The best GPU hosting for AI inference depends less on the headline GPU price than on how the workload behaves. RunPod is our best overall choice because it combines flexible GPU Pods, serverless inference, custom containers, and persistent storage on a single platform. Modal is better for code-first teams with bursty traffic, while Lambda is stronger when a dedicated GPU will remain busy for long periods.

This comparison evaluates GPU hosts for open-source LLMs, vLLM endpoints, Stable Diffusion, ComfyUI, fine-tuning and custom PyTorch containers. We considered usable VRAM, cold-start behaviour, billing granularity, idle cost, storage, egress, regional capacity, spot interruption risk and the work required to move from a prototype to a reliable service. For the broader application stack around a model endpoint, see our guide to the best AI hosting for apps, models and AI SaaS.

Best GPU hosting for AI: quick recommendations

Rank	Provider	Best for	Billing model	Main advantage	Main limitation
1	RunPod	Best overall for custom inference, ComfyUI and flexible GPU deployment	Per-second serverless and metered GPU Pods	Strong balance of price, GPU choice, templates, containers and storage	Cold-start and storage choices need careful configuration
2	Modal	Bursty serverless inference and Python-first teams	Per-second compute with scale-to-zero	Excellent developer workflow, autoscaling and snapshot options	Can become expensive for continuously busy endpoints
3	Lambda	Always-on inference, fine-tuning and multi-GPU instances	Hourly on-demand instances and committed clusters	Clear dedicated GPU configurations with large local storage	No suspend option for on-demand instances, so idle time remains costly
4	Koyeb	PaaS-style GPU applications with autoscaling	Per-second instances with scale-to-zero in public preview	Simple container deployment and unusually competitive public GPU rates	GPU availability and scale-to-zero behaviour vary by region and workload
5	Vast.ai	Lowest-cost experiments and fault-tolerant batch jobs	Marketplace pricing billed by the second	Very broad supply and aggressive prices	Host quality, bandwidth, storage and reliability vary between offers
6	CoreWeave	Enterprise inference, Kubernetes and large GPU fleets	On-demand, spot and contracted infrastructure	Deep AI infrastructure with high-end GPU and cluster options	More platform than most small teams need
7	Replicate	Launching a model API without operating GPU infrastructure	Model, hardware or output-based usage pricing	Fastest route from a supported model to a callable API	Less control and potentially higher cost at sustained utilisation
8	DigitalOcean Paperspace	Notebooks, development environments and conventional GPU machines	Hourly machines with compute billing stopped when powered off	Familiar virtual-machine workflow and persistent development environment	Older GPU options can be poor value for production inference

Our verdict: choose RunPod if you want one provider that can cover interactive GPU work, custom containers and serverless inference. Choose Modal when requests arrive in bursts and the application is comfortable with a Python-first deployment model. Choose Lambda when a dedicated GPU will remain well utilised and predictable access matters more than scaling to zero.

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How we evaluated GPU hosting providers

We did not assign a score based on the cheapest H100 listing. That method rewards providers that publish a low compute number while ignoring everything around it. A usable inference service also needs model weights, container layers, storage, networking, capacity and a reliable path from an incoming request to a warm GPU.

Our evaluation uses the following practical criteria:

• Usable VRAM: the memory available for model weights, activations and the KV cache, not merely the GPU family name.
• Cold-start path: container pull time, model download, deserialisation, transfer into GPU memory, compilation and warm-up.
• Billing behaviour: per-second or hourly charging, minimum billing periods, warm-worker cost and whether powering down actually stops compute charges.
• Persistent storage: cost, speed, regional restrictions and whether the same volume can attach to replacement capacity.
• Capacity: the chance of obtaining the required GPU in the required region during peak demand.
• Operational control: support for Docker, PyTorch, vLLM, ComfyUI, custom CUDA dependencies, health checks and autoscaling.
• Failure cost: spot interruption, failed starts, slow hosts and time spent rebuilding an environment.
• Production fit: observability, private networking, security controls, support and a credible scaling route.

