I migliori provider di GPU cloud per l'IA nel 2026: RunPod, Lambda, Vast, Together, Replicate

Local AI hardware has limits. A 70B model needs 32 GB+ of VRAM, a 405B model needs 250 GB+, and fine-tuning anything serious takes hours to days of pegged GPU time. For most serious AI work in 2026, the answer is rent the GPU, not own it.

The cloud GPU market has matured into roughly five providers worth knowing. Here’s the honest 2026 breakdown of which one to pick for which use case.

At a glance — H100 80 GB pricing (Q2 2026)

The big retail clouds (AWS, GCP, Azure) cost roughly 5-8× more than the AI-specialty clouds. Don’t use them for development unless your enterprise has credits or compliance requirements.

1. RunPod — best overall for developers

What it is: AI-native cloud with on-demand and serverless GPU options.

Strengths:

• Spin up an H100 pod in 30 seconds
• Persistent volume storage included (useful for model caches)
• Jupyter + SSH out of the box
• Templates for ComfyUI, vLLM, Stable Diffusion, etc.
• Both Secure Cloud (enterprise data centers) and Community Cloud (cheaper, slightly less reliable)

Weaknesses:

• Community Cloud quality varies (slow nodes occasionally)
• No SLA on Community Cloud
• Region availability uneven

Use it for: Development, fine-tuning sessions, prototyping, batch image generation.

Pricing: H100 $1.89/hr Secure, $0.99/hr Community. A100 80 GB $1.19/hr Secure. RTX 4090 $0.34/hr.

2. Lambda Labs — best for reliability + clusters

What it is: AI-focused cloud with strong enterprise pedigree (used to sell hardware).

Strengths:

• Per-minute billing (vs per-hour at AWS)
• 1-Click Clusters (multi-GPU spin-up)
• Strong reliability — feels closest to AWS quality
• Good for training runs that need to actually finish
• Reserved instance pricing (~50% off if you commit)

Weaknesses:

• Capacity is often constrained — H100s are not always available on demand
• No serverless / inference-as-a-service path
• UI is utilitarian

Use it for: Training jobs you want to actually complete, multi-day fine-tunes, anything where you can’t tolerate a node dying mid-run.

Pricing: H100 $1.99/hr, A100 80 GB $1.29/hr, H200 $2.49/hr.

3. Vast.ai — the marketplace bargain

What it is: A peer-to-peer marketplace — anyone with spare GPUs can list them, anyone can rent.

Strengths:

• Cheapest in the market (often 30-50% below RunPod)
• Massive variety (consumer GPUs, server GPUs, exotic configs)
• Per-minute billing
• Bid-and-ask system can save more

Weaknesses:

• Quality varies wildly by provider
• Some hosts have spotty networks
• No SLA, no enterprise support
• “Interruptible” instances can disappear

Use it for: Cost-sensitive workloads where some failures are OK, big batch jobs, learning + experimentation.

Pricing: H100 from $1.30/hr (varies). RTX 4090 from $0.25/hr.

4. Together AI — inference as a service

What it is: Managed inference for popular open-weight models. You don’t rent a GPU — you call an API.

Strengths:

• No infra management — just hit the API
• Cheap per-token pricing (e.g., Llama 3 70B at $0.65/M output tokens)
• Sub-200ms latency for most models
• 100+ models available
• Fine-tuning API also available

Weaknesses:

• You’re locked to their model list
• Less control over inference parameters
• Costs more per hour if you’re 100% utilizing
• Not for training from scratch

Use it for: Production inference at scale, when you don’t want to manage servers.

Pricing: Per-million-tokens. Llama 3 70B Instruct: $0.65/M output, $0.88/M input.

5. Replicate — one-shot model runs

What it is: Run any model from a curated catalog with a single API call. Pay only for the seconds the model runs.

Strengths:

• Easiest possible UX — copy a 5-line code snippet, done
• Huge model catalog (Stable Diffusion variants, FLUX, audio models, video, etc.)
• Per-second billing — pay only for actual inference
• Great for prototyping

Weaknesses:

• More expensive per-call than RunPod
• Cold start latency (5-30 seconds first call)
• Less control

Use it for: Prototypes, one-off image/audio generation, integrating AI into existing apps without infra.

Pricing: ~$0.001-0.01 per generation depending on model.

Practical recommendation by workload

• Fine-tuning Llama 3 70B for a few hours: RunPod Secure Cloud H100. Spin up, run, tear down.
• Multi-day training run: Lambda Labs reserved H100 cluster.
• Stable Diffusion at scale: Replicate (easiest) or RunPod (cheaper, more control).
• Running Llama 3 70B chat for an app: Together AI API. Don’t manage servers.
• Experimentation on a tight budget: Vast.ai. Just be ready for variability.
• Enterprise compliance / your-cloud-only: AWS / GCP / Azure (with SOC 2 receipts).

Pros and cons

The hidden costs that wreck a cheap hourly rate

The advertised per-hour GPU price is only part of what you pay. Two providers can quote the same H100 rate and bill you wildly differently once data movement, storage, and interruptions are counted. Before you commit a workload, run it past four line items that rarely appear in the headline number.

