Vercel vs Qovery for AI Infrastructure: Which One Should Run Your Models in 2026?

I have had the same conversation with a dozen CTOs this year. They shipped an AI feature fast, usually a Next.js chat UI streaming tokens from a hosted model, and it worked beautifully. Then a real requirement showed up: a fine-tuned open-weight model, a 40-minute agent run, or a compliance clause that says prompts cannot leave the company's own cloud. That is the week they discover their frontend platform was never meant to hold that part of the stack.

So the honest answer to "Vercel or Qovery for AI" is that they sit at different layers. Vercel hosts the AI-powered frontend and short-lived serverless functions on infrastructure Vercel owns. Qovery deploys backends, GPU inference, agents, queues, and databases inside your own AWS, GCP, Azure, Scaleway, or existing Kubernetes cluster. Most serious AI products in 2026 use both.

Key takeaways

• Vercel and Qovery solve different layers. Vercel hosts the AI-powered frontend plus serverless and edge functions on infrastructure it owns and bills. Qovery deploys backends, GPU inference services, agents, queues, and databases inside your own cloud account (bring-your-own-cloud).
• Vercel has no GPU compute and no long-lived containers. Vercel Functions cap out at a 800-second maximum on Pro and Enterprise, with a 1800-second (30-minute) extended maximum in beta. A workload that needs a GPU, runs longer than that, or must stay in your VPC belongs on Qovery or raw Kubernetes.
• If you are shipping a Next.js app that streams tokens from OpenAI, Anthropic, or Amazon Bedrock, Vercel is the fastest path to production, and moving it to Kubernetes would be a self-inflicted wound.
• The cost models differ structurally. Vercel bills per seat, invocation, active CPU, and bandwidth on hardware it owns (Vercel pricing). Qovery charges a platform fee on top of your own cloud bill, so AWS Savings Plans (up to 72% off) and GCP Spot capacity (up to 91% off) stay in your name, which matters most for GPU hours.
• The most common 2026 architecture is both: Vercel for the app shell, Qovery for anything that needs a GPU, a persistent process, or private networking. Decide per workload, not per vendor.

Vercel vs Qovery for AI infrastructure: what is the short answer?

Vercel is a frontend and serverless hosting platform running on infrastructure Vercel owns, best for AI-powered UIs that call hosted model APIs. Qovery is an internal developer platform that deploys containers and Kubernetes workloads inside your own cloud account, best for GPU inference, long-running agents, and private data.

Vercel is Next.js-native. You get serverless and edge functions, the AI SDK and AI Gateway for calling models across providers, fluid compute, and a global CDN. All of it runs on hardware Vercel operates and invoices.

Qovery is a bring-your-own-cloud platform on AWS, GCP, Azure, Scaleway, or your own existing Kubernetes cluster. It gives you git-push deploys, a preview environment per pull request, environment auto-stop for non-production, managed cluster upgrades, per-environment RBAC, and databases backed by managed cloud services. Your apps and data stay in an account you own.

The decision rule fits on one line: model calls from a UI go to Vercel; models, GPUs, agents, vector databases, queues, and regulated data go to Qovery. And to be clear about the edges, Vercel is not a place to run a GPU, and Qovery is not a CDN and does not do Next.js edge rendering.

What do AI workloads actually need from infrastructure in 2026?

"AI workload" is not one workload. It splits into five infrastructure profiles, and no single platform is best at all five. Match the profile to the layer before you argue about vendors.

The context is that this is now mainstream spend, not a science project. Menlo Ventures put enterprise generative AI spend at $37 billion in 2025, up from $11.5 billion in 2024. And Kubernetes has become the default substrate underneath it: the 2025 CNCF Annual Survey found 82% of container users run Kubernetes in production, and 66% of organizations hosting generative AI models use Kubernetes for at least some inference.

The five profiles break down like this:

• Streaming UI: the chat or copilot surface. Needs edge latency, response streaming, fast iteration, preview deployments.
• API orchestration: code that calls OpenAI, Anthropic, or Bedrock. Needs concurrency, retries, and request durations that grow once you chain or fan out calls.
• Self-hosted inference: vLLM, Ollama, TGI, or a custom model. Needs GPU node pools, container images in the tens of GB, warm capacity, and autoscaling on GPU metrics.
• Long-running agents and batch: agent loops, background workers, fine-tuning, pipelines. Needs persistent processes, queues, and runtimes measured in hours.
• Data layer: Postgres with pgvector, Qdrant, Weaviate, Milvus, Redis, object storage. Needs volumes, backups, and private networking.

