Alternatives
Products that do what VectorAdmin – An open-source vector database management system does
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…
- 1
- 2
- 3
- 4
- 5
- 6
- 7NN
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
- 8
- 9
- 10
- 11
- 12IB
Disclaimer it is a heavily AI assisted project. The goal was not to be the most performative but the kind that's easier to learn from. I wanted to share this in case there are people who had the same idea or wanted to see something like this.
Jun 2026 · github.com
- 13SD
Hey Hacker News! Last week we made the codebase for product 100% open source. This week we shipped a dashboard to manage connectors, as well as integrations with Google Drive, Zendesk, Notion, and Confluence. This means Sidekick is now the fastest way to sync data from these tools to a vector database. Why is this important? For developers building LLM apps, data integrations are often the least interesting and most time consuming part of the process. For those that don’t want to roll their own ETL, Sidekick is an opinionated tool that lets them get an API endpoint to run semantic searches…
2023 · app.getsidekick.ai
- 14LS
MyScale is designed for the storage and analysis of massive vector data with structured metadata. If you are eager to find a high-performance vector search using SQL queries, MyScale could be your preferred option. Thanks to the advantages of native structural database support, it provides you with a flexible filter with a WHERE clause, even JOIN when you want to jointly search vectors with filters on relevant metadata from other tables. MyScale is now open for registration and offers millions of vectors‘ free tier plan for you! (https://myscale.com/) Now you can also use…
2023 · myscale.com
- 15IW
Oct 2025 · github.com
- 16WA
Today you can easily adopt AI coding tools because you have git for branching and rolling back if AI writes bad code. We haven't seen this same capability for data and decided to build it ourselves. Nile is a new kind of data lake, purpose built for using with AI. It can act as your data engineer or data analyst creating new tables and rolling back bad changes in seconds. We support real versions for data, schema, and ETL. We'd love your feedback on any part of what we are building - https://getnile.ai/ What do you think?
Jan 2026
- 17SQ
Dec 2025 · github.com
- 18FA
Apr 2026 · brilliant.design
- 19ND
This is an AI generated TED talk from a system we built at the TED AI hackathon this weekend. It's built on top of ElevenLabs, SDXL and Wordware (https://wordware.ai/). We also have a custom index of over 2 million arXiv papers and 6 million Wikipedia articles. All open source: https://github.com/ashvardanian/extrapolaTED
2023 · youtube.com
- 20SV
Hi HN, I'm Daniel from Superlinked! We have built an open-source framework that improves vector search relevance and usefulness by combining structured metadata with unstructured data in your embeddings. We included self-hostable API server that sits between your data sources and vector database. Docs: https://docs.superlinked.com/ We're launching our cloud offering soon where you can use Superlinked to orchestrate high-performance retrieval for RAG, Search & Recommendation apps in your own cloud. Looking for feedback and happy to answer questions!
2024 · github.com
- 21WM
Hey HN — We're excited to share Trellis — a snowflake for unstructured data. We've built an AI engine that turns unstructured data into structured SQL-format based on the schema you define in natural language. We spent a lot of time building ML infrastructure and realized that most data warehouses and data pipelines are not designed for unstructured data (documents, PDFs, calls). While something like a Vector database and RAG are great at search tasks, they really struggle with aggregation and SQL type queries such as 1. How many emails in the past 6 months contain complaints about the…
2024 · demo.runtrellis.com
- 22IW
Input a SMILES string (or pick one molecule from the examples) and it returns up to 100k molecules closest in 3-D shape or electrostatic similarity – from 10+ billion scale databases — typically in under 5-10 s. *Why it might interest HN* * Entire index lives on disk — no GPU at query-time, less than ~10 GB RAM total. * Built from scratch (no FAISS index / Milvus / Pinecone). * Index-build cost: one Nvidia T4 (~ 300USD) for one 5.5B database. * Open to anyone, predict ADMET, export results as CSV/SDF. Full write-up & benchmarks (DUD-E, LIT-PCBA, SVS) in the pre-print:…
2025 · cheese-new.deepmedchem.com
- 23ZP
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
- 24MA
Hey HN, Matusa here! A friend and I have built Memora. Memora is a vector database with built-in multistage reranking, which can significantly improve search accuracy over semantic search. It also features a proprietary embedding model tailored for RAG use cases — where there's a structural mismatch between the content stored and the query used for searching (hence why HyDE works well). Memora started because we were working on a stealth AI startup where we used an agent that would query into a vector DB, but it would take multiple tries for the agent to find what it needed (20% of the time…
2023 · usememora.app
Ranked by how close each launch is in meaning, then by votes. Refine with a description →