The most useful cost measure is not price per GPU hour. It is cost per successful unit of work:

effective cost = compute + warm idle + cold starts + storage + transfer + failed runs
                 ---------------------------------------------------------------------
                              successful requests or completed jobs

This exposes why a slightly dearer provider can be cheaper in practice. A stable endpoint that loads a model once and processes a queue efficiently may outperform a low-rate marketplace instance that repeatedly downloads weights, loses capacity, or requires manual repair.

The recurring real-world insight: reproducibility beats the cheapest GPU

Across developer and ComfyUI communities, the same problem recurs: users choose a provider based on GPU rates, then spend time rebuilding packages, downloading models, or waiting for the exact host attached to their storage. The working environment becomes more valuable than the advertised accelerator.

The practical fix is to separate three layers. Put system packages and Python dependencies in a versioned container image. Put model weights, LoRAs and large artefacts in persistent or object storage. Keep generated outputs and customer data outside the disposable GPU instance. This makes it possible to replace a failed host, change the GPU type, or move regions without having to reconstruct the whole stack by hand.

This is especially important for ComfyUI. Custom nodes installed interactively may disappear when a container is replaced, whereas a large local model library can lock the user to a single volume or region. A reproducible image plus an external model manifest is less convenient on the first day, but far cheaper by the fifth rebuild.

GPU hosting pricing compared

The figures below are representative public prices checked on 2 July 2026. They are not directly interchangeable because providers bundle different CPU, RAM, storage and service layers. Marketplace prices also change continuously. Use them to understand the billing shape, then price the complete deployment.

Provider	Representative public price	Idle treatment	Storage consideration
RunPod Serverless	From $0.00016 per second for 16 GB GPUs; A100 80 GB at $0.00076 per second	Flex workers scale to zero; active workers remain warm	Container disk and network volumes are billed separately
Modal GPU	T4 at $0.000164 per second; L40S at $0.000542; H100 at $0.001097	No compute charge while scaled to zero	Volumes priced separately; model-loading design affects startup time
Lambda	A6000 from $1.09 per GPU hour; H100 PCIe from $3.29; H100 SXM from $4.29	Instance billing continues until termination	Persistent file systems are available; local instance data should not be treated as permanent
Koyeb	A6000 at $0.75 per hour; L40S at $1.20; H100 at $2.50	GPU scale-to-zero is available in public preview	Application and model persistence must be planned around the service architecture
Vast.ai	Market-driven and variable by host, region and reliability	Compute billed while running; storage continues while the instance exists	Storage and both directions of transfer may be separately priced
CoreWeave	Public on-demand, spot and inference pricing varies by GPU and region	Depends on deployment type and committed capacity	Several object, distributed and local storage options
Replicate API	Varies by model, hardware and deployment type	Public models charge active processing; dedicated deployments also charge setup and idle time	Model hosting is managed, reducing direct storage administration
Paperspace by DigitalOcean	A6000 at $1.89 per hour; A100 80 GB at $3.18; H100 at $5.95	Powering off a machine stops compute billing	Attached storage and add-ons continue billing after shutdown

1. RunPod: best overall GPU hosting for AI inference

RunPod earns first place because it covers more stages of the inference lifecycle than most specialist GPU providers. A team can begin with an interactive Pod, package the workload into a Docker image, attach persistent model storage and move a repeatable endpoint to Serverless without changing provider.

That flexibility suits ComfyUI, Stable Diffusion, custom Diffusers pipelines, vLLM, Ollama, PyTorch jobs and fine-tuning. RunPod also offers a wider range of consumer and data-centre GPUs than platforms that are limited to a few enterprise accelerators. A 24 GB card can be sensible for a small quantised LLM or image pipeline, while 48 GB, 80 GB, and larger options provide a path to heavier models.

The main risk is assuming that serverless removes all infrastructure decisions. RunPod bills the worker while it starts, loads the model, processes the request and waits through the configured idle timeout. A 30-second prediction with a 90-second cold start is not a 30-second bill. Cached models, baked container images, and network volumes can improve this, but each option affects build time, storage costs, and portability.

