AI SaaS Builds Fast but Customers Don't Come Back

“I think SaaS will be killed by AI. Your ideas will just be copied into an open source alternative because it is easier than ever to do so. I think most solo SaaS founders are cooked, and they are too relaxed about that.”

“Yeah this is from using it extensively, building 5 different apps including a flutter mobile app (that’s where most tokens went).”

“I run GrowthSpree, a B2B SaaS marketing agency, selling into the US and Europe. Six months ago we made a bet that the agency model itself was broken for the AI era, so we tore ours down and rebuilt it. This is what we did and what actually happened. The bet: become the first truly AI-native marketing agency. Not an agency that "uses AI tools" by bolting ChatGPT onto a 2015 workflow, but one where AI is wired into how we find demand, win clients, and run delivery. We rebuilt the operating model,”

“So based on the trend and the performance of models like Fable, what evidence do you have they will never get any better than they are right now 7/20/2026?”

“for the last year, i kept chasing ideas that looked exciting. every week there was a new ai trend, and i thought if i built something around it, maybe this would finally be the one. i launched a bunch of ai agent apps. i spent hours building them, polishing them, and shipping them. none of them worked. they didn't get users, they didn't solve a real problem, and honestly, i don't even blame the market anymore. after a while, i realized i was asking the wrong question. instead of aski”

“Nailed it. This is exactly like the early 2010s cloud migration. The missing link right now is the "harness" you mentioned. Most orgs don't have the budget or hardware to self-host massive local frontier models, so they need a local execution layer that manages boundaries for cloud APIs securely. We open-sourced a local MCP gateway built exactly for this—it acts as that zero-trust harness between the dev's IDE and the model: https://github.com/Maha-Strategies/maha-mcp-bridge”

“This is a better breakdown of the problem than I've managed myself, so let me actually answer it instead of dodging. They cluster, mostly. The shared internal tooling I mentioned in the post exists because I noticed the same thing you're describing: what a client calls their one-of-a-kind rush pricing rule usually maps onto one of a handful of shapes I've already built a version of. The genuinely novel part per client tends to be smaller than they think, usually one specific approval”

“In my view, and from what I've read as responses to many projects on this sub, the view of many more, is that AI slop is an issue. I think it's gross and obvious, and I think anyone paying attention to this field can recognize the telltale signs immediately. Just scrolling down this sub is a great example, with so many of the titles and intro written in a nearly identical voice. Of course I understand and appreciate the irony in trying to use AI to fix AI, but we're already deep into”

“I’m looking to get into SaaS but instead of using ai to create my own software I want to build everything my self from scratch. I know as someone who basically knows nothing about softwares it’s going to take quite a bit of time to learn and actually make a software. If anyone can give me any pointers on a road map to study I’d really appreciate it. I want something that can integrate on pc and mobile submitted by /u/ShallotVegetable1833 [link] [comments]”

“Please prove me I am wrong. They say the following to justify why SaaS is not dying: Workflows matter more than AI Trust and reliability are critical Compliance creates barriers to entry AI complements SaaS rather than replacing it I think this is true from the perspective of one SaaS editor competing against another SaaS editor. However, it seems to me that the main issue is the ability for customers to build their entire system with just a few prompts. They will say: “Just find a problem and s”

“I agree that AI has lowered the barrier to building products, so distribution and trust have become much more important. Where we think we stand out is the combination of AI-generated forms, full customization after generation, a large library of ready-to-use templates, and a workspace that's easy to use on both desktop and mobile. We've also focused on offering strong value for money, with our Pro plan at $19/month including 2,000 responses , while many established alternatives charge m”

“Everyone and their cat is trying to add AI to their apps, SaaS and businesses. Usually it's some sort of chat or sparkly "AI" button that's placed at the most visible spot on your site or app. It often looks very annoying, works poorly, users hate it - but the companies still push it, because if they aren't "AI-native" in 2026, they're pretty much doomed. And it makes sense it works poorly. If the company uses a powerful model, it will cost A LOT. So, either t”

“You're right, and honestly this comment made me stop and think. thank you for your constructive comment. I was thinking that, architecture is simple only 3 containers, and when one company start tonuse this when they are startup they will use it in 3-4 years max. They need to scale in this period or they will not need a crm anymore. live counter is going on the pricing page for sure to get more customer. I can pull it straight from the license key database, no manual updates needed there. I&”

“Not saying I agree with it but that is the question I believe. But as others have pointed out it’s pretty typical. But just some feedback… it’s not a good look to assume people give up their expertise for free. If you can’t pay that’s also fine but be transparent about it, but don’t take advantage of people’s good will. That’s not an innovative attitude!”

