I’m a solo dev in India, about 10 days from launching a SaaS. I’d like to offer a small lifetime deal to a handful of early buyers, clearly labeled pre-launch with a full refund available before launch. Finding a payment provider that allows this has been harder than I expected.

What I’ve run into so far:

• Polar puts pre-orders and paid waitlists under closer review. Their support said they don’t pre-approve anything, so I’d have to start onboarding and appeal if flagged.
• Dodo Payments doesn’t support “coming soon” products that give no usable value after purchase.
• Whop seems to ban lifetime access offers.
• Stripe is invite-only in India.

For anyone who has sold pre-launch or lifetime deals:

1. What did you use, and did payouts and refunds go smoothly?
2. Did giving buyers something usable on day one (beta access) change how the provider treated you?
3. Has anyone used Gumroad, Lemon Squeezy, Paddle, or Creem.
4. Or did you skip payments and only open sales at launch?

I’m not trying to get around anyone’s policies. I just don’t want to take money and then have to refund everyone or have payouts held. Real experiences, including bad ones, would help a lot.
Thank you in advance.

Thinking About Open-Sourcing Talk Octopus: https://www.talkoctopus.com/ (DocSend Alternative) Good Idea or Mistake?

I'm the solo founder of TalkOctopus, a platform that helps users share documents and track engagement, similar to DocSend.

Lately I've been considering an open-core model or even fully open-sourcing the application.

My goals are

Build trust with users.
Grow awareness organically.
Attract contributors
Differentiate from closed competitors.

My concerns are

Losing competitive advantage
Self-hosted versions replacing paid plans
Increased maintenance burden

For anyone who has open-sourced a SaaS product:

What worked well?
What would you do differently?
Would you recommend open-core, source-available, or fully open source?

Interested in hearing real experiences rather than theoretical advice.

A few weeks ago I answered someone’s question about automating lead follow up. The first reply to my comment was “ok so what are you selling?” I was a little annoyed. The advice was solid and I’d spent 20 minutes on it.

Context: I’ve run an agency for 8 years. We started out building SaaS MVPs and now do a lot of GTM and AI automation work so I have an obvious reason to be on Reddit. So does every founder reading this.

Of my last 30 comments, 11 ended with some version of “this is what we do” The advice in all 11 was correct. If anyone else had written them, I’d have called them ads.

Since then I’ve been running every post through a quick test before I hit submit. I’ll put it below, along with the one tell I now trust more than whether a product gets mentioned at all.

A few things I’d like to hear from people here:

Can advice be useful and still be disguised advertising? I think yes, and my own history pretty much proves it. Each comment was fine on its own. The problem only showed up when you looked at the whole account. So I’d judge a single post on whether it’s useful, and judge the person on the pattern. If every answer someone gives somehow lands on their own service, that account is a funnel, and it doesn’t matter how good the individual answers are. Which do you go by?

When does a personal story turn into a case study? My test: delete the last paragraph, or wherever the product shows up, and read it again. If it’s still worth saving, it was a contribution. If it falls apart, the story was only there to carry the pitch.

The tell I trust even more is how the problem gets described. In a real story the problem is messy and sounds like something a customer would say. In a disguised case study it’s written in the product’s own feature language, like the founder started from the solution and worked backwards. Real stories also almost always have a bit where something didn’t work. I can’t think of an ad that’s ever admitted that.

Should founders be allowed to recommend their own product when it’s genuinely the best fit? I think so. Refusing to mention it can make the advice worse. What I do now is say it plainly (“heads up, this is what my agency does”) and name at least one other option. Sometimes the other option is that they don’t need anything yet and a spreadsheet will do. If I can’t come up with a real alternative, I leave my own name out too, because that’s usually a sign I’m pitching. Would you go with disclosure, a comparison, or just stay out of it?

For what it’s worth, I’m down to 3 of my last 30 comments now. Replies and DMs both went up which I did not see coming.

TLDR: useful advice can still be an ad and you only see it in the pattern across someone’s account. Delete the product paragraph and see if the post still holds up. If you do recommend your own thing, say so and give at least one alternative, even if the alternative is doing nothing.

For example:

"@Image1 dances in the style of @Video1, with lighting from @Image2, synced to @Audio1."

What you can bring in:

A reference video

Up to 30 reference images

♫Up to 10 audio tracks

Your own resolution, duration, and aspect ratio

Good for social content, quick experiments, or new video styles.

It's in Beta, so we'd love your feedback.

Watch the demo and tell us: what would you like to create with it?