Persistent storage also needs deliberate placement. A local volume may preserve a working ComfyUI environment but tie recovery to a specific host or capacity pool. A network volume is more portable within its supported location, although it can restrict region choice and may be slower than local NVMe. For production, keep the container reproducible and treat the volume as data rather than the only copy of the environment.

Who should choose RunPod?

• Teams moving from notebook experiments to custom model endpoints
• ComfyUI users who need persistent models, LoRAs and custom nodes
• Developers serving open-weight LLMs through vLLM or another OpenAI-compatible server
• Products with variable traffic that can tolerate or optimise cold starts
• Batch image, audio and video generation pipelines that can use queues

Skip RunPod when the team wants a completely managed model API with no container ownership, or when an enterprise deployment requires contracted capacity and platform engineering support from the outset.

2. Modal: best serverless GPU platform for bursty inference

Modal is the strongest option for engineers who want to express infrastructure alongside Python code rather than manage virtual machines. Functions can request a GPU, define dependencies, mount model storage, expose web endpoints and scale down when work stops. This is a good fit for batch jobs, scheduled processing, asynchronous media generation and APIs with irregular demand.

Modal’s main advantage is the level of control it provides without requiring the team to operate Kubernetes. It supports GPUs ranging from T4 and L4 to A100, H100, H200, and B200, plus multi-GPU containers for larger workloads. Memory and GPU snapshots can reduce parts of the cold-start path, while minimum container settings allow latency-sensitive endpoints to remain warm.

There is a trade-off. Serverless unit pricing is often higher than that of a low-cost dedicated instance when the GPU is continuously busy. The platform remains attractive because the customer is also buying autoscaling, image builds, scheduling, logs and a simpler operational model. Once utilisation becomes steady, compare a warm Modal endpoint against an always-on Lambda, Koyeb or RunPod deployment using real throughput rather than hourly equivalents alone.

Modal is also a better fit for API-shaped workloads than for a long-lived desktop-style environment. ComfyUI can run on it, but teams should package nodes and models as a deployment rather than expect the experience of renting a persistent workstation.

Who should choose Modal?

• Python teams that want infrastructure defined in application code
• Bursty inference that can scale to zero between traffic windows
• Parallel batch processing, evaluation and data-generation jobs
• Applications that need fast experimentation across several GPU types
• Teams that value developer experience more than the lowest raw GPU rate

3. Lambda: best for continuously utilised dedicated GPUs

Lambda is better suited to workloads that behave like infrastructure rather than functions. Its on-demand instances provide dedicated NVIDIA GPUs, substantial CPU and RAM, local SSD storage, SSH access, JupyterLab, Docker and Lambda Stack. That makes it a natural home for a continuously running vLLM server, fine-tuning work, large-batch jobs, and multi-GPU inference.

Pricing is straightforward by GPU configuration. A single A6000, A100, H100, or B200 instance can be launched for development or production, while larger 1-Click Clusters are designed for multi-node workloads. Lambda also documents vLLM deployments and provides persistent file systems, which reduces the amount of platform discovery required for an open-weight LLM stack.

The hidden limitation is lifecycle control. On-demand instances can be launched, restarted or terminated, but not suspended. Trying to power down the operating system does not safely stop billing. Bursty workloads therefore need automation to terminate the instance and restore state from a persistent filesystem or object store. That is workable, but it is not serverless.

Availability can also be first-come. A good public rate has little value if the required GPU is unavailable in the region where the application needs it. Production buyers should test capacity over several days and discuss reserved access before treating an on-demand listing as guaranteed supply.

Who should choose Lambda?

• LLM endpoints with high and predictable GPU utilisation
• Fine-tuning and training jobs that need dedicated multi-GPU machines
• Teams comfortable with Linux, Docker, networking and model serving
• Projects that need large local SSD capacity alongside the GPU
• Organisations that may progress from one instance to a contracted cluster

4. Koyeb: best PaaS-style GPU deployment

Koyeb combines a conventional application platform with a strong GPU catalogue. Developers deploy containers or source code as services, then use health checks, autoscaling, regions and platform networking without building those components around a raw GPU VM. Its public rates are particularly competitive for A6000, L40S, A100 and H100 instances.

