Replicate

How the 9 leading AI models rank Replicate in GPU cloud providers.

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Rank in GPU cloud providers

18th

of 19 in category

18thof 19

position among 19 ranked brands

AI Recommendation Score

Recommendation score

13of 100

AI model agreement

2 / 9

AI models agree

AI models recommend Replicate

ChatGPTClaudeGeminiPerplexityDeepSeekGrokCopilotGoogle AIGoogle AI Mode

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How each AI ranks it

average position · verbatim answers kept

ChatGPTread the answer10th

As of September 2026, the “best” GPU cloud depends less on raw GPU speed and more on availability, price, interconnect, reliability, and how much platform you want managed for you. If you want the short answer:

Default shortlist:

CoreWeave — best serious AI cloud / enterprise GPU clusters
Lambda Cloud — best simple, developer-friendly H100/B200-style VMs
RunPod — best for cheap, fast, self-serve GPUs and serverless inference
AWS / Google Cloud / Azure / OCI — best if you already live in a hyperscaler or need enterprise procurement/compliance
Vast.ai / TensorDock — best bargain marketplaces, but with more variability
Modal / Replicate — best if you want serverless model execution rather than managing VMs

Recommended GPU cloud providers people actually use

Rank	Provider / product	Best for	Why people use it
1	CoreWeave	Enterprise AI training, inference clusters, large H100/H200/GB200 deployments	Purpose-built AI cloud, bare-metal NVIDIA GPU fleet, strong Kubernetes/cluster story, popular with serious AI companies. CoreWeave advertises GB200 NVL72, H200, H100, bare-metal GPU nodes, 40+ data centers, and large-scale AI infrastructure. (coreweave.com)
2	Lambda Cloud	Developers, startups, labs that want straightforward GPU VMs	Very popular with ML engineers because it’s simpler than hyperscalers and focused on GPU compute. Lambda’s on-demand cloud supports Linux GPU VMs with GPUs including NVIDIA HGX B200, GH200, H100, and older models. (docs.lambda.ai)
3	RunPod	Cost-sensitive startups, hobbyists, inference endpoints, quick experiments	One of the most commonly mentioned self-serve GPU clouds. Good for spinning up containers quickly, cheap-ish hourly GPUs, and serverless endpoints. RunPod says Pods are for AI development, training, fine-tuning, batch jobs, and long-running workloads, with 30+ GPU models, 31 global regions, per-second billing, and no long-term commitment. (runpod.io)
4	AWS EC2 P5 / P5e / P5en / P6	Enterprises already on AWS, large-scale production ML, regulated workloads	Expensive and quota-constrained, but deeply integrated with the AWS ecosystem. EC2 P5 uses H100, while P5e/P5en use H200; AWS says these can scale in EC2 UltraClusters to up to 20,000 H100/H200 GPUs. (aws.amazon.com)
5	Google Cloud A3 / A4	GCP-native teams, large training, GKE/Vertex AI users	Strong AI infra, good TPU/GPU ecosystem, and good integration with Google’s ML tooling. Google Cloud lists A4 with B200, A3 Ultra with H200, A3 Mega/High/Edge with H100, and A2 with A100. (docs.cloud.google.com)
6	Microsoft Azure ND / NC GPU VMs	Microsoft-heavy enterprises, Azure OpenAI-adjacent stacks, compliance-heavy orgs	Often chosen by enterprises already standardized on Microsoft. Azure’s ND H200 v5 series uses 8 NVIDIA H200 GPUs per VM with NVLink and InfiniBand-style scale-out networking for AI/HPC workloads. (learn.microsoft.com)
7	Oracle Cloud Infrastructure GPU / Bare Metal	Bare-metal GPU clusters, price/performance, large committed deployments	OCI is a real contender for large GPU procurement. Oracle advertises bare-metal and VM GPU instances with NVIDIA Blackwell, H200, H100, L40S, A100, A10, and AMD MI300X, plus very large supercluster scaling. (oracle.com)
8	Vast.ai	Cheapest possible GPUs, experiments, batch jobs, flexible marketplace rentals	A marketplace, not a traditional single-provider cloud. Great when price matters most and you can tolerate variability in hosts, networking, and reliability. Vast describes itself as a marketplace for affordable GPU cloud computing that can scale across Secure Cloud datacenters or community providers. (docs.vast.ai)
9	TensorDock	Cheap H100/A100/RTX rentals, smaller teams, global marketplace-style access	Similar bargain-marketplace appeal, with more curated positioning. TensorDock advertises 45 GPU models, from RTX 4090 to HGX H100 SXM5, and says it is a marketplace of independent hosts with variable pricing. (tensordock.com)
10	Modal / Replicate	Serverless inference, AI apps, jobs, APIs, not managing GPU VMs	Use these when you want to deploy functions/models instead of maintaining servers. Modal supports B300, B200, H200, H100, A100, L4, T4, and L40S GPUs; Replicate Deployments offer private endpoints, autoscaling, scale-to-zero, monitoring, and multiple GPU architectures including A100s and H100s. (modal.com)

