This is the same reasoning people use to say SaaS is dead, but it makes no sense. Rolling things yourself is often 10x more costly and not worth it, even with agents you need to pay 5-10 guys 150k-250k a year to build and train your own agent, why not pay fin 250k flat and not deal with any of it? Same goes with basically all other software that has nothing to do with your core product.
I see absolutely zero value in something like Fin. There is no model training needed. It's all context. Anyone who is training a Qwen model for their customer support is doing it wrong. Paying Fin $250k flat does nothing since it isn't going to actually know how to solve problems. The real challenge is the knowledge and context engineering and Fin doesn't help there. The technical stuff is really easy to build.
You misunderstand the model. Fin does not have flat fee. They charge exclusively for resolutions. That's the entire value prop.
Correct that knowledge and context engineering are the key. Fin DOES help here. They have an entire backend suite to help you build out areas where Fin is failing. It shows you questions it couldn't resolve, looks at the answers your human team gave, and suggests updates to help articles to
You're correct this could all be build by a skilled engineer, but that's not the point. It's built for non-techincal users to use and implement. A person who rose through the support ranks and shows some technical competency can learn the system without any software knowledge.
You don't need a fancy editor for "if this then do this". A simple text document is all you need. And if you do need a fancy editor, it's extremely easy to build it in 2026. Maybe 1-2 days.
I'm not a SaaS believer anymore.
In my case, I've spent the past 12 months running implementations at multiple companies. I've engaged directly with smart engineering teams to assist. It was not that easy.
What you outlined might work for a simple ecom business. It probably does 95% of the job for a simple case where you're delivering information. But it will fail the second it needs to take action or deliver personalized information based on client's account data.
That leads to the exact issue people here complain about... an LLM that doesn't actually answer the question, can't solve the problem, and is worse than talking to a human
But it will fail the second it needs to take action or deliver personalized information based on client's account data.
In 2026, every time I've tried to build a custom tool to replace a SaaS, I've succeeded. The biggest problem with SaaS is that they build a one size fits all. When you build a custom tool, you control everything from data to UI and it works for your business.
It's an incredibly common aspect of business. Enterprise level contracts often include the sort of white glove service to help fill in these sort of gaps. On simpler plans, having the tooling provided frees up just enough capacity to handle the exceptions to keep the process running smoothly (since one doesn't have to build and run simultaneously).
Sometimes people want to minimize the hassle with stuff. It's why car washes and oil change places and coffee shops exist.
In the last 6 months, I'm that person in the company who always said "let's just build it in a few days".
This process is an ongoing effort, with an upfront engineering commitment which depends entirely on the product, but can be months of work. But if you have your own backend, I would argue this hard works is made HARDER by implementing something like Salesforce/Fin, because you have to now pipe a bunch of data and structure over to them, which is a pain.
LLM models capable of doing this are a commodity, the UI for customers and support teams is pretty trivial, the database/backend is trivial.
Outside of some cases, if you have your own app, and you have a given support volume, build your own.
A recent example is that we replaced our support ticket system with an in-house built one. The new system lives directly in the same backend and uses the same database as our app. Every ticket now has real live customer data. You can't get this kind of integration easily using a 3rd party tool.
It was surprisingly easy to build. Just took 2-3 days for us. Massive improvement in productivity. Took about $100 in tokens to build. Maybe an hour of maintenance work per week.
This larger company took 48 hours: https://tradecore.com/resources/blog/we-replaced-zendesk-in-...
Doing this would've be seen as crazy in 2023. But in 2026, it's often an advantage. Better, more integrated, cheaper, faster.
I'm happy to buy if it's something I know we don't have the expertise to build. But for something like Fin, I know exactly how to build it for my company and I think I can build a better agent with better context, faster iteration, cheaper, and more tailored to my company's business.
and the amount of context needed to automate f500 is non trivial, plus you usually cant use reasoning because latency would blow up and you get escalated on
if this was so easy as you claim theres many millions for you to be made selling it to enterprises, but you wont
most f500s are going to outsource. i think sierra is already at 40% of the f50? and each of those deals they have to compete against teams of inhouse engineers and convince execs to buy instead of build
the reality is agents at scale is a hard problem and most f500 engineers are not equipped to solve it
Not they’re all getting incredibly expensive, even the last few startups I worked at were paying hundreds of thousands of euros for services that were total garbage.
Do I really need a crappy 20k/yr app to help me with my 1:1s? Do I really need a 100k/yr clicks counter that requires two devs to keep running and still heavily miscounts the clicks? Do I need another crappy app to manage my translation JSON files?
The value, of course, is that there is a website with a chatbox that some MBA can type in "never give any refunds anymore for any reason", and it just updates the AI support agent and sends an automated "I deserve a promotion and a raise" to their boss.
So all Fin is is a UI on top of the context engineering done by a software engineer who integrated with Fin. It's extremely easy to duplicate Fin's UI and get rid of the $250k fee.
It takes the same amount of time to build a custom agent as an agent on Fin. All Fin does is provide a fancy UI for non-technical people to create rules.
They can create the same rules in plain text. If they want a fancy UI like Fin to do it, just build one in a day.
A rule is just a line of text to an LLM.
I think they mostly benefit from time in market and name recognition. The AI angle was a good bet to make when they made it, but is increasingly less of a differentiator.
I don't think SaaS is dead - but I think for a product like Intercom, that is very expensive, they get eaten alive by smaller SaaS + in-house AI agent.
There's a wide swath of companies that do < (say) 20,000 cases monthly where the economics will never make sense. And a company finds Fin successful as it grows to 20k/mo, why would it decide to take on the headache as it grows to the 50k/mo? or whatever level where the economics could feasibly make in-house work?
The problem is that Fin prices at $0.99 per outcome. Only for companies with tremendous support volume would it even begin to make sense to build in-house.
I'm curious how you would calculate the other side of the ledger, the in-house approach. Assume the e-commerce business does not employ any AI/ML experts or programmers or anyone whose workday has ever been interrupted by a Github outage (this is the normal case for most businesses, not an artificial handicap). I'm curious how you would structure things to make an in-house more efficient than the $500/mo all-in.
You clearly have never ran a business and don't understand the dynamics of the make vs buy decision.