AI is a great, cheap foundation we can all agree on that (depending on token usage, of course).

When you re building your first product, and you re not sure if it will work and earn money, the obvious goal is to keep costs as low as possible and avoid burning money early on.

Most devs I talk to are quietly overpaying AWS or GCP. Not by a little but by a lot.

We've been building Huddle01 Cloud for a while now and honestly, the pricing difference is wild. Same bare-metal performance, global edge infrastructure with sub-100ms latency, no egress fees, no hidden markups.

What's everyone paying for cloud compute right now? Curious if others have found good alternatives.

Hey everyone!

I love the concept of "vibecoding", getting into the flow, using AI to build fast, and focusing purely on the product. But for a lot of developers (especially in emerging markets like Indonesia), that vibe instantly dies when it's time to deploy.

Suddenly, you are dealing with complex server setups, fighting DevOps configurations, or getting blocked because you don't have an international credit card to spin up a basic server.

Deployment shouldn't ruin the vibe. It should be just as seamless as writing the code.

Every developer I talk to has the same story. The idea is clear, the stack is decided, but before anything real gets built there's 3 hours of AWS configs, Dockerfiles, environment variables, and CI pipelines that have nothing to do with the actual product.

AI coding tools made writing code faster. Nobody has solved what comes after.

Now, since some do these things, while others charge every bit as much without these features, I already expect that they have:

• Built-in Github commit

• Credit rollovers (e.g. if I do not use all credits in a paid plan, they are added to the next month - indefinitely)

• Nothing that tries to keep my project within their ecosystem and then expects that as my business scales, I pay them more.

As Lovable, Bolt, v0, Base44, Bubble, Make, etc. jostle to out-do each other and be the one that we pay for, I think we will soon see:

• Back-end solutions that guide non-technical creators through the steps to ship a SaaS product that is actually ready to scale to take on real traffic

• Pre-emptive best-price/best-solution external solution-shopping, such as for white-listed bulk emailing and available domain search.

I just started solo dev last year and have been trying to keep all early prototype projects low budget. I personally build in a serverless way on @AWS and pay only for what I use (<$1 per month). I'm curious what do you all use for low-budget development? Maybe I can find even cheaper build stack from you guys lol :)

I'm building Clarity Cloud AI, an AI Native analytics platform for technical and non-technical folks alike. As a first time builder, the journey has been challenging yet fun. I'm planning to launch soon!

I have made a list of platforms where you can create your community and share your knowledge in a closed circle (and in some cases sell memberships).

The list is according to my preferences, so the platforms I use the most are at the top.

I ve been working on some AI projects recently things like scheduled agents, API responders, and multi-agent systems that need to run continuously. One of the biggest headaches I ve run into is deployment.

Most cloud platforms (AWS, GCP, etc.) are built for stateless apps or short-lived functions. But for long-running, stateful agents, the kind that need to persist data, auto-recover from crashes, and expose custom endpoints it gets surprisingly messy. I ve spent so much time setting up VMs, Docker configs, and recovery logic than actually writing agent behavior logic.

Thanks to supabase I was able to get my project up and running in record time. I use oauth, edge functions, storage, database ofcourse, queues:pgmq and pgvector.

Please check out the project at https://github.com/vpuna/vpuna-a...

It's a semantic search platform for structured and unstructured data , with MCP support and more

Curious what you re actually shipping with right now. Which stack are you using day-to-day, and why did you choose it over the alternatives? A bit of context (product type + team size) helps a ton.

If you ve switched stacks recently, what did you move from/to and what pushed the change? Cost, speed, hiring, DX, vendor limits, something else?