My practical recommendations

If you’re training or fine-tuning serious models

Use CoreWeave, Lambda, AWS P5/P5e, Google A3/A4, Azure ND, or OCI bare metal.

Pick CoreWeave if you want AI-native infra and can work through sales/enterprise channels.
Pick Lambda if you want a simpler GPU-cloud experience.
Pick AWS/GCP/Azure if your data, security, IAM, networking, or procurement already lives there.
Pick OCI if you want bare-metal GPU capacity and are comparing enterprise quotes.

If you’re doing quick experiments, notebooks, ComfyUI, LoRA fine-tunes, or batch jobs

Use RunPod, Lambda, Vast.ai, or TensorDock.

RunPod is the easiest “just give me a GPU now” option for many developers.
Lambda is cleaner and more conventional.
Vast.ai / TensorDock are often cheaper, but you need to be more careful about host quality, storage, bandwidth, and uptime.

If you’re deploying inference APIs

Use RunPod Serverless, Modal, Replicate, or a managed stack on AWS/GCP/Azure.

Modal is great for Python/serverless-style GPU jobs.
Replicate is great if you want model deployments and APIs with less infra work.
RunPod Serverless is a good middle ground if you want containerized inference and GPU control.

If you’re an enterprise

Start with CoreWeave, AWS, Azure, Google Cloud, OCI, and Lambda. You’ll care about committed capacity, private networking, SOC/compliance posture, support SLAs, data residency, storage throughput, Kubernetes/Slurm support, and egress costs more than a headline GPU hourly price.

Best picks by use case

Best overall AI-native GPU cloud: CoreWeave
Best developer-friendly GPU VM provider: Lambda
Best self-serve budget GPU cloud: RunPod
Best hyperscaler for existing AWS teams: AWS EC2 P5/P5e/P5en
Best for GCP/Vertex/GKE users: Google Cloud A3/A4
Best for Microsoft enterprise: Azure ND/NC GPU VMs
Best bare-metal enterprise alternative: OCI
Best bargain marketplace: Vast.ai or TensorDock
Best serverless GPU developer experience: Modal
Best model API/deployment experience: Replicate
Best “Paperspace-style” simple developer cloud: DigitalOcean, formerly Paperspace; DigitalOcean now says Paperspace is part of DigitalOcean and recommends new users explore DigitalOcean GPU offerings. (paperspace.com)

What I’d choose

If you don’t know where to start:

Solo developer / indie AI app: RunPod or Modal
Startup doing fine-tuning/inference: Lambda + RunPod backup
Production inference at scale: CoreWeave, RunPod Serverless, Modal, or Replicate depending on infra appetite
Large model training: CoreWeave, Lambda reserved clusters, AWS, GCP, Azure, or OCI
Enterprise with procurement/compliance: AWS, Azure, GCP, OCI, CoreWeave
Cheapest experiments: Vast.ai or TensorDock

One strong rule: don’t rely on only one provider. GPU availability changes constantly, especially for H100/H200/B200-class hardware, so serious teams usually keep accounts and images ready on two or three clouds.

Claudenot ranked

Gemininot ranked

Perplexitynot ranked

DeepSeekread the answer12th

The best GPU cloud provider depends on your workload: training large models, fine-tuning, inference, or experimentation. Here are the top providers people actually use, broken down by category.

🏢 Hyperscalers (Most Reliable, Enterprise-Grade)

1. Amazon Web Services (AWS)

Products: EC2 p4d / p5 instances (A100/H100), SageMaker for ML.
Best for: Enterprises already on AWS, heavy production workloads.
Pros: Massive scale, huge ecosystem, strong support.
Cons: Expensive, complex pricing, spot instances get preempted.

