AI · alternatives · 2026
24 alternatives to Tidepool – analytics for large text datasets
Hello HN! I'm Peter, one of the folks who helped create Tidepool. We last shared Tidepool with HN about 7 months ago https://news.ycombinator.com/item?id=36957762 Since then, the AI field has moved incredibly quickly and we’ve iterated a lot on our product! The core problem we are trying to solve is: there's a lot of useful business insights you can get from text data, but it's hard to do analytics on it. - SQL is built for tabular / structured data, but when it comes to text, the best you can do is do keyword search. - In the pre-LLM world, you might resort to training a…
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier. Tidepool – analytics for large text datasets launched in 2024; newer entries below may have overtaken it.
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Product analytics for AI applications
2023 · tidepool.so · its alternatives →
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Pylar▲502Hey HN! We're Hoshang & Vishal, the team behind Pylar - a governed access layer between databases and LLMs. We previously led data and AI and we kept seeing the same problem across teams using LLMs internally: agents are great with unstructured data, but the moment you want them touching your actual systems of record — Snowflake, Postgres, CRMs, product DBs — everything becomes fragile, risky, or outright unsafe. Two issues show up every single time: 1. Agents over-querying They don’t understand cost. They’ll happily generate queries that blow up your warehouse bill. 2. Accidental data…
Dec 2025 · pylar.ai · its alternatives →
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The text-based SQL learning game for data scientists and marketers
2023 · lost-at-sql.therobinlord.com · its alternatives →
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I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
2024 · embedding.io · its alternatives →
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Two weeks ago I was on my babymoon in Corfu, Greece. While in transit, I was overseeing a GSoC intern submit an important feature to my array database library, Xarray-SQL. He added `to_dataset()`, which completed the roundtrip between thinking of array data in a tabular model simultaneously as gridded rasters (the premise of the project is that every Nd array can be mapped to 2d, where orthogonal dims of the Nd array are just primary keys of a tabular representation). We discussed in chat, now that this feature existed, what demos could we make that would prove this data model works? With…
Jul 2026 · github.com · its alternatives →
- 7SA
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 · its alternatives →
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Ask questions about your data in plain english
2023 · movinglake.com · its alternatives →
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Hey HN! I got nerd-sniped by Bloom Filters this weekend, specifically for searching datasets with high "cardinality" (number of unique items). They're an _amazing_ data structure that, at a fixed size, tracks potential set membership. That means unlike normal b-tree indexes, they don't grow with the number of unique items in the dataset. This makes them great for "needle in a haystack" search (logs, document) as implementations like VictoriaMetrics and Bing's BitFunnel show. I've used them in the past, but they've never been center-stage in my projects. I wanted high cardinality keyword…
2025 · github.com · its alternatives →
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Hi HN, We're Luke and Phillip, and we're building Spice.ai OSS - a lightweight, portable runtime, built in Rust and powered by Apache DataFusion to locally materialize, accelerate, and query data tables sourced from any database, data warehouse or data lake. Phillip and I first introduced Spice on Show HN in September 2021. Since then, we’ve been schooled and humbled in every way building 100TB+ data and ML systems for the https://spice.ai cloud platform. Along with our customers, we struggled with getting fast, low-latency, high-concurrency SQL query within a budget, accessing and…
2024 · github.com · its alternatives →
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Bitscale▲96For last few months, we made pivots from Text-to-SQL to Legal AI to AI employees to finally this - We have finally been working on an interesting product with a few customers. Introducing BitScale - A new UI for AI built for growth teams. BitScale helps sales and marketing teams- 1) Reach out to 1000s of prospects with hyper personalised messages using tools like LinkedIn Research, Web research and of course ChatGPT 2) Find leads from communities, google maps and 10+ data sources, find their emails and phone numbers 3) Write 1000s of SEO optimised and Google researched blogs which are…
Mar 2026 · bitscale.ai · its alternatives →
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I've been exploring the (not so=) amazing potential of AI in coding and have compiled a list of tools. From AI-powered IDEs to code generators, this resource is my contribution to the community. I'm still on the fence about including txt2sql projects, as their functionality seems too basic to me. And I'm personally maintaining this, so your feedback is wellcome.
2025 · aicode.danvoronov.com · its alternatives →
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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 · its alternatives →
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It's not what you say, it's how you say it
2016 · soundcloud.com · its alternatives →
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Excited to share a project I’ve been building for months! Would love to receive honest feedback :) My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong? The product allows you to connect any LLM to any data source with centralized context…
2025 · github.com · its alternatives →
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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 · its alternatives →
- 17RS
We’ve been working on a search problem that requires querying both text and numbers simultaneously. For example, in a dataset of clothing items with descriptions and prices, a search for “slim pants for $20” should prioritize skinny jeans for $25 over slim pants for $50 because they are semantically similar and the price is closer. I’ve found that standard embedding models struggle with numerical ordering, while text-to-SQL methods rely on exact matches and often filter out too many results. To solve this, we built a system designed specifically for structured datasets like CSVs or tables.…
2025 · demo.tryvoker.com · its alternatives →
- 18UN
We've been working hard for the last year on building HeavyIQ, an LLM-powered plugin to the GPU-accelerated HeavyDB database, that allows users to ask natural language questions and get SQL, visualizations, and natural language answers back. The LLM itself is fine-tuned on tens of thousands of question and answer pairs, with a major focus around building the model’s proficiency at performing spatial and temporal joins. You can try it for yourself on this live demo of 400M tweets and a handful of other datasets of interest:…
2024 · demo-heavyiq.heavy.ai · its alternatives →
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An easy way to get all your interaction data in BigQuery
2017 · keen.io · its alternatives →
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A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL - eosphoros-ai/DB-GPT-Hub
2023 · github.com · its alternatives →
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Read expert whitepapers on BI, data analytics, and AI. Gain valuable insights and strategies for leveraging data to drive business success.
2015 · periscope.io · its alternatives →
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We built a structured search engine for Twitter called Bird SQL, available at https://www.perplexity.ai/sql. Our search interface uses OpenAI Codex to translate natural language to SQL. Our backend then verifies the SQL, executes it, and displays the results on the web app. This makes large structured datasets like a scrape of Twitter easy for anyone to explore. As background, while working on text-to-SQL as a general problem, we came to believe one of its most powerful applications is as a search tool because: - SQL is hard to write by hand and prone to errors - It allows you…
2022 · perplexity.ai · its alternatives →
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Largemem▲10We’re building Largemem, (https://largemem.com) a shared knowledge base where groups upload and maintain a common set of documents (PDFs, scans, audio) and query them conversationally. Each group has its own persistent knowledge base. We parse content into chunks, extract entities, and combine vector search with a lightweight knowledge graph, so answers can synthesize information across multiple documents and across the group’s shared context - not just retrieve isolated snippets. We would love to hear your feedback!
Nov 2025 · largemem.com · its alternatives →
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I got tired of the overhead required to run even a simple data analysis - cloud setup, ETL pipelines, orchestration, cost monitoring - so I built a fully local data-stack/IDE where I can write SQL/Py, run it, see results, and iterate quickly and interactively. You get data lake like catalog, zero-ETL, lineage, versioning, and analytics running entirely on your machine. You can import from a database, webpage, CSV, etc. and query in natural language or do your own work in SQL/Pyspark. Connect to local models like Gemma or cloud LLMs like Claude for querying and analysis. You…
Apr 2026 · stream-sock-3f5.notion.site · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →