Home » Leader in open-source vector-search technology announces a new pricing model for its Weaviate Cloud Service

Leader in open-source vector-search technology announces a new pricing model for its Weaviate Cloud Service

by Tech Reporter
1st Sep 22 5:08 pm

SeMI Technologies, the leader in open-source vector-search technology, announces an entirely new pricing model for its Weaviate Cloud Service.

Inspired by “pay-as-you-grow” pricing used in cloud storage, SeMI has introduced a new business model that makes it easier and more affordable for enterprises of any scale to take advantage of its Weaviate vector-search engine.

Beginning today, users have the option of paying a usage-based rate for search on a per-dimension basis. Pricing begins at $0.05 per million dimensions. (No, that’s not a typo; the rate is five cents per million.)

Read more tech news:

T-Mobile’s latest network testing offer highlights how eSIM is changing the competitive landscape

Kyle Roche withdraws from crypto class action suits after alleged ‘gangster style’ attacks on firms following ‘leaked videos’

Seven technologies to watch in 2023

Twitch’s global in-app revenues have plunged by 52% YoY to $47.9m in Q2 2022

SeMI Technologies’ co-founder Bob van Luijt told LondonLovesTech.com, “At this point, as we’ve reached 1,500,000 downloads, a lot of people know and love our open-source software, but they want us to run it for them.

” So, we’ve created a ‘NoOps’ option that allows them to pay only for what they use on our optimized Weaviate Cloud Service.”

In addition to remarkably convenient access to the latest vector-search capabilities, Weaviate Cloud Service customers get dedicated customer support on a private Slack channel.

However, consistent with SeMI’s commitment to creating truly open-source software, customers using the free service will always be able to access all of the Weaviate vector-search engine’s capabilities.

SeMI Technologies’ Weaviate vector-search engine is an example of a “third wave” database technology. Data is processed by a machine learning model first, and AI models help in processing, storing, and searching through the data.

As a result, Weaviate excels at answering questions in natural language, but it is not limited to language; it is as adaptable to searching images or even genetic information.

“Depending on the machine-learning model used, a “document”—basically a data object—in a vector database typically has anywhere from 120 to 12,800 dimensions,” van Luijt added.

“Since vector dimensions are the lowest common denominator, it makes sense for vector dimensions to be the basis for cost—as opposed to, say, API calls.

“We feel that this very transparent and predictable pricing model is consistent with our open-source philosophy.”

Leave a Comment

You may also like