Then there is compliance, which cuts across all five. Healthcare, fintech, public sector, and EU data-residency teams often cannot send prompts or embeddings outside their own VPC. That single constraint rules out vendor-owned compute no matter how good the developer experience is, and it is a big part of why open-weight and self-hosted models still hold a real share of enterprise usage.

Can you run GPU inference or self-hosted LLMs on Vercel?

No. Vercel's compute offering does not include GPU instances or long-lived containers, so self-hosted model inference has to run elsewhere: GPU instances in your own cloud, a GPU-specialist provider like Modal or RunPod, or a Kubernetes cluster. Vercel's documented path for AI is to call model providers through its AI SDK and AI Gateway, which is the right answer for most apps.

What Vercel genuinely offers is good. The AI Gateway routes requests across providers with failover and observability, and adds zero markup to provider token prices. Functions stream responses cleanly. But there are hard limits for anything heavier: a Vercel Function bundle is capped at 250 MB uncompressed (500 MB for Python), with a large-functions beta reaching 5 GB. A 15 to 30 GB model image does not fit, there is no persistent GPU memory between invocations, and there is no GPU at all.

Self-hosted GPU inference has real requirements: GPU node pools on EKS, GKE, or AKS, the NVIDIA device plugin, node autoscaling, model-weight caching on volumes or object storage, and headroom against per-region GPU quotas that new cloud accounts almost never have by default. This is exactly the layer Qovery targets. Qovery deploys your container, whether that is vLLM, Ollama, or a FastAPI inference service, onto node pools in your own cluster, including GPU node pools you configure in your cloud account, with git-push deploys, per-environment RBAC, managed cluster upgrades, and auto-stop for idle non-production environments.

Be fair about the crossover. Calling a hosted model API from Vercel is cheaper and simpler until you have sustained throughput, fine-tuned or open-weight models, or data-residency constraints. I show that arithmetic in the cost section below. And for pure GPU jobs with no app around them, a specialist like Modal, RunPod, SageMaker, or Cloud Run GPU can beat both Vercel and Qovery.

How do Vercel and Qovery compare on cost for AI workloads?

The structural difference outweighs any per-unit price. Vercel resells compute it owns and bills per seat, invocation, duration, and bandwidth, and GPU hours never enter the equation. Qovery leaves the cloud bill in your name, so reservations, Savings Plans, committed-use discounts, and spot GPU capacity still apply. For AI budgets, GPU hours are the line item that decides everything.

On the Vercel side, Pro is $20 per seat per month, with Fluid active CPU from $0.128/hour, provisioned memory from $0.0106/GB-hour, 1 TB of fast data transfer included then $0.15/GB, and function invocations at $0.60 per million. That model is clean and predictable for a frontend. It simply has no concept of a GPU hour.

On the Qovery side, you pay a platform fee (Business starts at $2,999/month billed yearly, including 20 users and up to 3 managed clusters) on top of your own cloud spend. The cloud bill stays yours, which is the whole point once GPUs are involved. An AWS g5.xlarge is about $1.01/hour on-demand and a p5.48xlarge with H100s is about $55/hour; a GCP a3-highgpu-8g with eight H100s is roughly $88/hour on-demand. A vendor-owned platform cannot pass through the discounts that tame those numbers, but on your own account AWS Savings Plans reach up to 72% and GCP Spot reaches up to 91% off on-demand.

Here is the crossover arithmetic, and I am labeling the assumption clearly. Take one AWS g5.xlarge (a single A10G GPU) at about $1.01/hour on-demand, which is roughly $737/month run continuously. Assume a sustained 1,000 output tokens per second with vLLM continuous batching. That is a deliberately conservative floor, since vLLM's v0.6.0 release alone delivered a 2.7x throughput improvement and published benchmarks show a single H100 serving Llama 8B well into the thousands of tokens per second. At 1,000 tokens per second, that instance produces about 2.6 billion tokens per month at full tilt, which pencils out to roughly $0.28 per million output tokens. Compare that to a hosted API: Claude Haiku 4.5 is $1 per million input tokens and $5 per million output tokens. The break-even against Haiku's output rate is about 147 million output tokens per month ($737 divided by $5, times a million). Above that sustained volume, self-hosting an open-weight model on your own GPU wins on price. Below it, the hosted API is cheaper and you have no cluster to run.

Two honest caveats. First, that comparison holds cost constant, not quality: an open 8B model is not Claude Haiku, so treat the math as structural, not a like-for-like swap. Second, below real scale Vercel wins on total cost of ownership, because a platform fee plus a Kubernetes control plane is real money. EKS charges $0.10 per cluster per hour, about $73/month, and GKE charges the same $0.10 per cluster per hour, before you have run a single pod.

