r/SaaS

I has multiple apps on Vercel and a month ago I received the following email for each of my 10 projects

https://preview.redd.it/aqn08q4i172h1.png?width=1722&format=png&auto=webp&s=5a378323b6ca5bb3c31ccd650f6fdccf184ae578

All my env variables, secrets, configs gone. No one in the support replied to my multiple emails. Got the following general email after 2 weeks

https://preview.redd.it/t3nwiibr172h1.png?width=3874&format=png&auto=webp&s=85f2f07f9de31a5c049c54f7a052893d44e6368a

For a SaaS that's a make or a break situation. I migrated everything to railway, spending days to get everything deployed and then today

https://preview.redd.it/g3crog8x172h1.png?width=1652&format=png&auto=webp&s=78e2de3923349338b6565d67ede69448ec77a5aa

Whole railway infra is inaccessible including their home page.

Is it time to normalize using AWS and GCP directly? I believe that is more painful initially but much more dependable for the long run.

What do you guys think?

Hey everyone this is my first time posting so please bear with me. This is not a self-promotion rather needed some advice.

I’m working with a pre-seed startup. We’re building a governance layer for fintech companies deploying AI models and agents in regulated workflows.

The product combines:

- A runtime governance layer that sits around AI models and agents, checking inputs, outputs, tool use, and actions against policy/risk criteria.
- A lightweight receipt layer that creates audit records for important AI decisions, escalations, and workflow events.
- A lifecycle governance layer that connects those records across training, evaluation, deployment, and runtime operations.

The idea is to make AI workflows auditable by default. For example, if an AI agent is involved in lending, credit risk, fraud review, AML/KYC, servicing, collections, or customer support, it should be possible to answer:

- What did the model/agent do?

- What data or context did it use?

- What policy was applied?

- Was the action low-risk, high-risk, or escalation-worthy?

- Was human review required?

- Can this be shown later to an internal compliance team, external auditor, or regulator?

We’re trying to create tamper-evident governance records across the lifecycle of AI systems, not just post-hoc documentation. Our current wedge is fintech, especially AI-based lending platforms, AI-native financial tools, and mid-sized regulated companies adopting AI.

The challenge: I’ve been doing LinkedIn cold outreach to potential fintech design partners, but haven’t heard back much yet. So I’m trying to figure out whether the problem is the idea, the positioning, the buyer, or the outreach channel.

Would love honest feedback:

1. Is this a real pain point for fintech teams right now?

2. Who is the right buyer/persona: CTO, compliance, risk, model governance, product, or audit?

3. Is “AI audit receipts” a compelling wedge, or does it sound too abstract?

4. Which use case sounds most urgent: lending, AML/fraud, collections, or customer support?

5. How would you recommend finding early design partners for something like this?

We’re early/pre-seed, so brutal feedback is welcome. I’m trying to understand whether this is a real wedge and how to reach the right people.

Been sitting with this observation for a while and i think it's finally clear enough to articulate.

The world doesn't pay for intelligence. It doesn't pay for potential, for good ideas sitting in a notes app, for the ability to analyze a problem from 12 different angles. It pays for results, leverage and distribution. And those things are often built by people with completely different strengths than the ones we associate with being smart.

A few patterns i keep seeing, and honestly recognizing in myself at different points:

Analysis feels safe. Execution feels risky. Analytical people live in their heads because it genuinely feels productive. You can always go one level deeper, refine one more time, find one more flaw in the plan. But real progress requires being okay with embarrassing first attempts and being wrong in public. Most analytical people would rather be right in private than wrong out loud.

Perfectionism is just fear with a better vocabulary. "I'm not ready yet" and "my standards are high" are sometimes true. But a lot of the time it's just that shipping feels exposed in a way that planning never does. Certainty doesn't come before action, it comes after it, and waiting for it is a trap.

The market rewards output not intelligence. You don't get paid for understanding the problem deeply. You get paid for reaching people, solving something real, building something that compounds. None of that happens in your head.

Smart doesn't mean effective. IQ helps you understand things faster. But effectiveness comes from focus, consistency, the ability to sell, tolerance for chaos and the capacity to keep moving when things are unclear. those aren't really taught anywhere, and they matter more to income than raw intelligence ever will.

