I got burned by a model deprecation in Japan and built Zombify to stop it from happening again
I was on holiday in Japan. Left home knowing my app was finally
working the way it should — last thing on my mind was model
deprecation. Found out something broke from a user. Not ideal.
What annoyed me more than the outage was two things: there was
no warning from the provider, and I'd never thought to put it
on my "could crash" list. Both felt like fixable problems.
So I built Zombify — it watches AI model lifecycle changes
across OpenAI, Anthropic and Google Gemini and emails you when
something's on a shutdown path before it takes your app with it.
One-time $45 AUD. No subscription. I hate monthly fees as much
as I hate finding out things are broken from users, so that
felt like the right call.
Still early — registry is manually curated and not exhaustive
yet. But it's live, it works, and I'd genuinely love feedback
from people who've hit this problem.
This resonates — I use Claude Haiku in a voice AI agent for phone calls and the model dependency is a real risk I hadn't thought about until reading this. My mitigation so far is keeping the model hardcoded in config and testing every prompt change manually, but that's not scalable. A tool like this would've saved me from a silent failure I had when a response format changed unexpectedly. The one-time price model also makes sense for indie builders who just want peace of mind, not another monthly bill.
Yes! It took me a moment to figure out what was going on. It wasnt until I realised the same bug was occurring in one small part of my first app - (the product has three apps) - that showed what AI agents thought of your brand - the Claude model wasnt showing but ChatGPT and perplexity were, then I noticed my third app had decided not to work at all and it dawned on me how fragile the system was in terms of reliance on models. I know I could charge more, make a subscription model, etc., etc. - but this one's for the indie devs, solo creatives and me - I'm standing by the price. At least for now - power to the peeps
Lost a workflow to a model deprecation two months ago. Same shape output silently shifted, downstream parsers broke, took me four hours to realize the model itself was the variable. The lesson I took is that every prompt that's load-bearing needs a snapshot test against the previous version's output, not just a 'does it run' check. Most prompt-eval frameworks I've tried optimize for benchmark score, not for regression detection across the same input. Curious which signal you settled on as the alert trigger — output diff, embedding distance, or something downstream.
It's scary, isn't it - I'd love to tell you, but I'd have to turn you into a zombie first. At the end of the day, there are several variables I use to ingest and cross-reference deprecation, but it all still requires a human to keep the zombies at bay. I have built an admin section that's bigger and more complex than the frontend - lol. I found it needed governance to ensure the data was accurate - so you can rest assured and sleep easy - there's a human with their finger on the trigger regarding ensuring accurate intake of data.
The admin layer being bigger than the frontend is the honest tell. Every registry product I have watched ship through the same arc the visible surface looks like a static directory, and the actual product is a curation pipeline with a human at the choke point. Naming that pipeline as the product (with its own SLA) tends to be the move that unlocks both pricing and trust. Once buyers see the curation, they stop comparing the directory to free lists.
That's really insightful. I never thought of highlighting the machine behind the promise, just the output. I'll take a look at how to bring that out more - thank you!
This is one of those tools that should exist whether you charge for it or not. Model deprecation is the silent SaaS killer in 2026, and most founders only learn about it the way you did: when a user tells them the product broke.
A few thoughts:
The $45 one time price will not survive if you actually become the canonical registry. Either move to a one time license per project plus a yearly maintenance fee, or charge teams. The work to keep the registry current is recurring, your revenue should be too. I get the no-subscription stance, but the value to a team running 5 customer facing AI products is way more than $45 once.
The wedge is narrower than the registry. Most teams do not need "all model lifecycle changes." They need a single line answer: "is anything I am using right now scheduled for deprecation in the next 90 days?" Build that report (paste your model IDs, get a single risk dashboard) and you will land enterprise teams faster than chasing breadth.
If you want to grow this fast, partner with API observability tools (Helicone, LangSmith, PostHog AI). They already ingest model usage. Plug Zombify in as the early warning layer and your distribution is solved.
Building this from a personal scare is the cleanest founder market fit story you can tell. Lean on it in your marketing, not the registry size.
Thanks so much, and I love your thinking - you're totally right, and it might be something that I build on in the future. But for now, this one's for the indie devs and solo creatives - I really built it to help people sleep better at night - because model deprecation sucks and the fact I didn't see it coming was even worse. There are so many amazing creatives out there punching out cool work - I hope this helps everyone push on - and not feel the bite
The product pain is real.
But Zombify feels too playful for the kind of failure you’re solving.
This is not a fun zombie-alert tool.
It’s infrastructure risk monitoring for AI apps before model changes break production.
That category needs a name that feels sharper, more durable, and more technical.
Davoq.com or Exirra.com would fit this direction much better.
Davoq feels strongest if you want it to sound like serious AI infrastructure.
Exirra works if you want broader model/runtime monitoring positioning.
