I built a serverless data API builder – no storage, low latency
Hey HN, I'm Bo, cofounder of Fleak.ai. Over the past several months, our team has been hard at work developing Fleak, a data API backend builder, and we would love your feedback on what we've built so far. What Fleak Does: Fleak simplifies the process of building and deploying API backends. It features a no-code IDE UI that lets you create workflows by chaining together steps such as native SQL transformations, calling LLM models, AWS Lambda functions, and more. With a single click, you can deploy these workflows to a production endpoint (during test, we are able to handle 5000 QPS without…
In plain words
Fleak.ai is a serverless data API backend builder that uses a no-code IDE to let users create workflows by chaining SQL transformations, LLM models, AWS Lambda functions, and other steps. Users can deploy these workflows to production endpoints with a single click, handling high throughput without requiring storage infrastructure or cluster configuration. It is designed for developers who want to build and deploy API backends quickly without managing underlying infrastructure.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN, I'm Bo, cofounder of Fleak.ai. Over the past several months, our team has been hard at work developing Fleak, a data API backend builder, and we would love your feedback on what we've built so far. What Fleak Does: Fleak simplifies the process of building and deploying API backends. It features a no-code IDE UI that lets you create workflows by chaining together steps such as native SQL transformations, calling LLM models, AWS Lambda functions, and more. With a single click, you can deploy these workflows to a production endpoint (during test, we are able to handle 5000 QPS without LLM node). Key Features: Native SQL Transformation: Work with SQL directly, without the need for a storage layer. Simplified Setup: No more cluster configuration and script juggling. Efficient Deployment: Streamlines the deployment cycle and reduces the need for constant monitoring. The inspiration for Fleak came from my frustration with maintaining production data pipelines, especially with the added complexity brought by LLMs. Fleak aims to simplify life for data practitioners. Known restrictions: 1. not all SQL syntax is supported, we are adding daily 2. free version has rate limit due to cost 3. LLM node latency is not optimized due to cost, but our base rate limit is higher than out of box models and can be elastic. Our codebase is meticulously maintained, though it's not open source yet as we're still considering which parts to release. You can check out Fleak at www.fleak.ai. We are hosting a small product feedback lunch on Stanford campus this thursday 8/8 1:30pm-2:30pm. Come join us! https://lu.ma/0beq21pd We'd love to hear your thoughts: Does this sound useful to you? What features would you like to see? Any advice on open-sourcing? Thanks!
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