Egress (data transfer out). This is the single biggest gotcha on hyperscalers. AWS charges roughly $0.09/GB to move data out to the internet, Azure about $0.087/GB, and Google Cloud around $0.12/GB (each after a small free tier). Pulling a 5 TB dataset or set of checkpoints back out can quietly add hundreds of dollars. Specialist GPU clouds like RunPod, Lambda, and Vast.ai typically charge nothing for ingress or egress, which is a real reason they beat a hyperscaler on total cost even when the raw GPU rate looks similar.

Idle storage. A persistent network volume keeps billing while your pod is stopped, usually around $0.07/GB per month. Leave a few hundred gigabytes of model weights parked between runs and you pay for compute you never touch. If you only spin up occasionally, it is often cheaper to delete the volume and re-pull weights from Hugging Face on startup.

Cold-start and serverless overhead. Serverless GPUs eliminate idle cost but the meter starts at container launch, so you pay for model loading and initialization, not just inference. For large models this preparation phase can add a meaningful slice on top of compute time. Serverless wins for spiky, low-duty-cycle traffic; a dedicated pod is cheaper once utilization is high.

Spot vs on-demand. Spot or “community” instances cut the rate by roughly 40-65%, but they can be reclaimed mid-job. High-end GPUs see the highest interruption rates, and warning windows are short — AWS gives about two minutes, Google as little as 30 seconds. The rule of thumb:

• Use spot for checkpointed training, hyperparameter sweeps, and batch/offline inference that can resume.
• Use on-demand or reserved for production serving, demos, and anything latency-sensitive where an interruption is unacceptable.

The honest takeaway: estimate your data-out volume and storage footprint first, then compare providers on the total bill — not the sticker rate.

Domande frequenti

Is it cheaper to rent an H100 or buy a 4090?

For occasional use (under 200 hours/year), renting wins. RunPod H100 at $1.89/hr × 200 hours = $378/year. A 4090 costs ~$1,400. Break-even for renting H100 vs buying 4090: roughly 750 hours/year of pegged use. Most personal AI users are nowhere near that.

Why is Vast.ai cheaper than RunPod?

Vast.ai is a marketplace — many GPUs are hosted on consumer connections in datacenters or even home labs, with no SLA. RunPod’s Secure Cloud is enterprise infrastructure. You pay for reliability and predictable performance.

Can I run training on Together AI?

Together offers a fine-tuning API for specific models (Llama 3 8B, 70B, etc.) but you can’t run arbitrary training jobs. For arbitrary training, rent a GPU (RunPod / Lambda) instead.

What about Modal, Beam, and other newer providers?

Modal is excellent for serverless AI (auto-scale to zero) — great for sporadic workloads. Beam is similar. Both charge per-second and shine for intermittent inference workloads. For sustained training, the GPU-rental clouds (RunPod / Lambda / Vast) are cheaper.

Do I need a paid cloud GPU for serious AI work in 2026?

Depends on workload. If you have a local 4090 or 5090, you can do 90% of practical AI work locally. Cloud is for: 70B+ training, jobs that take >24 hours, jobs requiring multiple GPUs, or production inference at scale. For most learners and hobbyists, local hardware + occasional cloud bursts is the right pattern.

Are there free GPU credits anywhere in 2026?

Google Colab Free tier still works (limited T4 / L4 access). Kaggle gives 30 GPU hours/week of T4. Lambda gives $100 credits to new accounts. RunPod occasionally runs promotions. None of these are enough for serious work but they’re good for learning.

What hidden fees should I watch for when renting a cloud GPU?

The big three are egress (data transfer out), idle storage, and minimum or cold-start charges. Hyperscalers like AWS, Azure, and GCP charge roughly $0.087-$0.12 per GB to move data off their network, which can dwarf the GPU cost on data-heavy jobs. Persistent storage usually keeps billing (about $0.07/GB per month) even while your instance is stopped. Specialist GPU clouds typically waive egress entirely, so always compare the total bill, not just the hourly rate.

Should I use spot or on-demand GPUs?

Use spot (or “community”/preemptible) instances for work that can checkpoint and resume — model training, hyperparameter sweeps, and batch inference. You save roughly 40-65%, with the trade-off that the instance can be reclaimed on short notice (often a 30-second to two-minute warning, and high-end GPUs are reclaimed most often). For production serving, live demos, or anything latency-sensitive, pay for on-demand or reserved capacity; an interruption there costs you more than the savings.

Does egress pricing lock me into a provider?

It can. If your data and trained models live on a hyperscaler, the cost of moving terabytes out creates real friction against switching clouds — that is by design. To stay portable, keep your datasets and checkpoints on a provider with free egress (or in neutral object storage), and avoid letting large artifacts accumulate behind a paid transfer wall. Planning your storage location up front is far cheaper than paying to migrate later.

Conclusione

In 2026, the cloud GPU market has matured enough that you have real choices for real prices. RunPod is the right default for developers — cheap, fast, reliable enough. Lambda Labs if you need clusters or actual SLAs. Vast.ai if you’re hardcore about cost. Together AI / Replicate if you’d rather call an API than manage servers.

Don’t use AWS / GCP / Azure for AI dev work unless you have to. The 5-10× price multiplier doesn’t buy you anything you actually need.

The era of “you need to own GPU hardware to do AI” is over. The right pattern in 2026 is: own enough hardware for daily development, rent the rest when workloads exceed it.