When is Vercel the better choice for an AI product?

If your AI product is a web app calling third-party model APIs, Vercel is the fastest path to production, and replacing it with Kubernetes would be a mistake. Choose Vercel when latency to the user and time to first deploy matter more than unit economics or data locality.

It is the right call for Next.js and React workflows, preview deployments, a global edge network, and streaming UI patterns through the AI SDK and AI Gateway. It fits teams with no platform engineer and no regulatory requirement to keep prompts or embeddings in-house. And it is ideal for prototypes, demos, and design-partner builds where shipping this week beats optimizing next quarter.

The wall appears at specific, documented places: the 800-second function maximum on long agent runs, no GPU compute, no native private VPC peering to your own data stores, and bandwidth plus invocation costs that scale with usage on hardware you do not control. When you hit those, keeping the frontend on Vercel while moving the backend into your own cloud is a legitimate and common end state, not a failure.

If you are weighing near neighbours for this profile, Netlify, Cloudflare Workers, and Railway cover similar ground for parts of it. The point stands: for the app shell, a frontend-native platform is usually the right tool.

When is Qovery the better choice for AI infrastructure?

Qovery wins when the AI workload has to run inside your own cloud account, whether that is GPUs, persistent processes, private data, or a regulated environment, and you still want Heroku-style developer experience instead of hand-written Terraform, Helm charts, and a platform team you have not hired yet.

Bring-your-own-cloud is the core of it. Your apps run in your own AWS, GCP, Azure, or Scaleway account, or on an existing Kubernetes cluster you already operate, so the cloud bill, the discounts, and the data all stay in your name. The concrete AI use cases are the ones that break serverless: vLLM or Ollama inference services, LangChain and agent workers, embedding pipelines, pgvector or Qdrant next to private data, GPU batch jobs, and RAG backends that need to sit inside the VPC.

The developer experience is what protects velocity. You get git-push deploys, a preview environment per pull request (so a prompt or model change gets its own throwaway stack), environment auto-stop for non-production, managed cluster upgrades, per-environment RBAC, and databases backed by managed cloud services. Multi-cloud is a practical advantage here rather than a slogan, because GPU availability and quotas differ by provider and region, so being able to place a workload where the capacity actually exists is a real edge.

I will name the limits too. Qovery is not a CDN and does not run Next.js edge rendering, so if you need edge rendering or global static delivery, keep that on Vercel. And raw Kubernetes or a rival platform can still win for mature platform teams with bespoke scheduling needs, or for teams comparing Northflank, Porter, and Render for a narrower scope. Given that the 2025 Stack Overflow Developer Survey puts the US median DevOps engineer salary at $165,000, the build-it-yourself path is rarely as cheap as it looks.

Can you use Vercel and Qovery together in the same AI stack?

Yes, and it is the most common architecture I see in 2026. Vercel serves the frontend and thin API routes, Qovery runs inference, agents, workers, and the data layer inside your own cloud account, and the two connect over authenticated HTTPS or private networking. Nothing about them conflicts.

The reference architecture is straightforward. A Next.js app on Vercel handles the UI and streaming. An authenticated API route forwards the request to Qovery-deployed FastAPI, vLLM, or agent services running on your own cluster. Those services talk to pgvector or Qdrant plus object storage inside your VPC. Model keys and secrets live in your backend, and any prompt or embedding that touches regulated data terminates inside your own VPC rather than transiting a vendor's compute.

Preview environments pair nicely too. A Vercel preview for the UI runs against a Qovery preview environment with the backend and a seeded vector database, and both tear down when the pull request closes. For teams outgrowing serverless, this is also the migration path: move the GPU and long-running parts first, keep the frontend where it is, and avoid a big-bang rewrite.

One thing to watch is egress. When the frontend and backend live in different clouds, cross-cloud traffic is billed as internet egress, around $0.09/GB out of AWS and from $0.12/GB out of GCP. Keep the chatty, high-volume traffic (embeddings, model I/O) inside one cloud and send only the thin request/response hop across the boundary, and that cost stays negligible.

How do you decide between Vercel and Qovery? A 7-question checklist

Run these seven questions in order. The first yes decides the layer, and each maps mechanically to Vercel, Qovery, or both.

The honest position is that Qovery is the answer for the half of the AI stack that has to live in your own cloud, and running Vercel for the other half is good architecture, not a compromise. Decide per workload, and you will usually end up running both.