we regularly see our customers land 7/8-figure BYOC deals
early founders don’t realize: enterprises that need BYOC don’t have as many vendors to choose from
Security is always top of mind for large customers. So despite having our own cloud, we offer:
- customer-managed compute
- BYOC
- on-prem
- and all the different variations
We are in a golden age where, if you are good at systems and understanding, AI increases your abilities by an order of magnitude. But if you are not good at it, you just spin your wheels and end up nowhere helpful
our team works each week in two phases
weekdays
- iterative work
- in-person
- ship hourly
- tight feedback loops w/ customers
weekends
- deep work
- remote
- experiments and moonshots
- reflect and re-strategize
curious how other teams do it
i’ve seen founders lose BYOC deals with reasons like:
“they only want air-gapped”
“they want to change our architecture”
both issues stem from:
- fear to push back on IT
- not educating customers on BYOC
- and honestly, sometimes bad architecture
we’ve done BYOC in the F100, Big 4, Global 2000, finserv, healthcare, space, and more.
BYOC lets you stay focused on product not deployment support.
supporting self-hosted will slow you down.
push back.
you won’t lose the deal.
they’ll come around.
@TheEthanDing they’ll start treating it like cloud spend. it’s easy to just run random experiments or forget to delete automations. then you end up with a huge bill.
difference here is maybe you can throw more ai at the problem to do cleanup and too analysis to curb bad behavior lol
also applies to AI startups if you want to win the enterprise.
don’t lock in to one cloud provider.
enterprises have a preferred cloud and it could be aws, gcp, or azure.
we built @ryvnai to abstract and simplify deployment over all 3. and on-prem.
OpenAI lost the enterprise war before it even started.
Committing to Azure was the biggest strategic mistake in AI history.
While OpenAI locked itself into one cloud, Anthropic ran natively on AWS, Google Cloud, and Azure simultaneously.
Enterprises don't switch clouds for a better model.
They pick the model that fits their infrastructure.
Distribution beat intelligence.
It always does.
go talk to an operator at an enterprise.
you’ll quickly realize, their problems are magnitudes more complex.
we’ll see 100x more FDEs before we see less.
OpenAI and Anthropic are effectively telling the market they can't solve every problem with a generic AI coworker.
You don't pour billions into massive forward-deployed joint ventures if you think the next model release is going to take care of it.
In the cloud supercycle, semis led and software followed (and you didn't need Qualcomm or ARM to tell you the value was migrating up the stack).
In AI, the infra layer itself is telling us the application layer is a separate, massive opportunity they can't fully capture.
a16z's @joeschmidtiv on why the app layer isn't dead: https://t.co/84QN5Mj9T3