The 2026 addition that changes the comparison is GPU scale-to-zero. It can reduce a service to no running instances after an idle period, then wake it when traffic returns. This is currently a public preview feature, so production teams should measure wake time, failure handling and capacity behaviour before relying on it for a strict latency target.

Koyeb suits an API that should look and operate like the rest of a containerised application stack. It is less natural for an interactive notebook or a heavily customised workstation. It also has a narrower ecosystem than RunPod for one-click ComfyUI templates and community GPU workflows.

A sensible architecture is to keep the web application, database and job producer on a general application host, then send inference work to the GPU service. Our Railway Hosting review explains why Railway works well for APIs, queues and workers but is not the first choice for directly hosting large models.

5. Vast.ai: best cheap GPU marketplace for experienced users

Vast.ai operates a marketplace rather than a uniform fleet. Hosts compete on GPU price, storage, bandwidth, reliability and location. This can deliver excellent value, particularly for experiments, rendering, model conversion, fine-tuning, and batch jobs that can tolerate interruptions.

The marketplace model is also why Vast.ai ranks below more consistent platforms. Two offers for the same GPU can differ in CPU performance, disk speed, network throughput, reliability history and transfer charges. A cheap instance with slow model download speeds or limited storage may complete fewer jobs per hour than a more expensive listing.

Interruptible instances can substantially reduce compute costs, but they are a poor default for a synchronous customer-facing API. Use them for checkpointed training, queued generation and restartable batch work. On-demand offers are safer for interactive work, although the host still needs to be evaluated rather than trusted solely by GPU name.

Vast.ai also makes cost components more visible than some buyers expect. Storage continues billing while an instance exists, including when stopped, and hosts can charge for inbound and outbound data. Large model weights, container layers and generated media therefore belong in the cost model from day one.

6. CoreWeave: best for enterprise GPU infrastructure

CoreWeave targets organisations that need more than a rented GPU. Its platform covers managed Kubernetes, serverless and dedicated inference, object storage, distributed file systems, high-speed networking, and large clusters built around current data-centre accelerators.

This makes CoreWeave compelling for high-volume inference, multi-model serving, enterprise controls and teams already operating Kubernetes. It also creates room to optimise beyond a single container on a single GPU, including data locality, cluster scheduling, high-throughput storage, and reserved capacity.

The trade-off is organisational weight. A small SaaS product serving one 8B model rarely needs a platform designed for large AI fleets. The engineering and commercial conversation becomes worthwhile when capacity guarantees, multi-node performance, private infrastructure or large-scale observability justify the added complexity.

CoreWeave should be shortlisted when inference is a core production capability rather than an experimental feature. For a prototype, RunPod, Modal, Koyeb or Replicate will normally produce a useful endpoint faster.

7. Replicate: best managed alternative to raw GPU hosting

Replicate is not a conventional GPU host, but it belongs in this comparison because many teams do not actually need a server. They need a stable model API, predictable inputs and outputs, and a path to deploy a custom version without managing CUDA, drivers, or an autoscaler.

Public models charge for active processing and may share a hardware pool, so users can encounter queues or cold boots. Official models are maintained and kept warm, with pricing based on outputs such as tokens, images or video seconds. Private models and dedicated deployments provide more control but may charge for setup and for idle and active time.

Replicate is strongest during product validation. It lets a team test whether users value an AI feature before building a dedicated inference platform. It is also useful for media generation where model choice changes frequently. The limitation appears at sustained scale: a managed per-output or per-run price can exceed the cost of a well-utilised dedicated endpoint, and the platform offers less low-level tuning.

Creators who want to compare image models or generate video, audio and music without operating inference infrastructure can use DIY AI Studio. That is a different purchase decision from renting a GPU, but often a more efficient one for content workflows.

8. DigitalOcean Paperspace: best for notebooks and conventional GPU machines

DigitalOcean Paperspace provides GPU-backed Machines, Notebooks, Deployments and Workflows. It is easiest to understand as a development and machine-learning environment rather than a specialist low-cost inference cloud.

Paperspace Machines have one useful billing behaviour: powering off the machine stops compute charges, while storage and add-ons continue. That is friendlier than VM products that bill until they are destroyed. The platform also offers persistent development environments and familiar notebook workflows.