The hardest thing to accept, and i've been guilty of this more than i'd like to admit, is that a lot of what feels like deep work is just discomfort avoidance with a productivity label slapped on it.

Curious if others have felt this tension, and what actually helped you shift from analysis mode to just moving.

I have a MVP, I am reaching out to people to get them to review the product, use it and suggest the features they'd like to see in the future iterations. Overall I want it to be absolutely aligned with what the market demands but I don't seem to get any traction.

Advices I get from most of the places seem so generic.

Hello everyone,

Wanted to share a super cool project (IMO) we have been working on. It’s a zero-dependency React data grid, called LyteNyte Grid. Check it out, and hopefully, you will find it useful and save yourself a ton of time.

Some of the reasons to use LyteNyte Grid.

• Crazy Performance: LyteNyte Grid is super light at only 40kb (gzipped) and is extremely fast. It can handle millions of rows and 10,000+ updates/sec. Based on our internal benchmarks, it is one of the fastest grids available on the market.

• Feature-rich: Brings 150+ features, most of which are free and open source. Features such as cell range selection, row master-detail, and row grouping are included for free with LyteNyte Grid. This is something we are quite proud of. There are paid libraries (I won't name them) that offer less.

• No Styling Tradeoffs: With LyteNyte Grid, you can choose whether to go headless or styled. There is basically no tradeoff when considering styling choices.

• Full Prop Driven: You can configure it declaratively from your state, whether it’s URL params, server state, Redux, or whatever else you can imagine, meaning zero sync headaches.

• Unique DX Experience: Our grid is built in React for React and has a clean declarative API, which eliminates awkward configuration workarounds.

We also recently dropped LyteNyte Grid AI Skills. This is a really nice feature if you’re using AI coding agents. It lets you describe an advanced data grid solution, and your AI agent codes it for you. We have been testing this with increasingly complex grid instances, and the results have been awesome.

All our code is publicly available on GitHub. Happy to answer any questions you may have.

If you find this helpful and like what we’re building, GitHub stars help. Feature suggestions and code contributions are always welcome.

• GitHub
• Live Demo

I'm building a tool that tells SaaS founders exactly why their customers churn using AI. Looking for 3 founders to try it free in exchange for feedback. DM me.

I run a B2B AI company with my co-founder. I'm CEO and run product, she does customer validation and ops. We sell to mid-market CPG brands. We automate two finance workflows that are massive pain points and that nobody has bothered to fix properly because this customer type is well just different.

7 paying customers. Just over $10K MRR. Zero churn. We're 7 months in and raising a seed right now.

Wanted to share some things I got wrong, partly because I see a lot of vertical AI founders making the same mistakes.

The first thing is that for the first 2 months I led every demo with "we use AI agents to..." and lost most of those deals. CPG controllers don't care that you use AI. They care that you'll catch their distributor stealing $20K a month from them. Once I changed the opening line to "we recover deductions, here's how much we got back for X last quarter," conversion went up a lot. The AI part is implementation detail and not the value prop we thought it was going into it.

Second thing. I tried selling to enterprise first because the contract sizes looked good on paper. 9-month sales cycles, every deal stalled at procurement, completely brutal. Mid-market ($20M to $200M in revenue) closes in 3 to 6 weeks because the buyer is the same person as the user. That's the controller or the CFO directly. No procurement, no legal review that takes 4 months. If you're early stage and building vertical AI, do not start at enterprise. The temptation is real and it's wrong.

Third thing, and this is the one I really got wrong. I hired a dev shop to build a part of the product at like $5K MRR. One of the worst decisions we made. And instead decided to build the workflows ourselves using AI tools.

A few other things, faster:

• Cold outbound is soooo DEAD in this space. Reply rate was less than 1%. We sent THOUSANDS of emails when we first started. Introductions from brokers and just full on founder led sales build a referral network through conferences was the move.
• Outcomes beats pure software here. People in CPG finance don't want another dashboard. They want someone to handle the workflow for them. Our pricing reflects that and so does retention.
• The "we have AI" wedge is closing. 12 months ago it was a differentiator. Today every competitor and their moms in the category leads with it. Depth of workflow coverage is the new wedge.