2. Microsoft Azure

Products: ND-series VMs (A100/H100), Azure Machine Learning.
Best for: Teams using Microsoft tools, OpenAI/security integrations.
Pros: Good H100 availability, enterprise contracts, strong MLOps.
Cons: Can be costly; less flexible than specialized providers.

3. Google Cloud Platform (GCP)

Products: A2/A3 VMs, TPU VMs, Vertex AI.
Best for: TensorFlow/JAX users, Kubernetes-heavy workflows.
Pros: Excellent networking, TPUs are unique, competitive prices for sustained use.
Cons: A100/H100 availability can be tight; UI/UX less friendly.

⚡ GPU-Specialized Clouds (Best Price/Performance)

These are the “GPU-native” providers that people use for serious training when they want more power per dollar and faster deployment.

4. CoreWeave

What: Cloud provider built specifically for GPU compute.
Best for: Large-scale AI training, batch jobs, research.
Pros: Cheaper than hyperscalers, excellent H100/A100 availability, fast networking (Infiniband), Kubernetes-friendly.
Cons: Less “full cloud” services (no managed databases, etc.).

5. Lambda Labs

What: Started as a GPU hardware supplier, now runs a dedicated cloud.
Best for: Deep learning researchers, PyTorch users.
Pros: Great prices, pre-configured images, 1-click H100/A100 clusters.
Cons: Limited regions, less enterprise support.

6. RunPod

What: Popular for on-demand GPU instances + serverless inference.
Best for: Developers, fine-tuning, cost-effective deployment.
Pros: Very cheap, easy UI, can scale to zero, great for prototyping.
Cons: Not for massive multi-node training; support can be slow.

7. Paperspace (DigitalOcean)

What: Gradient notebooks and GPU VMs (A100/H100).
Best for: Startups, ML engineers, Jupyter-heavy workflows.
Pros: Simple pricing, good notebooks UI, solid performance.
Cons: Smaller capacity than hyperscalers.

💸 Budget / Community Providers (Cheapest)

8. Vast.ai

What: Marketplace where people rent out their GPUs.
Best for: Running jobs on a budget, research, non-critical workloads.
Pros: Cheapest rates by far (often 50-70% less).
Cons: No SLA, variable hardware quality, networking can be slow.

9. JarvisLabs

What: Low-cost GPU rentals with fixed pricing.
Best for: Fine-tuning, inference, personal projects.
Pros: Clean UI, excellent prices for A100/H100.
Cons: Smaller provider, limited support.

🚀 For Hosted AI / Inference (No GPU management)

If you don’t want to manage GPUs at all:

10. Together AI

What: Managed GPU cloud for inference and fine-tuning (OpenAI-compatible API).
Best for: Running open-source models at scale.

11. Modal

What: Serverless GPU compute. You write a Python function, it runs on GPUs and autoscales.
Best for: Batch jobs, web APIs, bursty workloads.

12. Replicate

What: Hosted model deployment via API.
Best for: Developers who just want to call a model endpoint.

Top Picks by Use Case

Use Case	Recommendation
Enterprise production	AWS, Azure, GCP
Cutting-edge training at scale	CoreWeave, Lambda Labs
Experimenting / fine-tuning	RunPod, Paperspace
Maximum savings	Vast.ai, JarvisLabs
Inference / API deployment	Together AI, Modal, Replicate

Quick Tips Before You Rent

Check GPU availability — H100s are often sold out; A100s are more reliable.
Understand pricing models — On-demand vs. reserved vs. spot.
Watch out for egress fees — Transferring large model weights out of the cloud can be expensive.
Try a small instance first — Don’t spin up a 8×H100 cluster without testing your code on a single GPU.

Bottom line: For most people, RunPod or Lambda Labs are the best mix of price, usability, and performance. For serious enterprise workloads, AWS or Azure are safer bets. For large-scale training without breaking the bank, CoreWeave is the industry favorite right now.

Groknot ranked

Copilotnot ranked

Google AInot ranked

Google AI Modenot ranked

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How this is measured

Replicate’s AI Recommendation Score (13/100) reflects how widely and often the 9 AI models recommend it for GPU cloud providers: share of voice, mention rate and how early the AI models name it. Cited sources are published as receipts, never as a score input. Every monthly refresh asks each AI model the same buyer question once, and the exact run count behind every edition is published in its JSON record. Placement is determined solely by AI recommendation data; it reflects what AI recommends and is not an endorsement by CiteHawk. Read the full methodology →