The weakness is the price-to-performance ratio across several GPU tiers. Older cards remain available, and the public rates for A100 and H100 machines are higher than aggressive specialist providers. Paperspace makes more sense when the notebook environment, workflow tools, or the DigitalOcean relationship have value beyond the GPU itself.

For the surrounding database, object storage, Kubernetes and application hosting options, our full DigitalOcean review covers the operational trade-offs in more depth.

GPU hosting pros and cons compared

Provider	Pros	Cons
RunPod GPU Cloud	Pods and serverless in one platform. Broad GPU selection. Strong ComfyUI and custom-container fit	Cold starts can include model-loading cost. Storage design affects portability. Capacity can vary by GPU and region
Modal platform	Excellent Python developer workflowAutomatic scale-to-zeroSnapshots and fast iteration	Higher unit cost at continuous load. Less like a persistent workstation. Requires adaptation to Modal’s programming model
Lambda	Clear dedicated GPU instances. Strong multi-GPU and vLLM fit. Large CPU, RAM and local storage allocations	No instance suspension. Manual autoscaling and endpoint operations. On-demand capacity is not guaranteed
Koyeb	Competitive public GPU pricing. PaaS deployment and health checks. Scale-to-zero option	GPU scale-to-zero remains in preview. Smaller community ecosystem. Regional capacity needs testing
Vast.ai	Very low marketplace prices. Broad hardware supply. Good interruptible options for batch work	Variable host quality. Separate storage and bandwidth costs. More operational screening and automation
CoreWeave	Enterprise AI infrastructure. Managed Kubernetes and large clusters. Strong storage and networking choices	Too complex for many small projects. Commercial planning may be required. Less suited to casual experimentation
Replicate models	Fastest route to a managed model API. No driver or GPU administration. Large model catalogue	Less infrastructure control. Shared models can queue or cold boot. Dedicated usage can be costly at scale
DigitalOcean Paperspace	Good notebooks and persistent development. Compute billing stops when powered off. Part of the DigitalOcean ecosystem	High-end GPU prices are less competitive. Some older hardware remains prominent. Not the strongest serverless inference choice

Choose VRAM before choosing a GPU brand

VRAM is the first hard constraint. A faster GPU cannot load a model that does not fit. Model weights are only part of the requirement; inference also requires memory for activations, attention caches, batching, and the serving framework. Long context windows can increase KV-cache demand enough to change the hardware decision.

A practical starting map looks like this:

VRAM range	Typical fit	Main caution
16 to 24 GB	Small quantised LLMs, Stable Diffusion, SDXL, lightweight fine-tunes and development	Limited batch size and little headroom for long context or several models
40 to 48 GB	Larger diffusion pipelines, 13B to 30B-class quantised models, more batching and ComfyUI workflows	Model fit depends heavily on precision, context and runtime overhead
80 GB	Higher-throughput inference, larger quantised LLMs and workloads that need a larger KV cache	A 70B model may still need quantisation or multiple GPUs depending on configuration
96 to 180 GB	Large models, long context, fewer shards and memory-heavy multimodal inference	Premium hardware is wasted if traffic cannot keep it busy

Architecture matters alongside capacity. NVIDIA’s L40S specification lists 48 GB of memory and positions the card for multimodal generative AI, while H100-class hardware adds high-bandwidth memory and transformer-focused acceleration. The newer GPU is not automatically the better buy. For image generation or a modest quantised model, a cheaper 48 GB card can provide a better cost per completed request.

Cold starts are a pipeline, not one number

Providers often describe a container start time, but the user experiences the complete model-ready path. That path can include:

1. Waiting for a compatible GPU to become available
2. Pulling the container image
3. Mounting or downloading model weights
4. Loading weights into system memory
5. Transferring them into GPU memory
6. Compiling kernels or building an execution graph
7. Running a warm-up request

A cached image solves only some of these steps. Persistent storage can avoid downloading weights but still leave deserialisation and GPU transfer. A memory snapshot may reduce more of the path but can introduce framework or driver restrictions. Measure time to the first successful response with the actual model, image and region.