“So the LLM is interacting with the SaaS via the browser, not tools (MCP server or similar), right? So the contract between your product and the SaaS is the UI. If this is the case I presume that would be really fragile. Second, you say the business logic is prompting. Here you might be missing the verification of goal accomplishent. If I extrapolate this to how we build AI feature now where we have to put guardrails, define evals, set tool paths for each use case so that we ensure tools are used”

“I might be wrong. Open to listening but your picth just seems unreasonable. Each SaaS product is different, serves different workflows, for different goals. That's the reason why just connecting a blackbox to a SaaS and it just knowing what to do seems unrealistic to me.”

“The assumption you seem to be making is that in (enterprise) SaaS UIs one of the barrier to adoption, retention and growth is in the “number of clicks getting to perceived value”. That might be true in Adtech and some muscle memory type low dollar b2c apps. This assumption needs to be tested before building anything. That’s pen and paper work and not agentic/agent/LLM/AI type work. Also, the procurement process surrounding SaaS services and its UIs are not concerned with UX typically.”

“Hey everyone, I work on the operations side of a UK commercial risk-tech startup, and we’ve been seeing a really painful trend lately with British SaaS founders who are scaling fast. I wanted to drop a quick warning here because nobody in the traditional UK insurance space actually talks about this. If your company has grown significantly this year, meaning your revenue doubled, you hired a bunch of new staff, or you pushed a major new feature (especially AI) your current Professional Indemnity”

thinking an AI wrapper suddenly makes it better is such an annoying trend

“Well, that makes sense. And you're broadly right. Any engineering team that has spare capacity could definitely do this, but even then, it doesn't very often happen. I mean, crikey, I know hundreds of digital agencies, all with excellent engineering teams, and they all buy their project management tools, and they all buy their CRMs and their other software, because it's just simpler. It works out of the box. They don't have to maintain it. They can concentrate on their much highe”

“I keep seeing people say vibe coding is going to replace developers, but I think it mostly exposes a different problem. It's never been easier to build a working SaaS. You can go from idea to MVP in a weekend. The hard part starts after that. I've seen polished apps get almost no users, while another product with a clunky UI quietly grows to around $35k MRR. The difference wasn't the code. It was that one solved a very specific problem for a very specific audience. A lot of founders”

“Abandoning all the AI Plus users to force a Business tier we don't need or want.”

“Maybe I’m late to this, but I’ve been noticing a weird shift. When I’m comparing tools now, I catch myself asking ChatGPT or Perplexity before I even open Google. Stuff like “what’s a good CRM for a small team?” or “which email marketing tools are actually worth trying?” Makes me wonder if this is starting to matter for SaaS discovery, or if it’s still mostly noise. Are any of you actually tracking whether your product shows up in AI answers, or is that not on your radar yet? submitted by”

“i think acquisition is where this gets ugly. building is basically cheap now, so everyone can throw a product into the world, but attention didnt get cheaper. if anything users are more suspicious because they’ve seen 50 half-baked tools already.”

“How much could this possibly be costing you at this stage. Seems kind of odd to "run out of money" on just AI infrastructure costs”

“My advice. Get a job and keep working on it. Automate as much as you can with AI (dev loops), but don't ship slop. You aren't gonna get investors until you can demonstrate value (by having customers). Its a long road.”

“That was pretty much already done with ONARQ. Anyone with no business experience can be successful at business now, but still you can only lead a horse to water”

“so... with near-zero churn and people asking for demos, i would spend a defined window proving repeatable acquisition before selling. give yourselves 60-90 days with one channel, one buyer, and a weekly target for qualified demos or new MRR. track how much founder time each customer consumes, whether demand comes from the same kind of customer, and whether sales are repeatable rather than referrals. if you hit the target and can see growth without the business becoming a full-time services job,”

“I agree! With Ai, the hard parts of building a SaaS aren’t just writing code, it’s understanding users, making the right decisions, designing systems, design better architectural decisions, solving unexpected problems, and getting people to actually use the product”

