Alternatives
Products that do what OptiSchema Slim does
Local-first AI PostgreSQL tuning with ‘what-if’ indexes
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- 4SD
SnapQL is an open-source desktop app (built with Electron) that lets you query your Postgres database using natural language. It’s schema-aware, so you don’t need to copy-paste your schema or write complex SQL by hand. Everything runs locally — your OpenAI API key, your data, and your queries — so it's secure and private. Just connect your DB, describe what you want, and SnapQL writes and runs the SQL for you.
2025 · github.com
- 5PI
This is a Postgres extension that can determine if a query should have an index. For example, for this table: create table book( id int primary key, title text not null ); You can run `index_advisor()` to see if there should be an index on a select statement: select * from index_advisor('select book.id from book where title = $1'); And it will return (summarized): {"CREATE INDEX ON public.book USING btree (title)"} It works particularly well with pg_stat_statements[0] which tracks execution statistics of all SQL statements executed on your Postgres database. It leans heavily on HypoPG[1], an…
2024 · github.com
- 6PE
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too. The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our…
Mar 2026 · github.com
- 7PT
PgQueuer is a minimalist, high-performance job queue library for Python, leveraging the robustness of PostgreSQL. Designed for simplicity and efficiency, PgQueuer uses PostgreSQL's LISTEN/NOTIFY to manage job queues effortlessly.
2024 · github.com
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2018 · github.com
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Hi HN! We're Evgeny and Arjun, and we’re building a better way to do analytics with Postgres. We love Postgres for its simplicity, power, and rich ecosystem. But engineers have to still get bogged down with heavyweight and expensive OLAP systems when connecting an analytics data stack. Postgres is amazing at OLTP queries, but not for OLAP queries (large data scans and aggregations). Even in this case, we’ve still heard from countless scaling startups that they still try to use only a read replica to run analytics workloads since they don’t want to deal with the data engineering complexity of…
2024 · github.com
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2019 · pugsql.org
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Hey HN, Postgres Pro is an open source Model Context Protocol (MCP) server built to support every stage of the development process. It does a lot more than help generate and run SQL—it helps you make sure your application and database actually run well. Features: - Industrial-strength tuning algorithms similar to those in MS SQL Server - “What if?” tool to evaluate LLM-generated index suggestions - Works with Cursor, etc., to make changes in ORM code - Ensures safe SQL execution with restricted mode - Includes health checks to stay ahead of production problems
2025 · github.com
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Hi HN. This is a project I've been building for the last month. It's a macOS postgres client that uses your OpenAI key to generate and immediately execute queries against your database locally on your machine. I imagine the user being someone familiar with SQL and able to identify errors in the generated code, but regularly needs to write queries to answer relatively simple questions. This is my first software project I'm intending on selling myself and would love feedback on the product and landing page. I'd also love ideas on how to market it to users, as I'm very new to this. In the near…
2023 · pgmagic.app
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Hey HN! We’re Emma and Chris, founders of Velvet (https://www.usevelvet.com). Velvet proxies OpenAI calls and stores the requests and responses in your PostgreSQL database. That way, you can analyze logs with SQL (instead of a clunky UI). You can also set headers to add caching and metadata (for analysis). Backstory: We started by building some more general AI data tools (like a text-to-SQL editor). We were frustrated by the lack of basic LLM infrastructure, so ended up pivoting to focus on the tooling we wanted. So many existing apps, like Helicone, were hard to use as power…
2024 · usevelvet.com
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Hi HN, I’m Tullie, founder of Shaped. Previously, I was a researcher at Meta AI, worked on ranking for Instagram Reels, and was a contributor to PyTorch Lightning. We built ShapedQL because we noticed that while retrieval (finding 1,000 items) has been commoditized by vector DBs, ranking (finding the best 10 items) is still an infrastructure problem. To build a decent for you feed or a RAG system with long-term memory, you usually have to put together a vector DB (Pinecone/Milvus), a feature store (Redis), an inference service, and thousands of lines of Python to handle business logic…
Jan 2026 · playground.shaped.ai
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Hey! I'm Andrei. I got frustrated by how people tend to build overcomplicated backend systems, being "motivated" by big tech case studies and popular books. So, I started exploring lean architecture, and building my digital garden of ideas, approaches and data that align with this direction. Here I want to present one of the tools – Sizing tool for PostgreSQL. I've benchmarked PostgreSQL on different EC2 instances and disks, with different initial data sets to see performance that these instances can give you. And I've built a tool to visualize this data, which I welcome you to explore. So,…
Jul 2026 · postgres.saneengineer.com
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Mar 2026 · github.com
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