Vast.ai vs RunPod vs Lambda: best cloud GPU for LoRA and ComfyUI

Market checked 24 July 2026

This is a dated snapshot, not a permanent price table

Vast.ai is a marketplace, so hosts set prices and availability changes in real time. RunPod publishes Pod prices but the GPU you want may not be available in your preferred cloud or region. Lambda lists straightforward instance prices, while access is first-come and inventory can still be the real constraint.

Treat every dollar figure below as a comparison point captured on 24 July 2026. Open the provider's live console before starting a job. Check the exact GPU model, VRAM, disk, transfer rate, reliability or cloud tier, and whether stopping preserves data. A copied 2025 price table is worse than no table because it makes an expensive decision look exact.

Real Fake Girls has no affiliate relationship with Vast.ai, RunPod, or Lambda. There are no referral links here and no provider paid for placement.

Price the completed result

Calculate real job cost, not GPU-hours alone

A $0.35/hour marketplace GPU that spends 35 minutes downloading models, then dies before a checkpoint, can cost more than a $0.70/hour machine that finishes cleanly. Your time is part of the bill too. If an unfamiliar host needs an hour of debugging, the $0.20 you saved on compute is fiction.

• Setup: container boot, custom-node installation, package compilation, model downloads, and dataset extraction all happen while the meter may be running.
• Storage: checkpoints and models can outlive compute, but persistent disks keep billing until you delete them.
• Transfer: a 20GB checkpoint library changes the economics of moving to the lowest bid every session.
• Failure: interrupted instances are only cheap when the training script resumes from a recent checkpoint.
• Utilization: do preprocessing locally when possible. Do not rent an H100 while manually fixing captions in a notebook.

For a fair comparison, time one complete representative job on each provider. Record launch-to-first-step, training or generation time, upload and download time, failure rate, and the final bill. That small spreadsheet is more useful than anyone's blanket “cheapest cloud” claim.

The honest trade-off

Vast.ai vs RunPod vs Lambda at a glance

These products are not identical shelves of GPUs. Vast sells access to many independent hosts. RunPod spans Community and Secure Cloud plus Serverless products. Lambda presents a more conventional AI cloud. Reliability, compliance, and operational consistency are features you pay for, not footnotes to erase from the comparison.

Prices observed 24 July 2026

A useful snapshot of listed on-demand compute

The following examples come from the providers' public pricing pages. Taxes, exact region, secure tiers, host-specific storage and transfer, availability, and configuration can change the final price. Vast does not have one honest static number, so its live market stays “check console.”

The cheapest GPU in a VRAM class is not automatically the fastest. Architecture, tensor-core support, memory bandwidth, host CPU, RAM, disk throughput, and software compatibility all affect completion time. Benchmark your actual workflow; do not infer throughput from VRAM alone.

Rent enough memory, not bragging rights

Choose 24GB, 48GB, or 80GB by workload

Start from the model's current training or inference documentation, then add headroom for optimizer state, batch size, validation, caching, and other processes. A workflow that technically fits with every low-memory switch enabled may be slower and more fragile than moving up one class.

For LoRA specifically, rank and image count do not determine VRAM by themselves. Base-model size, what modules are trained, precision, optimizer, resolution, batch size, gradient checkpointing, and latent/text-embedding caching all matter. Use the guide for your exact trainer and model version.

Cheapest when you operate it well

Vast.ai makes sense for checkpointed, portable work

Vast's advantage is the marketplace. Hosts compete, so patient users can find strong value—especially for batch generation or training that can resume. You can choose on-demand, reserved, or interruptible access. Vast says interruptible instances are often at least 50% cheaper, but they may be paused. That discount is real only if interruption does not erase an hour of progress.

