A technical observation
Keyword search hits its limits the moment you need to retrieve meaning. Two close phrasings do not always share a single common term.
Embedding models solve that problem. They project text, images or signals into a vector space where proximity stands for similarity. What remains is querying those vectors quickly, and at scale.
That is Vela's job. You send your embeddings, we index them, and we answer similarity queries in milliseconds, up to a billion vectors per index. One single API, no cluster to administer.
One single API
Indexing, filtering and queries behind a single entry point
Managed service
No cluster to size, replicate or upgrade
Measurement
Published latency and recall, verifiable on your own datasets
Legible costs
Billing tied to vectors stored and queries served