“I’ve been tracking the massive environmental impact of AI recently specifically how resource-heavy these models are when it comes to electricity and water consumption. We are already starting to see real-world friction. For example, Georgia lawmakers introduced bills (like HB 1059) to pause data center permits, and several US counties have put a freeze on new builds due to strained local grids and public protests. On top of that, there's a lot of viral misinformation floating around, like th”

“History definitely rhymes. I am old enough to remember the early social media backlash around “digital sharecropping”: the idea that when you build your business entirely on Facebook, Instagram, or YouTube, you are building on someone else’s land. You create the content. You attract the customers. You build the reputation. But the platform controls the rules. A lot of businesses learned that lesson the hard way when algorithms changed, accounts were suspended, organic reach disappeared, or terms”

“I'm aware of the responsibility shifting here but even then my point still stands. I've seen some SaaS companies tighten their grip thinking "they'll never bother because of the regulatory burden" only to watch large customers walk away and build in-house solutions instead. Super common? No, but this was pre-LLM and slightly later.Compliance is an important factor in customer retention, but the cost of the other side of the equation (technical implementation) has dropped significantly and in som”

“How do you founders deal with customer's issues around data protection? Things like "don't use my data to train or find tune your models", "don't aggregate my data to derive analytics or benchmarks" etc. Do you negotiate, accept and use workarounds, or something else? submitted by /u/wishingchairs [link] [comments]”

“> It’s good enough and it’s only getting better. People say this but it’s not actually guaranteed. It could also get worse… it’s a concept lots of people in the space are familiar with: model drift. This is why they need to keep training models. However, to keep training models they need more new data. If new code is all AI generated, it’ll exacerbate the flaws it has. Another factor is price. If local models catch up, self-hosting becomes much cheaper. It’ll be more about licensing harnessin”

“Before the infra was owned by SaaS companies, the business logic was built by them and that gave a bit of leeway to experiment and release. With Agent companies owning neither, just the execution layer, obviously the dynamics are bound to change. SaaS operated akin to B2B model, while agent companies are operating in B2B2C model, just being an intermediary. Try owning the infra, as well as the business logic (model), you will get back the leverage that you feel is lost.”

“the pattern i keep seeing when i look at how SaaS companies reposition after their AI feature turned into table stakes: the ones that recovered didn't try to out-AI the AI wave, they moved the pitch to where the output actually plugs into a workflow. so "we write your outreach messages" becomes "we update the sequence automatically the moment someone changes jobs or the company raises a round" or whatever the actual trigger is. the generation part doesn't go away, it”

“I do think if any of the model providers is at least somewhat trustworthy with customer data, it's Anthropic. They're on top right now and that's because of their focus on enterprises. They'd be obliterated tomorrow if it leaked that they had agreements (whether a slider in the UI or a custom contract) not to train on customer data and they were caught doing it. Their reputation is all they have and nobody wants to be the one who destroyed a trillion dollar market cap. The gain f”

“It's the long-tail search traffic that keeps trickling in. I've noticed it's especially good for niche AI categories.”

Gotta build a software factory lol i kinda did this and its kinda working

“Building agency infrastructure like multi-account management and white-labeling is a massive technical challenge on its own, so it makes total sense why your focus was there. The shift from basic AI generation to agentic workflows happened almost overnight. A few years ago, AI personalization meant pulling a line from a LinkedIn profile and plugging it into a template. Today, it is about continuous reasoning, like watching for actual buying signals and mapping them to a specific ideal customer p”

“tbh the copy layer is almost noise at this point. done enough B2B outreach to see that once everyone got AI writing, the differentiation moved back to data quality and timing. who you reach and when (new funding, leadership change, expansion into new market) hits way harder than perfect copy. might actually be that your ICP-matching piece is the more defensible part of what you built, just needs the data side to match”

“Hi all, I've been building a SaaS business for a bit now, and it is basically AI deep research and some stuff that is pretty hard to do manual deep research on or even deep research with a basic chatbot. (I am not saying what the topic is just because its still too early for me to release and I want to be vague, but it is a specific topic) My business plan is basically to focus almost solely on ad revenue and maybe affiliate links and potentially sponsorships of my newsletter if I can ever g”

“A few years ago, we built an AI feature into the product that felt genuinely useful for what the other AI models could do at the time. You'd put in your website URL, it would figure out your target ICP and industry, suggest a messaging angle, write the messages, and create a campaign for you. For when this was built, it was a real step up from doing all of that manually. Then I got busy with other things. Over the next year or two, I was deep in building out agency functionality. Bulk campai”

“I Will test your SaaS end to end, I will identify what makes the SaaS good or what breaks user flow even an AI couldn't identify. I will be your first paid customer to test your entire paid plans. I will be your first feedback provider. Let's go... submitted by /u/synthetic-sytrus [link] [comments]”

“I’d probably build once people are already trying to solve it without you. If they’ve made some annoying manual process for it, that tells you more than a few people saying the idea sounds cool.”

