RunPod vs Vast.ai: which GPU cloud for your LLMs?

Both let you rent GPUs by the second without owning hardware — but they solve the problem in opposite ways, and that single difference cascades into pricing, reliability, security and workflow. RunPod operates its own (and vetted partner) data-center capacity as a managed product. Vast.ai is a marketplace: independent hosts — from professional data centers to someone's spare mining rig — list GPUs and compete on price. Cheaper, yes, with exactly the variability you'd expect.

Full comparison

The RunPod workflow

Pick a GPU, pick a Docker image (community templates exist for Ollama, vLLM, ComfyUI, axolotl fine-tuning and most common stacks), and a pod is running in about a minute. Three platform features matter beyond the basics:

• Two capacity tiers. Community Cloud (partner hosts, cheaper) vs Secure Cloud (vetted data centers, pricier) — anything sensitive belongs on Secure Cloud, full stop.
• Network volumes. Persistent storage that survives pod termination and attaches to a new pod — so your 40GB model downloads once, not every session. This alone changes the economics of repeated work.
• Serverless endpoints. Autoscaling GPU workers billed only while processing, including scale-to-zero. The catch is cold starts: loading a big model into VRAM takes tens of seconds, so idle-to-first-token latency can be painful. Mitigations: keep min-workers=1 during business hours (a hybrid of serverless and always-on), use smaller models, or bake weights into the image. Serverless wins for spiky/low-volume traffic; a steadily busy endpoint is cheaper as a plain pod — the same utilization logic as our cost guide, one level up.

The Vast.ai workflow — and how to read a listing

Vast.ai is a search interface over thousands of host offers. The price sorting is seductive; the skill is reading everything else. Before renting, check:

• Reliability score — the host's historical uptime. Below ~99%, expect what the number says.
• DLPerf — Vast's throughput benchmark; two "RTX 4090" offers can differ meaningfully due to cooling, power limits or PCIe wiring.
• Internet up/down — a 40GB model on a 100 Mbit host line = an hour of billed download before you start. For short jobs, transfer speed dominates total cost.
• Disk speed — slow disks throttle model loading and checkpoint writes.
• On-demand vs interruptible — interruptible offers are the deepest discounts but can be reclaimed when someone bids higher; only take them for checkpointed work.

The meta-rule: on a marketplace, you are the quality-control department. Factor five minutes of listing-reading into every rental, and re-verify a host before long runs.

Data logistics (the hidden time cost)

Neither platform charges meaningful egress, but moving tens of GB around still costs billed minutes and patience. Habits that pay: download models from Hugging Face inside the datacenter (their peering is usually excellent) rather than uploading from home; on RunPod, keep a network volume as your model cache; on Vast.ai, prefer hosts with fast links and script your environment setup (a provisioning script or custom Docker image) so a reclaimed instance can be rebuilt in minutes; push checkpoints to external storage (HF, S3, B2) on a schedule — treat every marketplace instance as disposable.

A worked example: 20-hour fine-tune on an 80GB GPU

Illustrative math, not a price sheet. Say an A100/H100-class GPU lists at ~€1.5–2.5/h managed and ~€0.8–1.5/h on the marketplace (interruptible lower still). The 20-hour job: RunPod Secure ≈ €30–50, hands-off; Vast.ai on-demand ≈ €16–30; Vast.ai interruptible ≈ €10–20 + checkpoint engineering + restart risk. If your time is worth anything and the run must finish tonight, the managed premium is cheap insurance; if you're running twenty experiments this month, the marketplace discount compounds into real money. That's the whole decision, in one example.

Security, honestly

On a marketplace, the host has physical control of the machine: root on the hypervisor, access to disks and RAM. Encryption at rest helps little against someone who owns the box while it runs. Consequence: Vast.ai is fine for public/open-weight models, public datasets and synthetic data — and inappropriate for customer data, proprietary code or anything GDPR-sensitive, whatever your contract says. For sensitive work in the cloud, RunPod Secure Cloud (vetted DCs, SOC-type controls) is the floor; for regulated data, the honest answer is often on-premise — see the on-prem-vs-cloud guide.

When neither is the answer

Quick map of the neighbors: hyperscalers (AWS/GCP/Azure) when you need enterprise contracts, VPC integration and compliance paperwork more than low prices; Lambda/CoreWeave-class AI clouds for reserved multi-GPU training clusters; Modal/Replicate when you want to deploy code/models, not manage machines at all; a token API when you don't actually need your own model running (see the cost guide); and your own hardware once utilization is provably high. RunPod and Vast.ai own the middle: real machines, per-second billing, no procurement cycle.

Decision guide

• Serving an LLM to real users → RunPod (serverless for spiky traffic, pod for steady; Secure Cloud if sensitive).
• Fine-tuning / training run → Vast.ai if you checkpoint; RunPod if it must just finish unattended.
• Cheap experimentation / benchmarking → Vast.ai, reading listings carefully.
• Sensitive or regulated data → RunPod Secure Cloud, or go on-premise.
• Trying a GPU before buying it → either; an afternoon of rental beats a wrong €2,000 purchase (see the GPU guide).