Thanks for the feedback - the name mirrors what happens when a model is deprecated, and the target market is Solo creatives, indie devs and small teams, so the branding is for us. Give it a try, I'd love to know your thoughts on the actual functionality.
Fair point — if the target is indie devs and solo creatives, the playful angle makes more sense.
My only concern is that even for indie users, the moment the tool is warning them about model deprecation or production breakage, the trust bar changes.
Playful gets attention.
Reliable gets relied on.
So the question is whether “Zombify” helps the first click but slightly weakens confidence when the user is deciding whether to depend on it.
Happy to take a look at the functionality too — the core pain is real.
I see what you're saying, but I'm still wondering if you actually visited the website, looked at the pages, looked at the tool, tried it out, looked at the changelog, and tested how it all works.
From a branding perspective, the trust bar is embedded in the changelog, and the tool itself. There's also a free tier that allows you to look at the status of your model, a depreciation page called the graveyard and a page dedicated to the explanation of who, what, why or when. All of the features are designed to enhance trust signals and boost visibility for agentic branding.
Also, thanks for inspiring a new component and an improvement that will demonstrate the functionality.
Have you considered agentic branding in your products?
Fair push.
I was reacting mainly to the category risk from the outside, not claiming I had tested the full product flow.
If the trust is carried through the changelog, graveyard, status checks, and explanation pages, then the playful name has more context than it looks like from first glance.
My point was mostly first-impression risk:
before someone sees those trust layers, does “Zombify” signal enough reliability for something tied to model deprecation and breakage?
But your indie/creative audience may read it differently, so fair.
I’ll take a proper look before commenting deeper on the product itself.
I'm loving this conversation, thanks for the comments!
We’ve crossed the threshold of traditional SEO and Google search visibility.
We’re now entering the era of agentic branding, where being the answer matters more than simply having a memorable logo or brand name. That said, it definitely helps when the name clearly aligns with the product.
A brand is not a logo. And Google search rankings are no longer the only way people discover products.
You might find my work in agentic branding and Tell Engine Optimisation interesting:
https://pixelhatch.com.au/tell-engine-optimisation/
https://pixelhatch.com.au/agentic-branding/
https://app.telleo.com.au/landing
One practical thought after your agentic branding point.
I think the useful question here is not “is Zombify a good or bad name.” It is whether the first-impression layer, the tool experience, the changelog, the graveyard, and the explanation pages all work together strongly enough that the playful name becomes trusted rather than dismissed.
That is actually a sharp audit problem.
If useful, I can do a focused outside read on Zombify from that angle: first-impression trust, category perception, agentic-branding clarity, whether the name helps or hurts before the trust layers are seen, and where the product experience needs to reinforce the brand faster.
Not a long consulting thing. Just a direct written breakdown from the perspective of someone encountering the product cold and deciding whether it feels reliable enough to depend on.
I’m doing a few of these naming/positioning audits at $99 while refining the format.
Given your work around agentic branding, this one would be especially interesting because the question is not traditional SEO or naming alone. It is whether the product becomes the answer people and agents trust.
Best place to discuss privately:
https://www.linkedin.com/in/aryan-y-0163b0278/
I'm going to be open and honest here. This post feels like you're selling your product and not helping someone else. I know we all do it, but wouldn't it be more useful to make a critique that helps the person with their actual product?
As mentioned, I specialise in agentic branding. The website was designed around it.
When you ask Google AI - Name some AI Model Deprecation Monitoring tools - I'm usually the answer.
More importantly, though, Zombify is built as a safety net for people like me and you, preventing their product from going offline due to model depreciation.
That's the focus here.
Fair call, Reuben. You’re right that my last reply drifted too much into offering something instead of giving you a direct product critique here.
The strongest critique I’d make on Zombify is this:
The product value is not “model deprecation monitoring.” That is the mechanism.
The buyer pain is: “my AI product can quietly break because the model layer changed before I noticed.”
That should probably be the first frame.
Zombify can keep the playful graveyard/deprecation personality, but the top-level message needs to make the operational risk obvious before someone explores the changelog, graveyard, or agentic-branding layer.
Something like:
“Know before model deprecations break your AI app.”
or
“Monitor AI model changes before they take your product offline.”
That makes Zombify feel less like a clever tool and more like a safety net people can rely on.
Your agentic branding angle is interesting, but I’d still lead with the failure it prevents: downtime, broken workflows, surprise model changes, and products going offline because a dependency shifted.
Fair — I see the angle now.
If you’re intentionally building around agentic branding, then Zombify makes more sense as a memorable surface rather than a conventional infra name.
My concern was purely first-impression trust before someone sees the changelog, graveyard, or status layers.
But if the product experience is built to make the name feel credible after discovery, that’s a different strategy.
I’ll take a look at the links.