Sc

SchemafreeSQL – Data, Fluid as Code

Hacker News

SchemafreeSQL – Data, Fluid as Code

Hi HN, I'm Dean, the non-technical co-founder of SchemafreeSQL. We released our beta version about a year ago. You can see the HN Post here https://news.ycombinator.com/item?id=30291592 Today I am pleased to announce our initial release of our hosted SFSQL offering. A major concern from the HN Beta feedback we received was our longevity. Being a hosted database solution I can see why. We took that to heart and re-engineered our offering. We de-risked it by minimizing the amount of infrastructure under our management, fly.io manages customer's dedicated SFSQL endpoints, Aiven.io manages customer's dedicated databases across 5 clouds, our serverless offering is a managed AWS Aurora Serverless cluster, our in-house databases is managed by Planetscale.com, and Stripe handles subscriptions. The cost of these services are mostly on demand, bringing our monthly fixed cost to a very manageable level. I highly recommend all these services. We are boot strapping SFSQL for now. Our business model, how we make money, is simple. Our prices are higher than our costs. Just like all businesses, margins matter and because we incur the costs of these service and pass them on, our margins take a hit. We envision being more of an add-on to these service providers and others like them eventually. Our margins would increase and the total cost to our customers would decrease. In this model our pricing is purely value based. "Data, Fluid as Code" is what we settled on after countless iterations. I believe it captures the "why" question, "why did we build this". SFSQL originally started out as an Object store for a online dev. environment we built in 2000. It has evolved over time. Its schemaless properties where added as we had a need to better handle user provided data structures and refactoring associated with many of our client projects. Eric, the technical co-founder and creator of SFSQL, answered the "How" question in our HN beta post. For more in depth info on what's going on behind the scenes Eric is available via email support@schemafreesql.com, he is not available to respond here. The demo apps were all built by me. Eric would like it known that he is not responsible for those codes bases, which are all available on GitHub. I built these apps while testing out SFSQL and seeing if we play nice with the various serverless platforms. Client solutions we have built with SFSQL are not available for public display so we went with these demo apps I built. The apps show how easy it is to hook up a back-end to a web app with SFSQL even by a non programmer like myself. I hope you check out SFSQL. Try it for free, no sign-up required, and please leave us feedback https://schemafreesql.com/givefeedback_HN.html

Share card

Actual performance

42points
19comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
83%83% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, user · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month, monthly · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: margin, subscription, margins · Missing: arr, mrr, revenue
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: make money · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

DataMorf
DataMorf34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

No-code data transformer with enrichment

Indie Hackerscommitment-side-project
Zipasaurus
Zipasaurus54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Postal Code Data Platform

Indie Hackers1apis
No
No-code data analysis64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

No-code data analysis

Hacker News1
We
We added an Oink data importer for Cheers37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

We added an Oink data importer for Cheers

Hacker News9
Te
Techcrunch data 2005-201250%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Techcrunch data 2005-2012

Hacker News2
Ma
Mambocollector – Statsd Data collector for MySQL44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Mambocollector – Statsd Data collector for MySQL

Hacker News2
Mu
Munge your data with TXR50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Munge your data with TXR

Hacker News2
Ag
AgriCatch – Data aggregation on Django43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AgriCatch – Data aggregation on Django

Hacker News4
In
Incorporating Religion Denominational Data into a US Births/Deaths Viz50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Incorporating Religion Denominational Data into a US Births/Deaths Viz

Hacker News2
Re
Rennet – Coagulating all the data50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Rennet – Coagulating all the data

Hacker News11