• Filter for the exact GPU, enough host RAM, disk space, reliability, and bandwidth.
• Prefer a verified or datacenter-style offer when the dataset or deadline is sensitive.
• Use a versioned container or startup script so changing hosts is routine.
• Write checkpoints and outputs somewhere that survives the failure you are planning for.
• Destroy finished instances: stopped instances continue to incur storage charges.

The hidden failure mode is treating every listing as interchangeable. A low bid attached to slow disk, expensive transfer, weak reliability, or a poor CPU can lose the entire advantage. Vast is a good tool for somebody willing to read the listing, not a magic discount code for somebody who wants identical machines every time.

The practical middle

RunPod is the solo creator default for a reason

RunPod usually offers a smoother first hour than a raw marketplace while keeping creator GPUs affordable. Templates shorten setup, compute is metered, and the published catalog covers consumer cards through A100/H100-class accelerators. For a person who wants ComfyUI or a LoRA trainer running tonight, that balance matters.

Storage is where people create surprise bills or surprise data loss. RunPod documents three different types:

• Container disk: temporary session storage; cleared when the Pod stops.
• Volume disk: local /workspace data retained until the Pod is deleted; listed at $0.10/GB/month running and $0.20 stopped.
• Network volume: independent, portable storage listed at $0.07/GB/month for the standard tier, with network-dependent performance.

Put models, datasets, checkpoints, and outputs in the persistence layer you intentionally selected. A custom node installed to the wrong filesystem can vanish on restart while the 50GB model cache survives, or vice versa. Test stop/start behavior before the expensive training run.

RunPod is not automatically cheapest, and Community Cloud capacity can move. But saving fifteen minutes of rebuilding on every session can outweigh a small hourly premium. That is why it is the default here, not because its logo wins a benchmark.

Pay for predictability

Lambda earns the premium on infrastructure-heavy jobs

Lambda is the least compelling choice for a hobbyist who only needs a cheap 24GB ComfyUI box for two hours. Its listed A10 is substantially more expensive than RunPod's consumer-card examples. The case improves when the job needs large attached SSDs, preinstalled Lambda Stack, API/CLI automation, no egress fees, or two-to-eight-GPU instances.

• Good fit: a research workflow the team launches repeatedly from code.
• Good fit: moving large checkpoints out after a run, where no egress charge simplifies budgeting.
• Good fit: multi-GPU training where the machine shape and host stack matter more than the lowest hourly rate.
• Weak fit: occasional image generation that fits easily on a 24GB card.

Capacity is self-serve and first-come, so “predictable” describes the product shape, not a promise that every accelerator is always available. Check inventory before committing a deadline, and automate launch plus teardown if the workflow repeats.

The bill stops when the resources do

Use a checkpoint and shutdown checklist every time

Before launch

• Confirm GPU model, VRAM, host RAM, disk size, bandwidth, and provider/host reliability.
• Estimate model, cache, dataset, checkpoint, sample, and output storage separately.
• Prepare a pinned container, requirements lockfile, or repeatable startup script.
• Upload only consented, licensed data; encrypt or minimize sensitive datasets.
• Choose checkpoint intervals based on what you can afford to lose, not a round number.

During the job

• Watch GPU utilization. Near-zero use while compute bills means the pipeline is blocked.
• Write checkpoints to persistent storage and test that one can actually resume.
• Sync irreplaceable checkpoints and sample grids to a second location during long runs.
• Record the effective start time, setup time, first training step, and final bill.

Before shutdown

• Download the final LoRA, configuration, captions, logs, validation samples, and workflow.
• Hash or open the downloaded artifact; “transfer complete” is not a recovery test.
• Delete secrets, tokens, and private input data from disposable storage.
• Stop compute, then explicitly delete disks, volumes, snapshots, and endpoints you do not need.
• Check the billing page once more; a stopped machine may still have billable storage.

The best provider is the one whose failures you can recover from and whose bill you can explain. Run one small end-to-end job before renting the expensive GPU. If it cannot save, resume, download, and cleanly terminate, it is not ready for the real dataset.