GPU Cloud Comparison: RAW vs AWS vs GCP vs Lambda Labs vs CoreWeave

The GPU Cloud Landscape in 2026

The GPU cloud market has exploded. In 2023, getting a GPU meant AWS or GCP with eye-watering pricing and week-long waitlists. Today, there are dozens of providers competing on price, availability, and developer experience.

But more options means more confusion. Every provider has different pricing models (per-hour vs monthly vs spot), different GPU SKUs, different regions, and different levels of abstraction. Some give you root access; others lock you into their platform.

This guide compares the providers that matter for production AI workloads: RAW, AWS, Google Cloud, Lambda Labs, CoreWeave, RunPod, and Vast.ai. Real pricing, real tradeoffs, no fluff.

Quick Comparison: GPU Pricing

Here's what you'll pay for a dedicated GPU across major providers. All prices are for on-demand instances (not spot/preemptible) as of April 2026.

AWS and GCP prices vary by region. Lambda, CoreWeave, RunPod, and Vast.ai prices fluctuate with availability. RAW pricing is fixed monthly.

RAW vs AWS

AWS is the 800-pound gorilla. Massive global infrastructure, every GPU SKU imaginable, and an ecosystem of managed services. But for dedicated GPU workloads, the pricing is punishing.

AWS GPU Instances

• g4dn (T4) — $0.526/hr. 16 GB VRAM. The budget option. Good enough for small model inference but the T4 is a 2018 GPU with limited tensor core performance.
• g5 (A10G) — $1.006/hr. 24 GB VRAM. The workhorse for inference. Reasonable throughput but expensive at $734/mo.
• g6 (L4) — $0.978/hr. 24 GB VRAM. Newer Ada Lovelace architecture. Marginally better performance than g5.
• p5 (H100) — $32.77/hr. 80 GB HBM3. The training machine. $23,920/mo for a single instance.

The Hidden Costs

AWS pricing gets worse once you account for:

• Egress fees: $0.09/GB for data leaving AWS. Streaming model outputs to users can add $50–200/mo depending on traffic.
• EBS storage: $0.08/GB/mo for gp3 volumes. A 500 GB volume for model weights costs $40/mo extra.
• Data transfer between AZs: $0.01/GB. Adds up fast if your inference server and application are in different availability zones.
• Reserved instances: AWS pushes 1-year or 3-year commitments for discounts. If you want flexibility, you pay full price.

RAW vs AWS: The Math

When to choose AWS: You need auto-scaling across hundreds of GPUs, your team already has AWS expertise, you need GPU instances in 20+ regions, or you need to spin up/down GPUs hourly. AWS excels at elastic, bursty GPU workloads with global reach.

When to choose RAW: You need a steady-state GPU for production inference, you want predictable monthly costs, you don't need auto-scaling, and you want 77% lower costs with no hidden fees.

RAW vs Google Cloud (GCP)

GCP's GPU offerings mirror AWS with slightly different SKUs and marginally different pricing. The same hidden-cost dynamics apply.

GCP GPU Instances

• G2 (L4) — $0.753/hr. 24 GB VRAM. Ada Lovelace architecture. GCP's best value for inference.
• A2 (A100 40GB) — $3.67/hr. 40 GB HBM2e. The older training GPU. $2,679/mo.
• A3 (H100 80GB) — $31.22/hr. 80 GB HBM3. Same ballpark as AWS p5. $22,790/mo.

When to choose GCP: You're already deep in the Google ecosystem (Vertex AI, BigQuery, GKE), you need TPUs (GCP exclusive), or you need managed ML pipelines with Vertex AI integration.

When to choose RAW: You're running self-hosted inference and don't need GCP's managed ML services. 72% savings for the same class of GPU hardware.

RAW vs Lambda Labs

Lambda Labs is the GPU-focused cloud that AI researchers love. They offer A10, A100, and H100 instances with a simple interface and competitive pricing. No egress fees — a major differentiator from AWS/GCP.

When to choose Lambda: You need H100s for training (Lambda has competitive H100 pricing at ~$2.49/hr), you want per-second billing for short experiments, or you need multi-GPU instances (8× H100 nodes).

