Compliance is hard to get around. There are products that have done this by sheer force of their cyber security budgets such as large cloud providers which are HIPAA compliant. As a result, companies that require thorough compliance measures are simply not our target audience.
Your second point about performance is critical to almost all of our target audience. First of all, there are self-hosted versions of this such as Facebook’s FAISS (has GPU acceleration), Spotify’s ANNOY, Elastic search KNN search and Milvus. We plan to optimize performance using the latest ANN algorithms, caching, being as close to the wire (Non-REST) transmission of vectors, dimensionally reduction e.t.c. At the end of the day, our stack will probably not be as fast as a self-hosted version but it’ll confer other advantages that we hope ML engineers will find useful similarly to AWS RDS or DynamoDB.
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You bring up two viable points of view.
Compliance is hard to get around. There are products that have done this by sheer force of their cyber security budgets such as large cloud providers which are HIPAA compliant. As a result, companies that require thorough compliance measures are simply not our target audience.
Your second point about performance is critical to almost all of our target audience. First of all, there are self-hosted versions of this such as Facebook’s FAISS (has GPU acceleration), Spotify’s ANNOY, Elastic search KNN search and Milvus. We plan to optimize performance using the latest ANN algorithms, caching, being as close to the wire (Non-REST) transmission of vectors, dimensionally reduction e.t.c. At the end of the day, our stack will probably not be as fast as a self-hosted version but it’ll confer other advantages that we hope ML engineers will find useful similarly to AWS RDS or DynamoDB.