommendations, and multimodal search. Pricing is usage based: serving uses node-hour charges, with separate build, update, and streaming charges. Build and update data costs $3.00/GiB; streaming inserts cost $0.45/GiB.