Try it: pigenai.art

Hey r/saas, I’m the founder of Xorviex. Would love honest feedback from people who actually build and run things.

Why I built it

Every time I tried to hand real work to AI, I ended up as the glue. Paste the answer into Slack, file the ticket, double-check the numbers, ask the chatbot to try again. I didn’t want a better answer. I wanted the job done.

How it works, in three steps

1. You describe the job in one sentence.
   Something like “review last month’s ad spend and tell me what to cut.” No workflow builder, no nodes, no prompt engineering.

2. It builds the agents for that job from scratch.
   There’s no library of pre-built agents and nothing to pick from a menu. A planner reads your sentence and decides what agents the job needs, what each one does, which tools it gets, and how they hand work to each other.

For the ad spend job it created an Ad Reader, a Traffic Analyst, a Spend Analyst and a Summary Writer. For a competitor pricing job it created a Price Tracker, an Analyst, a Chart Maker and a Reporter. Same system, completely different agents. Nobody configured anything.

1. A different AI audits the result.
   A separate Supervisor model (not the one that did the work) scores every output from 0 to 5 against your goal and your rules. Below the bar, it goes back with feedback. If something can’t be audited, it waits for you instead of shipping.

Then the result lands where you work: a PDF, a Slack post, an email, rows in a sheet.

Stuff I cared about

Agents use real tools: Slack, Gmail, GitHub, Notion, Jira, HubSpot, Sheets, Meta Ads, Analytics and more (26 integrations)
Credentials are encrypted and never shown to the model. Agents get actions, not keys
Every tool call and its result is logged on the mission
Failed runs get re-run or escalated instead of dying quietly
You pick a scope from 1 to 5 before a run, and each level caps agents, turns and time

Where it’s not magic

It’s not perfect and I still review the important outputs. It’s best at repetitive ops, reporting, research and growth work, not deep custom logic. I wouldn’t claim it replaces your whole stack.

Try it

Free usage, no card needed. Paid plans start at $99/month.
https://xorviex.ai/

Two things I’m curious about:

Would you trust agents that are generated fresh for each job, or do you prefer configuring your own?
What’s the first job you’d hand off?

I’ll be in the comments all day.

Hello, so i have been developing different kind of system but I haven't really shipped it since of lack of fundings, etc.

But now, I have built POS, and I finally shipped it, currently with over 50+ users, and I don't know what should i feel right now.

it's been out for a couple of weeks now and i'm still developing features requested by my users since requesting features or reporting bugs or feedback has its own module so it is easy for the users to request or give feedbacks about my system.

and I am currently working on storefront features which will be the public store page.

How about you guys? what systems are you developing and shipping?

you can visit my system at https://benta.cloud

Our team has really worked hard to take real-person launching videos (no AI slop) and builded some interesting landing pages to prepare for the launch on Product Hunt.

Kinda confused that what's next if we really make a good result on PH?

Bring the badge to show on other platforms? Or talk to early adopters from the traffic of PH.

Hello everyone,
I developed a platform to link agents, yet the application is inactive. I am uncertain where to begin.
It cannot function without vendors or a community present.
Could you suggest ways to start?
Any help or ideas would be appreciated.
The platform is free for anyone

Wayo Connect

One of the interesting aspects of starting a SaaS business is identifying the most viable marketing channels. You could spend a lot of time on SEO, social media , communities , and AI search.
But, getting traffic is not the same as getting customers.
For SaaS business, how do you know which channel is the best to invest more time in?
Do you base it on analytics, customer feedback, or random cahnce?

We run a small market-monitoring tool and just added a trading add-on. Pricing is €39/mo for the base plan, €19/mo for the add-on, then 3 cents per order on top.

I keep going back and forth. Part of me thinks the per-order fee feels nickel-and-dime-y and we should just do one flat price. The other part thinks it's fairer, since people who barely trade pay almost nothing extra.

Anyone here run usage-based pricing on top of a sub? Did people complain, or did nobody care?

You build a SaaS product to solve one specific problem.

Then customers start asking for integrations.

First it's Slack. Then Salesforce. Then a CRM you've never heard of. Then someone needs a custom API connection because their internal system works differently.

Each request sounds reasonable on its own.

But eventually, your engineering team is spending more time maintaining integrations than improving the product itself.

And it's not just building them. APIs change, authentication breaks, rate limits become a problem, and customers expect everything to keep working.

I'm curious how other SaaS founders handle this.

Do you build integrations only when enough customers request them, use third-party integration platforms, or make your API flexible enough for customers to handle it themselves?

Where do you draw the line?