For synchronous chat or voice, keep a minimum warm replica in place when the latency target cannot accommodate a cold start. For image and video generation, accept work into a queue and report progress to the application. The latter architecture treats cold starts as a throughput problem rather than a failed web request.

When serverless GPU hosting is cheaper

Serverless wins when traffic is intermittent, and the endpoint can scale down for meaningful periods. A useful break-even estimate is:

break-even utilisation = always-on hourly price
                         -------------------------------------
                         serverless per-second price x 3,600

Suppose an always-on L40S costs $1.20 per hour and a serverless L40S has an hourly-equivalent compute rate near $1.95. Ignoring performance and storage differences, the always-on instance becomes cheaper above roughly 62 per cent utilisation. Below that point, scaling to zero may save money. The real break-even moves once cold starts, minimum warm workers, failed requests, and engineering time are included.

Serverless is usually the better starting point for a feature with unknown demand. Dedicated instances become more attractive after usage is stable enough to batch requests and keep the GPU occupied. Do not migrate solely because the monthly bill looks large. First, check whether model batching, quantisation, continuous batching, or a smaller GPU can improve utilisation without changing the provider.

When a hosted model API is cheaper than renting a GPU

A hosted API is often cheaper during validation, even when its per-token or per-image rate looks high. There is no idle GPU, no model download, no autoscaler to operate and no staff time spent fixing CUDA or container failures. This is why Replicate and other managed inference APIs remain useful alternatives.

Renting a GPU becomes more compelling when at least one of these conditions applies:

• The workload is steady enough to keep the accelerator busy
• The model uses private weights or custom code unavailable through an API
• Latency requires a dedicated warm endpoint
• Per-request API margins become unacceptable at scale
• The team needs low-level control over quantisation, batching or serving parameters
• Data handling rules require a controlled deployment environment

Many AI SaaS products should use a split architecture. Host the customer-facing application, authentication, database and queues on Railway or DigitalOcean, then send only inference jobs to the GPU provider. That keeps expensive accelerators focused on model work and makes the rest of the application easier to scale and recover.

Best GPU host by workload

Workload	Best starting choice	Why
ComfyUI with custom nodes and persistent models	RunPod	Templates, custom containers, GPU choice and persistent storage suit iterative visual workflows
Bursty Python inference API	Modal	Code-first deployment, autoscaling and scale-to-zero reduce platform work
Always-on vLLM endpoint	Lambda or Koyeb	Dedicated pricing can beat serverless when utilisation stays high
Cheap fine-tuning or checkpointed batch work	Vast.ai	Marketplace and interruptible supply can reduce compute cost
Enterprise multi-model serving	CoreWeave	Managed Kubernetes, storage, networking and capacity planning support larger fleets
Prototype using a known public model	Replicate	Fast API deployment without GPU administration
Notebook-led model development	DigitalOcean Paperspace	Persistent notebook and machine workflow is straightforward
Image, video, audio or music creation without infrastructure	DIY AI Studio	Uses managed creative models without requiring the user to deploy a GPU stack

Common GPU hosting mistakes

Buying the fastest GPU before sizing the model

Start with model memory, target context, batch size and latency. A cheaper 48 GB GPU can outperform an underutilised H100 in terms of cost per request.

Keeping the model inside an unreproducible machine

Manual package installation feels fast until the instance disappears. Build a versioned container, pin important dependencies and store models or manifests outside the instance.

Using spot capacity for a synchronous API

Interruptible GPUs should be placed behind a queue with retry and checkpoint logic. A customer waiting on an HTTP request should not discover that a higher bidder reclaimed the server.

Ignoring model download and container size

A huge image or repeated weight download can erase serverless savings. Separate stable runtime layers from frequently changed application code, and cache model artefacts close to the compute.

Assuming persistent storage is automatically portable

A volume can be limited to a region, host or availability zone. Check what happens when the preferred GPU is unavailable and whether another instance can mount the same data.

Leaving development GPUs running

Idle notebooks, Pods and instances are a common source of avoidable spend. Add automatic shutdown, budget alerts and a daily resource audit during development.

