Why every Ellelion product runs on Vercel
July 23, 2026 · 5 min read · By @ici_dab
I run all of Ellelion on Vercel because one person can't babysit servers and still ship features. A git push gives me a preview URL to click through and a one-click rollback if I break production. Fluid Compute keeps functions warm and cheap, and the AI SDK, AI Gateway, and eve framework are already there. Skillselion runs on it now. GrowSocialelion is next.
One builder, nine products, one platform
I build Ellelion on my own. Nine AI agents, and I'm the whole team. When you're the only engineer, the platform underneath you can't need babysitting. It takes the code, puts it online, keeps it there, and stays out of the way, or it eats the hours you needed for the actual product. I picked Vercel for the whole portfolio: for my own day-to-day, for Skillselion, which is live today, and for GrowSocialelion, which ships next.
Fluid Compute does the server work I'd rather not
Vercel runs on Fluid Compute by default, and it changed how my functions behave. Instead of cold-starting a fresh instance for every request, it reuses warm ones across concurrent traffic, so the cold starts and the bill both drop. Functions run full Node.js, middleware included, and the default timeout is now 300 seconds. My products call a model and then sit there waiting on the answer. Warm instances and a generous timeout help that a lot more than anything I'd hand-tune.
Preview deploys and one-click rollback are my whole QA team
Every push gets its own preview URL. I open it, click through the change on a real deployment, and promote to production once it holds up. When I break prod, and I do, rollback is one click instead of a fresh build and a cold sweat. There's no QA team here. This preview-and-promote loop, wired straight to git, is the closest thing I've got, and most days it's enough to let me ship without flinching.
The AI stack is already in the box
Ellelion is agent-first. Every product ships an MCP server so a coding agent can drive it, so I need a host that treats AI as part of the runtime rather than a plugin I bolt on. Vercel does. The AI SDK builds the features, the AI Gateway routes across model providers behind one key with automatic fallbacks (it went GA in August 2025), and Sandbox runs generated or untrusted code in isolation (GA since January 2026). There's even a Vercel MCP server, so my own agents can read logs and touch deployments without me sitting in the loop.
The Gateway earns its place. Vercel says it reaches 200+ models from every major provider through a single endpoint, 100+ of them at no markup. If a provider goes down, one line of config fails over to another. Wiring five provider SDKs across nine products is the kind of busywork I'm trying to design out of my life, and here I don't have to touch it.

GrowSocialelion is built on eve
My next product, GrowSocialelion, runs on eve, Vercel's framework for agents. Vercel calls it like Next.js for web apps, but for agents, and after building on it for a few weeks I'd say that lands. Instructions live in Markdown, tools in TypeScript, and the runtime is durable: workflows survive a crash, every step is checkpointed, and an agent parks itself while it waits and wakes up on the next message. Anything that needs a human to approve it stops at a gate first. Schedules cover the recurring jobs, connections cover the authenticated services. I'm building an agent that acts on someone's behalf, and eve is shaped for that from the first line.

The economics work for one person
Vercel Functions bill on Active CPU. You pay for CPU time while your code is running, plus a little provisioned memory and the invocation, and nothing for the wall-clock minutes a function spends waiting on a model or a database. That memory sits under 10 percent of the active-CPU rate, so an agent parked on a slow LLM call barely shows up on the bill. Most of my traffic is that shape: a call fires, waits, comes back. Paying for the work and not the waiting is what keeps nine products on one budget from sinking me.

The numbers back that up, and they're Vercel's own. It reports that Fluid Compute trims costs by up to 85 percent through in-function concurrency, that Active CPU adds up to 90 percent more on idle-heavy work like AI inference, and that together they run over 45 billion requests a week while saving customers up to 95 percent. Active CPU starts at 0.128 dollars an hour. Take vendor percentages with the usual pinch of salt, but they match what shows up on my invoice.
I make the same call every time
I don't shop for a new platform with each product. Skillselion is on Vercel. GrowSocialelion is going up on it now. The other seven will land the same way. One deploy flow, one preview loop, one set of tools I already know in my hands. Every hour I don't spend relearning my own infrastructure is an hour I get to spend on the part people pay for.
Sources
- Vercel: eve, the framework for agents
- Vercel Docs: Fluid Compute
- Vercel Blog: Introducing Active CPU pricing for Fluid Compute (85% / 90% / 95% savings, 45B weekly requests, $0.128/hr)
- Vercel Docs: AI Gateway
- Vercel: AI Gateway models (200+ models, 100+ with no markup)
- Vercel Docs: Sandbox
- Vercel Docs: Functions and Active CPU pricing
Common questions
- Is Ellelion affiliated with or sponsored by Vercel?
- No. Ellelion is an independent company (Ellelion LLC, Wyoming, USA) and pays for Vercel like any other customer. This post is my own opinion on the tools I chose, not a paid placement.
- Does every Ellelion product run on Vercel?
- Yes. Skillselion is live on Vercel today, GrowSocialelion is being built on it next, and the rest of the nine-product portfolio deploys the same way.
- What is eve?
- eve is Vercel's framework for building AI agents: filesystem-first, with Markdown instructions and TypeScript tools, durable execution that survives crashes, human-in-the-loop approval gates, schedules, and connections. Vercel positions it as like Next.js for web apps, but for agents. GrowSocialelion is built on it.
- What is Fluid Compute?
- Fluid Compute is Vercel's default compute model. It reuses function instances across concurrent requests to cut cold starts, runs full Node.js including in middleware, and has a 300-second default timeout, which suits AI workloads that wait on model responses.
Verified on July 23, 2026