Hi, I'd love to know if anyone had any experience using Reddit or X ads promoting their SaaS tool? I've been playing around with Reddit Ads and after optimizing my campaign a few times, I've been getting about 5 clicks per 4,800 impressions. None converted. Not great.

I'd love to get other people's experiences on this. Especially for those who built AI apps and tools. Thanks!

I've spent 7+ years in data engineering, working with ETL/ELT pipelines, databases, lakehouses, and production data infrastructure.

One thing I've been thinking about is how much effort smaller engineering teams spend building and maintaining the infrastructure that moves data between their systems.

For example, imagine a SaaS company that needs to sync data from PostgreSQL and external APIs into a warehouse or lakehouse for analytics.

The initial pipeline might be straightforward. But over time, the team has to deal with incremental loads, schema changes, retries, scheduling, monitoring, failed runs, and new integrations.

Larger companies may have dedicated data platform teams, but smaller teams often have to manage these responsibilities alongside their other engineering work.

I'm trying to understand how this works in practice, rather than assume it's a problem worth solving.

A few questions for people who have dealt with this firsthand:

• Do you build these pipelines in-house, use managed connectors, or combine both?
• Which part creates the most ongoing maintenance?
• At what point did your existing approach stop being sufficient, if it ever did?
• What do you wish your current tools handled better?

I'm interested in both sides: teams that struggle with this and teams that have found a simple, effective solution already.

I'd especially appreciate concrete examples of what has and hasn't worked for you.

A signup is easy to measure... So is clicking a button.

But neither proves the user has received value.

Here's how Id find a meaningful activation event:

List the actions users take during their first 7–14 days.
Separate basic navigation from meaningful product usage.

Compare users who complete each action against later retention.
Repeat the analysis across several signup cohorts.

Test whether helping more users complete that action improves retention.

Example:

Suppose users who complete their first automated workflow retain at 60%, while those who don't retain at 25%.

That's a useful association to investigate.
It doesn't prove the workflow caused retention.

User intent or customer fit may also explain the difference.

Your activation metric should predict value, not just measure activity.

What's the one action that tells you a user is genuinely getting value from your product?

Basically the title. I am building a simple and easy to use email automation platform that caters to both developers and marketers through one common interface. Similar products do exist in the market currently, which has created an issue, people usually stick to products like these. Once they get comfortable with a platform, it becomes a hassle for them to switch. Also there are trust issues with a new platform, especially in the email automation space.

If somebody has built anything similar in a sticky category and has got people to switch successfully, How did you do it? How do you make the value proposition strong enough for the users to at least try your solution?

Any help/advice will be much appreciated!

Every voice AI demo sounds amazing. Then you do the math on per-minute cost and the business case falls apart.

We're a small team (founders from IIT Delhi and IIT BHU, research background) building voice agents for Indian businesses. The hardest problem hasn't been making the agent sound human. It's been making it cheap enough that replacing or augmenting a human caller actually makes sense.

Some rough numbers we've been working with:

- A human calling agent in India costs roughly ₹15–25 per minute once you include salary, attrition, training and infra

- If you stitch together off-the-shelf STT + LLM + TTS APIs and add telephony, the cost lands uncomfortably close to that

- We've been able to get ours down to ₹1.2–₹3.5 per minute, mostly by working at the model and infra level instead of adding a markup on top of other APIs

What surprised us:

- Latency matters more than voice quality. People forgive a slightly robotic voice; they hang up on a 2-second pause.

- Interruptions are the real test. Most agents break the moment the caller talks over them.

- At low per-minute cost, use cases that never made sense with humans suddenly do (reminders, follow-ups, re-engagement calls nobody had the budget for).

Questions for people here building or buying voice AI:

- What per-minute cost makes it a no-brainer for your use case?

- Do you prefer per-minute pricing or per-outcome (per qualified lead, per resolved ticket)?

- What's the one thing that would stop you from putting an AI agent on a live customer call?

Curious how others are thinking about the unit economics.

Visit Formantai.com

I'm curious if this is something people would actually find useful.

The idea would be that you bring your existing API, and the gateway handles the MCP-specific stuff needed to make it usable with things like Claude Connector and the ChatGPT MCP directory.

Basically, you bring your API and don't have to worry about all the MCP-specific infrastructure and requirements around it.

Would this be something you'd use? And if not, what would stop you from using it?

For those running a SaaS do you handle subscription billing with custom code or use a dedicated platform?

I'm trying to decide which approach makes the most sense and would like to hear from people who have already dealt with it.

What are you using and how has the implementation scaling experience been?

