Do you have something people can actually try? If not, Show HN is not appropriate, please see the first sentence here
You’re right that HN expects something runnable. We’re spinning up a public endpoint so people can test with their own models directly instead of requesting access. I’ll share it shortly. Thank you for the suggestion.
In other words, how many middlemen do you think you TAM is?
You go on to say this is great for light workloads, because obviously at scale we run models very differently.
So who is this for in the end?
This isn’t for single-model apps running steady traffic at high utilization. If you’re saturating GPUs 24/7, you’ll architect very differently.
This is for teams that…
• Serve many models with uneven traffic • Run per-customer fine-tunes • Offer model marketplaces • Do evaluation / experimentation at scale • Have spiky workloads • Don’t want idle GPU burn between requests
A lot of SaaS AI products fall into that category. They aren’t OpenAI-scale. They’re running dozens of models with unpredictable demand.
Lambda exists because not every workload is steady state. Same idea here.
How do you know this? What are the numbers like?
> Lambda exists because not every workload is steady state
Vertex AI has all these models via API or hosting the same way. Same features already available with my current cloud provider. (traffic scaling, fine-tunes,all of the frontier and leading oss models)
What we focus on is the runtime layer underneath. You can run us behind Cloud Run or inside your existing GCP setup. The difference is at the GPU utilization level when you’re serving many models with uneven demand.
If your workload is steady and high volume on a small set of models, the standard cloud stack works well. If you’re juggling dozens of models with spiky traffic, the economics start to look very different.
As an example, we’re currently being tested inside GCP environments. Some teams are experimenting with running us behind their existing Google Cloud setup rather than replacing it. The idea isn’t to swap out Cloud Run or Vertex, but to improve the runtime efficiency underneath when serving multiple models with uneven demand.
I don't see anything you do that they don't already do for me. I suggest you do a deep dive on their offering as there seem to be gaps in your understanding of what features they have
> economics start to look very different
You need to put numbers to this, comparing against API calls at per-token pricing is a required comparison imo, because that is the more popular alternative to model hot-swapping for spikey or heterogeneous workloads
Really, it looks like someone who is new to startups / b2b copy, welcome to first contact with users. Time to iterate or pivot
I would focus on design, aesthetics, and copy. Don't put any more effort into building until you have a message that resonates
If that’s not a problem space you care about, that’s totally fair. But for teams juggling many models with uneven traffic, that’s where the economics start to matter.
Are you going to make this open source? That's the modus operandi around Ai and gaining adoption for those outside Big Ai (where branding is already strong)
there is nothing to try or play with, it's just content