Alternatives
Products that do what RelytONE does
All in One Postgres.
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We've been hard at work for a few weeks and thought it's time for another update. In case you missed our first post, PostgresML is an end-to-end machine learning solution, running alongside your favorite database. This time we have more of a suite offering: project management, visibility into the datasets and the deployment pipeline decision making. Let us know what you think! Demo link is on the page, and also here: https://demo.postgresml.org
2022 · postgresml.org
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2024 · github.com
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2015 · github.com
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2014 · databaselabs.io
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2018 · github.com
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Hey HN! Lev here, author of PgDog (https://github.com/pgdogdev/pgdog). I’m scaling our favorite database, PostgreSQL. PgDog is a new open source proxy, written in Rust, with first-class support for sharding — without changes to your app or needing database extensions. Here’s a walkthrough of how it works: https://www.youtube.com/watch?v=y6sebczWZ-c Running Postgres at scale is hard. Eventually, one primary isn’t enough at which point you need to split it up. Since there is currently no good tooling out there to do this, teams end up breaking their apps…
2025 · github.com
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2021 · stackgres.io
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2024 · github.com
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hi hn, hydra ceo here hydra is an open source, column-oriented postgres. you can set up remarkably fast aggregates on your project in minutes to query billions of rows instantly. postgres is great, but aggregates can take minutes to hours to return results on large data sets. long-running analytical queries hog database resources and degrade performance. use hydra to run much faster analytics on postgres without making code changes. data is automatically loaded into columnar format and compressed. connect to hydra with your preferred postgres client (psql, dbeaver, etc). following 4 months…
2023 · hydra-so.notion.site
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2022 · github.com
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2024 · github.com
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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
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Hey HN, I built pg-mcp, a Model Context Protocol (MCP) server for PostgreSQL that provides structured schema inspection and query execution for LLMs and agents. It's multi-tenant and runs over HTTP/SSE (not stdio) Features - Supports multiple database connections from multiple agents - Schema Introspection: Returns table structures, types, indexes and constraints; enriched with descriptions from pg_catalog. (for well documented databases) - Read-Only Queries: Controlled execution of queries via MCP. - EXPLAIN Tool: Helps smart agents optimize queries before execution. - Extension…
2025 · github.com
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2019 · github.com
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Hi HN! I’m Eoin, founder of Sourcetable (https://sourcetable.com). Sourcetable is an AI-native spreadsheet that syncs with all your data. Users pair with an AI copilot that helps them do their spreadsheet work, as well as more database-centric analysis and SQL. Soucetable syncs with databases including Postgres, MySQL, and MongoDB, and over 100+ business applications including Stripe, Zendesk, Hubspot, Quickbooks and Google Analytics. That data is available in a spreadsheet, and any models you build automatically update in near-real-time as new data flows in. The core primitives…
2024
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2016 · bedquiltdb.github.io
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