When to choose RAW: You need affordable inference GPUs ($199/mo vs $548/mo), you want guaranteed availability without waitlists, or you prefer flat monthly billing with no surprises.

RAW vs CoreWeave

CoreWeave is the Kubernetes-native GPU cloud. Built on top of NVIDIA hardware with a heavy emphasis on orchestration and managed services. Pricing is competitive for H100-class GPUs but less so for inference-tier cards.

When to choose CoreWeave: You're running large-scale GPU clusters (50+ GPUs), you need Kubernetes orchestration for complex ML pipelines, or you have a team that thinks in pods and deployments.

When to choose RAW: You need a single GPU server for inference, you don't want Kubernetes complexity, you want the simplest possible setup (SSH into a server, install your framework, done).

RAW vs RunPod

RunPod is popular with the AI hobbyist and indie developer crowd. They offer both "Cloud" (dedicated) and "Community" (shared/spot) GPU instances, with a serverless option for burst workloads.

When to choose RunPod: You need serverless GPU endpoints (pay only when invoked), you're comfortable with spot/community instances that can be interrupted, or you want RTX 4090s specifically (great consumer GPU, 24 GB VRAM).

When to choose RAW: You need guaranteed uptime on dedicated hardware, you want included storage instead of paying per-GB, or you want flat monthly costs without managing spot interruptions.

RAW vs Vast.ai

Vast.ai is the GPU marketplace — a peer-to-peer network where anyone can rent out their GPUs. Pricing is the lowest in the market, but reliability and security are tradeoffs.

When to choose Vast.ai: You're training models and don't care about uptime (can checkpoint and resume), you want the absolute lowest $/hr for GPU compute, and you're not handling sensitive data.

When to choose RAW: You need production reliability, data security (dedicated hardware in a professional data center), GDPR compliance, or guaranteed network performance. For the same price as Vast.ai's average, you get enterprise-grade infrastructure.

Full Feature Comparison

* Lambda Labs availability fluctuates — popular GPUs are often sold out with waitlists.

When to Choose Each Provider

There's no single "best" GPU cloud. The right choice depends on your workload, budget, and operational requirements.

Choose RAW if:

• You need a steady-state GPU for production inference (chatbots, APIs, transcription)
• You want predictable monthly costs — no surprise egress or storage fees
• You need EU hosting for GDPR compliance
• You want bare metal simplicity — SSH in, install your framework, start serving
• You're cost-conscious and your GPU runs 24/7

Choose AWS or GCP if:

• You need auto-scaling across dozens or hundreds of GPUs
• You're already invested in the AWS/GCP ecosystem
• You need GPU instances in many global regions
• You need managed ML services (SageMaker, Vertex AI)
• Your GPU usage is bursty (need GPUs for hours, not months)

Choose Lambda Labs if:

• You need H100s or multi-GPU clusters for training
• You're an AI researcher who values simplicity over enterprise features
• You want competitive per-hour H100 pricing without AWS/GCP complexity

Choose CoreWeave if:

• You need large-scale GPU clusters (50+ GPUs)
• Your team runs everything on Kubernetes
• You need managed inference endpoints at scale

Choose RunPod if:

• You need serverless GPU endpoints (pay-per-invocation)
• You're comfortable with spot instances for non-critical workloads
• You want RTX 4090s for inference

Choose Vast.ai if:

• You want the absolute cheapest GPU compute available
• You're training models with checkpointing (can survive interruptions)
• You don't handle sensitive or regulated data

The Bottom Line

For most developers running production AI inference — serving a chatbot, running Whisper transcription, generating images, or hosting a RAG pipeline — a dedicated GPU server at a flat monthly rate beats per-hour cloud pricing by 60–80%.

The big clouds (AWS, GCP) make sense when you need their ecosystem, global reach, or elastic scaling. The GPU-native clouds (Lambda, CoreWeave) make sense for training clusters. The marketplaces (RunPod, Vast.ai) make sense for experimentation and spot workloads.

RAW makes sense when you need a reliable, affordable GPU server that's always on, always yours, and doesn't surprise you with hidden fees.