“For SaaS products adding AI features, I think the clean split is becoming: Use hosted frontier APIs for core text/reasoning features. Route between providers instead of hard-coding one model everywhere. Keep GPU-hosted custom models for the parts where generic APIs are not enough. Make every expensive workflow async, logged, and cost-capped. Scale inference endpoints to zero when demand is spiky. This is especially relevant for SaaS teams building things like: document research / citation workfl”

“After AI, if you provide chatbox on your platform for every user, or ai agent, even after you scale, you can not reduce the cost, because more customers mean more tokens. I wonder how the saas community think of this problem ? submitted by /u/Natural-Lab-6211 [link] [comments]”

“Solid breakdown. The point about AI engines (ChatGPT, Perplexity) reading your site is underrated — a lot of people are still optimizing purely for Google when the traffic mix is shifting fast. Curious about your editing process. How much manual editing are you doing per post? I've found that AI-generated content tends to have a "sameness" to the sentence rhythm that a human pass can fix in 10-15 minutes. Also, are you using any structured data / schema markup? FAQ schema on blog p”

“I wouldn't mistake a market correction for the death of software. The easy opportunities are mostly gone, but there are still plenty of problems worth solving.”

“What happens when these companies crash? What about if they are hacked? Or more likely, when randomly suspend a model? I think the answer is owning an autonomous bot that runs without any external dependencies. Like R2D2 from star wars; it should be helpful in the palm of your hand. Right now the tech isn’t there imo but soon it will be possible. I think eventually G2M with a 2 part product: an all in one homelab “mini-pc-on-steroids” and then the actual agent as an opensource product pre instal”

“I have been spending a lot of time looking at AI SaaS products lately, and one thing I notice is that many interesting projects do not seem to get much attention. Some of the ideas that I have found were not popular on Product Hunt or all over the internet. They were just being built by founders who are trying to solve a problem that AI SaaS products can help with. I would like to ask you about your AI SaaS projects. If you are building an AI SaaS product, what is it about? I really want to know”

“I own significant shares in an AI-driven media production company we started 1.5 years ago (films, commercials, virtual-production work for clients, plus some bigger fictional projects). I can't tell anymore whether I'm the clear-eyed one here or just the burnt-out one, so I need an outside read. What looks good: Solid first year. We've held a few very large clients for a couple of years. Even after our first CEO left us around 250k in debt, we removed him and cut it to 130k in 3 to”

“finish building it faster. with AI building the product doesn't take weeks or months people already forgot the concept of waitlists for SaaS”

“people's opinion on SaaS is already shiftting. imagine that they sniff outs it AI created or has some to do with it!”

“For me it shifted the hard work from coding to testing and, debugging is a little easier than before.”

“I think a lot of builders just dont realize how much work it is to support an api as a product, docs, support, uptime, it becomes a whole second business, so they keep it internal and focus on their main app”

“Something I keep seeing with micro SaaS builders: the product is a UI wrapped around one genuinely useful capability. A parser. A converter. An enrichment step. A scoring model. Some piece of logic that took real work to get right. That capability is an API. Maybe other developers or builders would actually pay to call it. This is what is standing between "hey I have an endpoint" and "someone pays me to use it": Metering and billing. You need per-call usage tracking, a billin”

“is this sub AI slop replying to AI slop? seriously what is this. Who in the world writes one micro sentence per paragraph? When did this become a writing style?”

“I am in a loop of just building stuff same lol. what finally got me out of it was realizing that Google and now AI tools like perplexity literally just surface whatever has the most backlinks and authority. so if you dont own that real estate someone else will and theyll get all your potential users. I started putting content out through Outrank because doing SEO manually was never gonna happen for me, im too deep in the code. but the real advice is just pick ONE channel and commit to it for 3 m”