Alternatives
Products that do what Upstash Vector does
Serverless vector database for AI and LLMs
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2014 · github.com
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Hey HN, At Mintplex Labs are building developer tools for AI applications. One area we encountered frustration was the use of Vector Databases like Pinecone, Chroma, QDrant, or Weaviate to "unlock" long-term memory and contextual answers. It is nearly impossible to manage this data when in use for production. The craziest thing was how you cannot atomically CRUD any vectors in most of these vector databases. Let alone easily copy, clone, or migrate data or entire indexes without paying for re-embedding - among other things. With VectorAdmin you get a database level UI with the ability to…
2023 · vectoradmin.com
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2013 · insightdatascience.com
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Yo. OtterTune is a database optimization service. It uses machine learning to automatically tune your MySQL and Postgres configuration (i.e., RDS parameter groups) to improve performance and reduce costs. It does this by only looking at your database's runtime metrics (e.g., INNODB_METRICS, pg_stat_database, CloudWatch). We don't need to examine sensitive queries or user tables. We spun this project out of my research group at Carnegie Mellon University in 2020. This week we've announced that OtterTune is now available to the public. We are offering everyone a starter account to try it out…
2021
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2020 · github.com
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Hey HN - we launched Zep's document vector DB today. Zep is an open source memory store for LLM apps, and this builds on existing chat history memory persistence, embedding, and enrichment capabilities. Zep uses Postgres and pgvector for database operations and vector search. Vector search can be complicated on Postgres, with careful configuration required at both index creation and query time. We've focused on significantly improving this developer experience. Zep automatically selects index and query parameters for developers based on best practices and known heuristics. Vector database…
2023 · github.com
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Hi HN. Peter here. As a machine learning engineer, I mostly think in terms of feature vectors, embeddings, and matrices. One of the most useful byproducts of deep neural networks is embeddings because they allow us to represent high-dimensional data in terms of lower-dimensional latent vectors. These feature vectors can be used for downstream applications like similarly search, recommendation systems and near duplicate detection. As an ML engineer, I was frustrated by the lack of a datastore in which vectors are first-class citizens. As a result, most ML engineers, including myself, end up…
2021
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2024 · github.com
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