Happy to dig into any of this. Genuinely curious what other vertical AI founders are seeing, especially anyone selling into industries that don't read TechCrunch.

when we launched, I had a theory. write useful content on Reddit, get leads. post on LinkedIn, get leads. do outreach, get leads. classic B2B playbook.

two months later I actually looked at where our paying customers came from. the data destroyed most of what I assumed.

the numbers

73% of paying users had touched one of our free tools before signing up. not the landing page. not a Reddit post. not an outreach DM. a free tool.

we have 8 of them on our homepage. no signup, no email gate, no card. you land on the page, use the tool, get real data back in about 3 seconds. that's it.

the other 27% came from various things: Reddit comments, LinkedIn posts, word of mouth, the TV appearance we somehow ended up getting. all combined, less than a third.

I was not expecting this. I had spent way more time on content and outreach than on the free tools. the ROI was completely inverted.

why free tools convert better than content

I've been trying to understand this for a few weeks now and I think it comes down to one thing: demonstration vs. description.

a blog post or a Reddit post describes what your product does. it asks the reader to trust you before they've experienced anything.

a free tool demonstrates it. the person does the thing, sees a real result, and forms their own opinion. no trust required from you. the product does the convincing.

the mental shift for the user is completely different. after a landing page they think "maybe this works." after a free tool they think "I just saw it work." those are not the same buying decision.

what surprised me even more

the free tools also started ranking organically on Google for specific keywords we never targeted. turns out nobody else was offering these as standalone tools with no signup wall. so Google just gave us the traffic.

and the users who came through the free tools churned significantly less than users who came through other channels. they already understood what the product did before they paid. there was no "oh this isn't what I expected" moment.

what I got wrong early

I spent the first 3 weeks of the launch optimizing our landing page copy. changing headlines, testing CTAs, rewriting the value proposition. none of it moved the needle.

the free tools didn't change. and they quietly drove most of our revenue the whole time.

in hindsight this makes sense. a landing page is a promise. a free tool is proof. I was optimizing the promise while ignoring the proof.

the part I'm still figuring out

the free tools work but they're also a support surface. some people use them heavily without ever converting. that's fine as a marketing cost but it raises the question of where to draw the line between "free enough to build trust" and "free enough that nobody needs to pay."

we haven't solved that yet. right now we draw the line at volume and at features that require the full API. but I'm not sure that's the right place.

curious if anyone else has found free tools to be a major conversion driver, or if this is specific to our category. and if you've figured out where to draw the free tier line, I'd genuinely like to know.

I am basically an small level developer and i got this idea for an dating app where everyone is anonymous at first and the more you talk, the more information about the other person gets revealed so that it forces you to actually talk to each other making connections .

In addition i was thinking of:

• Having limited skips between chats
• Filtering/banning out nudity or accounts with any of that intentions
• showing only common interests so u have something to talk about.

I was just wondering if people are sort of interested in this for me to proceed with it.

i have got an idea to build a webapp like linktree for resturants to showcase their menu. that way there is no need to build custom websites. qhat do you think of this idea. How much should i actually charge for it?

15 days ago I shipped a launch platform for SaaS and indie founders who are tired of one-day spikes on crowded leaderboards.

Day 15 numbers:

- $425 revenue
- 213 products live
- 450 signups
- 12K page views (up 2,138%)
- 1,800 X followers (from 120)
- DR 44 (from 0)
- 350 newsletter subs

Pre-MRR. Revenue is one-time fees for featured slots and a hand-done submission service while I work out the right recurring plan.

What worked, ranked by signups:

1. Replying to other founders on X. If someone tweeted "just launched" or "looking for users", I replied with something useful and a soft mention. Biggest source by a wide margin.
2. X ads. Small daily budget, indie/SaaS targeting. Cheap, steady trickle.
3. My own X audience. 120 to 1,800 followers in 15 days. Daily posts with a number or a screenshot. Day 1 posts got 4 likes. Day 14 posts get 600+.
4. Reddit. r/SideProject, r/buildinpublic, r/IndieDev, r/juststart, r/linkbuilding. 30K combined views. Numbers + lessons format only.
5. Free dofollow backlink on every approved submission. Builders forwarded it to each other.

Happy to answer anything in comments.

Disclosure: I'm the founder. Will share the link if asked.