Posted by hek2sch 3 hours ago
We started the company two and a half years ago, and raised $7.3m in 2024 (announced only almost a year later). We've spent less than half of this amount.
Earlier this week we came to the difficult decision to wind down the project. The open-source repository remains available on GitHub (Apache 2.0) but won't be actively maintained by the team moving forward.
Their website landing page is now also showing the software is no longer maintained. No mention of why they made this decision, my best guess is they burned through their seed money and were unable to attract further investments.
[0]: https://www.tensorzero.com/blog/tensorzero-raises-7-3m-seed-...
or you're incompetent
I’d bet on extreme irresponsibility.
(Honestly I don’t think so here, but I predict that will happen eventually)
The discussion here isn't about funding, it's that there's a presumptively useful community tool which got abandoned because its owners took their toys and went home when the money ran out (instead of making a sincere effort at transitioning to community governance). That's on the IP owners being selfish jerks and/or grifting losers. It's not the VC's fault.
There are good and bad ways to extract yourself from maintainership obligations. This is the bad way.
Also the whole project is open source. If you want, you could take it over.
Yes, that's exactly what it means!
"It's still open source because you can fork it if you really want" is a specious and unhelpful attitude, and it tells me that you, like the owners of this thing, are not to be trusted to manage such a thing.
The report says, the CEO and founder, is a Ketamine addicted weirdo, who does Nazi salutes in public, is know to have at least 24 kids, and lives in an isolated farm in Texas, with at least 5 to 7 female partners, and got sued for calling a guy who saved kids a Pedophile.
You in?
It was a simple project in terms of technical complexity. I didn't publish it as I counted several similar projects in the field.
Putting $7.3M into such a project would make sense only in the case of a precise growth plan with already declared customers and an promising sales funnel. There is no technical moat.
> It was a simple project in terms of technical complexity.
That’s the thing, though. The version I build for myself sheds all the features that get in my way. I don’t share them either because they’re only useful for me.
Perhaps in the future big tech projects will be delivered with a common “core” and the expectation that agents fill in the use-specific stuff.
I feel like this is really going to change the software industry moving forwards. Historically it was tedious and time consuming to actually develop tailored dev tools which is why so many organizations relied on third party solutions. When nowadays you can easily half bake something in a few hours and get it working, tailored _specifically_ to your needs.
I suspect so, the headless / "api/cli only" tools like CRM are pretty big right now and I don't think we've seen the end of that trend, probably more like just beginning.
"infra is safe" Hmm, but that wasn't a good idea. because if an open source infrastructure project like TensorZero gets shut down this quickly, won't they start to realize that those investment theories are also risky?
The difficult thing about AI infrastructure is that, unlike other industries, it will not become fragmented. It will likely remain tied to specific big tech models. What does this mean? It means that because AI models are not yet standardized, the infrastructure itself is actually riskier. In other words, the privatization of standards is happening.
The challenge with AI infrastructure is that an independent, stable standard layer has not formed, unlike in other software infrastructure markets such as databases, web servers, cloud, and containers. Over time, those ecosystems developed relatively standardized interfaces and operational layers. But the LLM ecosystem is still evolving rapidly. Models themselves change fast, APIs differ, pricing differs, context windows, tool calling, structured output, evaluation, fine tuning, caching, routing, everything keeps changing.
So even if an infrastructure startup tries to build a common abstraction layer across multiple models, before that common layer can stabilize, big model or cloud providers like OpenAI, Anthropic, Google, AWS, or Azure can just absorb the same functionality directly. In the end, AI infrastructure is at high risk of becoming an attached feature of model providers rather than solidifying as an independent layer.
But if a startup that raised 7.3 million dollars fails this quickly, who would trust and invest in such things? That aside, it seems AI startups are all the rage these days. I also want to learn AI and get funded like that. Does anyone here trust me enough to invest? About one hundredth of that would probably be enough
> VCs think, 'Apps are risky, infrastructure is safe,' so they invested in AI infra.
First off, this isn't even infra in the infra sense of the word. Infrastructure implied something physical, a pure software product can almost never be considered 'infra'. A tool maybe, but not 'infra'.
VCs can also be irrational and driven primarily by personal connections rather than reason. I didn't do a deep dive in this project/leadership, but often who you know is some important than what you produced. There's a reason why a lot of VCs go for the old motto of "I'd rather invest in an A team with a C product; than invest in a C team with an A product".
I agree that most people misunderstand the concept of a 'moat' and become obsessed with that misunderstanding. People tend to think that only technical 'coding skills' which they can easily understand constitute a moat. But in reality, the moat is the entire workflow across the product's lifecycle, including coing skills. In that sense, infrastructure workflows are nothing more than 'the most easily replaceable consumables.' The essential purpose of infrastructure is to pursue 'standardization,' which paradoxically means a state of 'zero switching costs' where customers (app developers) can switch at any time to a better API or a big tech built in feature. Pure technology that doesn't latch onto the messy real world domains of customers will inevitably be absorbed without resistance by massive capital.
In some ways, customer lock in at the application layer, or even the fan culture around a product, creates emotional lock in. The end user app that provides a specific workflow integrated into users' daily routines can overcome even technical inferiority through 'experience' and 'emotion.' Technology can be copied, but the user identity attached to a tool is what I think a real moat is.(That is also the reason I love Windows.)
The example you gave, Cursor's Composer, is exactly the case I'm talking about. I think Cursor is inferior, and I don't think its Composer model feature is all that great either. But Cursor has a passionate fan base, and users who choose Composer as the best value for money no longer care about absolute technical performance or benchmark scores. They are captivated by the 'speed of experience' of code being completed quickly as they intended, and the 'frictionless workflow' the tool provides.it's not the company that builds the best AI model that wins, but the company that wraps 'good enough technology' in 'great UX' and dominates users' habits. That is how apps dominate infrastructure, and that's the moat you and I are thinking about.
That said, this conclusion is probably too hasty and has many flaws. Still, your thoughts are so similar to mine that I'm leaving this reply. Thanks for the great comment. Have a good day
> are all the rage these days
Are they? Overall it seems kind of tame compared to 2020-21 since VCs are somewhat risk average outside of a few outliers. Funding looks much more concentrated these days.
“TensorZero is used by companies ranging from frontier AI startups to the Fortune 10 and fuels ~1% of global LLM API spend today.”
One percent seems like a lot. Anyone on HN use this?
Ultimately I found the data model and UI to be both cumbersome and unintuitive. Langfuse ended up being the observability tool I went with instead over the one I built (and still use today).