Running the entire SaaS stack on the GPU server

Databases, web applications and background orchestration rarely need expensive GPU compute. Separate them so the model server can be replaced, scaled or terminated without taking the product offline.

GPU hosting deployment checklist

• Calculate model weight size at the intended precision.
• Reserve VRAM for activations, KV cache, batching and framework overhead.
• Test the complete first-request cold start, not only container boot time.
• Measure tokens, images or jobs per hour on the actual model.
• Price warm idle, storage and transfer alongside GPU compute.
• Confirm GPU availability in the required region at several times of day.
• Package the runtime in a reproducible container.
• Keep durable models, outputs and checkpoints outside ephemeral compute.
• Use a queue for long-running or restartable generation jobs.
• Set health checks, execution timeouts and graceful shutdown behaviour.
• Use spot capacity only where interruption can be retried safely.
• Load test with realistic context length and concurrent requests.
• Set budget alerts below the point where an automatic limit would cause an outage.
• Document how to move the workload to another GPU type or provider.

Which GPU hosting provider should you choose?

RunPod is the best default for most teams because it supports the messy middle between experimentation and production. It can run a persistent GPU environment today and a serverless custom endpoint later, without forcing an early commitment to a single deployment model.

Modal is the better choice for bursty, code-first inference where scale-to-zero and developer speed justify a higher unit rate. Lambda is stronger for a continuously utilised GPU, especially when the team wants SSH, Docker, vLLM and a clear path to multi-GPU infrastructure. Koyeb deserves a serious look for containerised APIs because its public rates and PaaS controls are unusually competitive.

Vast.ai wins on potential price but asks the buyer to manage marketplace variability. CoreWeave is the enterprise choice once GPU infrastructure becomes a platform problem. Replicate is often the smartest first step when validating demand, while Paperspace remains useful for notebook-led development.

The final decision should come from a one-week workload trial. Record cold-start time, successful requests, GPU utilisation, model-loading time, storage, transfer, failed starts and engineering intervention. The provider with the lowest invoice is not necessarily the cheapest. The provider that completes the workload reliably with the least wasted GPU time usually is.

GPU hosting FAQs

What is the best GPU cloud for AI inference?

RunPod is the best overall choice for most custom AI inference workloads because it combines GPU Pods, serverless endpoints, custom containers, persistent storage and a broad GPU catalogue. Modal is better for bursty Python workloads, while Lambda is better for continuously busy dedicated GPUs.

Is RunPod or Modal better?

RunPod is better for flexible GPU rental, ComfyUI, persistent workspaces and teams that want both Pods and serverless deployment. Modal is better for Python-first applications that need automatic scaling, scheduled jobs and infrastructure defined in code.

Is GPU serverless cheaper than an hourly GPU?

It is cheaper when the workload is idle for substantial periods. An hourly instance becomes more economical as utilisation rises. Include cold-start time, warm replicas, storage and request batching in the comparison.

How much VRAM is needed for AI inference?

Small quantised LLMs and common diffusion pipelines can fit within 16-24 GB. Larger models, batching and long context often need 40 to 80 GB or more. The exact requirements depend on model size, precision, context length, serving framework, and concurrency.

What is the best GPU hosting for ComfyUI?

RunPod is the strongest general choice because it supports templates, custom Docker images, persistent storage and several consumer and data-centre GPUs. Vast.ai can be cheaper for experienced users who are comfortable evaluating hosts and rebuilding environments.

Can Railway host an AI model on a GPU?

Railway is better suited to the application layer around an AI model, including APIs, databases, queues, workers and webhooks. A dedicated GPU provider should handle large model inference, with Railway sending jobs to it.

Is spot GPU hosting safe for production inference?

Not for a synchronous endpoint unless the architecture can fail over immediately. Spot and interruptible instances are better suited to queued jobs, checkpointed training, and restartable batch workloads.

Should I use a managed AI API instead of GPU hosting?

Use a managed API when demand is uncertain, the model is already supported, and infrastructure control is not a product requirement. Rent a GPU when utilisation is steady, custom weights are required or low-level serving optimisation creates a meaningful cost or latency advantage.