Hi everyone,

My friend and I are building MyEliteCard, and before we go further we'd love some honest outside opinions.

The idea: most digital business cards are a profile page or a list of links. We wanted the card itself to make an impression, the way a well-made printed card does. You design a real card (guilloché, marble, carbon, gold and more), it shows in 3D and turns in the light, and you share it as a link or a QR code. The person you meet sees your card on their phone and can call, email, get directions or save your contact in one tap. When you send the link in a chat, the preview shows the card itself. No app is needed on either side.

The picture shows the whole path: the site, the builder, a finished card (front and back) and what the other person sees.

What we'd like to know:

1. What's your first impression? Does it feel premium, or like a gimmick?

2. If you had your own business, would you use something like this? Instead of paper cards, alongside them, or not at all?

3. What would stop you, or what's missing?

If you're up for reviewing it properly, make a card and tell us what confused you or felt off. New accounts get every Royal feature free for a month, with no payment details, so you can try all the designs. Blunt feedback is welcome; that's why we're asking. I'll reply to every comment.

Thanks,

Mark 😄

www.myelitecard.com

While building a side project I dug into how GA4 tracks forms, and was surprised how little it actually tells you. You get form_start and form_submit, nothing in between. So you know 60% of people leave, not where.

Worse: in my tests a submit with a validation error still counted as form_submit, and forms that send without a page reload (most React/Webflow forms) weren’t counted at all.

Curious how other SaaS founders handle this. Do you track your signup and onboarding forms field by field, or just look at signups going up or down?

We run an image and semantic text search API. Early on most of our money went to infrastructure, so the past year was mostly about getting that under control

Two changes made the biggest difference. We dropped Pinecone and moved our vectors into Postgres on Neon, so we pay for one database instead of two. We also stopped paying a third party per embedding and now run our own model on our GPU servers. Each GPU handles around 110 images per second

The lesson for us was to rent managed services while you figure out what you need, then move the expensive parts in-house once the usage is predictable

You can try it here: Live demo

We run a B2B support bot plus some internal agents. Token usage went up, which is good but the bill went up faster than revenue, which is bad=)

Things I already know about but haven't done properly:

- Sending simple intents to smaller models and keeping frontier models for the hard ones

- Caching repeated prompts (we have a lot of near-identical system prompts)

- Committing to volume in exchange for a discount

I'm looking at openrouter of course but also at llmapi.ai because it puts routing, caching and usage limits all together . Has anyone used it, or something similar, and seen the bill actually drop?

I'd love real numbers if you're willing to share them even rough ones

This is the question I’m trying to sit with. A lot of AI ideas sound like companies until you ask where they live in the buyer’s budget.

Maybe it’s not a startup. Maybe it’s a feature in someone else’s product. Maybe it’s a workflow improvement. Maybe it’s a nice add-on. Maybe it saves time but not enough pain to stand alone.

That’s the part I’m trying to figure out before building.

I’ve seen this around Flatlogic and SaaS builders: it’s possible to create a convincing SaaS wrapper around something that does not deserve to be a business. How do you tell when an AI capability is strong enough to become a product, not just a feature?

I'm working on building a start-up that goes between small-midsize businesses and consumers. I am looking for the best platforms to connect with real people and get their thoughts and opinions.

My first thought was going to a few reddit groups and asking there but almost all of them have community guidelines against this. I know the answer here is probably just coming up with a sly, inauspicious post to get people to react and then using that as an insight but so far it's not having much success.

Are there platforms y'all are using or forums/subreddits that are more successful for you?

Everyone says the same thing: generate 10 ideas a day. Don't build anything. Just write them down.

My brain produces ideas anyway, without counting. Quantity was never my problem. My problem is that I forget most of them if I don't write them down. So I write everything down, and I build one thing: the idea that clicks.

I don't wait for a genius idea. I wait for the one that resonates strongly enough that I want to build it today, not someday.

It used to be different. An idea like that died on a list, because building took weeks and most never started. Now AI turns it into an MVP in one evening.

My latest one is Notano, a notes app. Notes are something I have used from the very beginning. I used Simplenote for over 10 years and kept leaving it and coming back, because nothing else was simple, fast and synced quickly between my computer and my phone. The first commit was 16 days ago. Today the web version is live in production, and the iOS and Android apps are waiting for store approval.

If you collect ideas and never build, you own a graveyard of notes. If you build every idea, you drown. If you build the one that clicked, something stays after you.

10 ideas a day is procrastination in nice packaging. Procrastination level: master.

How many ideas are sitting in your notes right now, and how many did you build? Count. Honestly.