AI Adoption in Healthcare: Workflow, Trust and Scale
AWS vs CoreWeave for AI: A Practical Guide for AI/ML Teams
The AI infrastructure market has split into two camps: broad general-purpose cloud platforms that do everything, and specialized GPU clouds that do one thing extremely well. AWS and CoreWeave represent opposite ends of that spectrum.
This guide compares AWS and CoreWeave across seven dimensions that matter most to AI/ML engineers and data teams: compute power, developer tooling, data infrastructure, pricing, scalability, security, and ecosystem support.
Development teams compare AWS and CoreWeave because the choice affects everything from training cost to deployment speed. At the moment, AWS offers the widest menu of cloud services on the planet. CoreWeave built its entire business around GPU density and Kubernetes workloads.
AI/ML engineers and data teams care about three things above all: access to the latest GPUs, predictable cost per training run, and minimal operational overhead. The right platform depends on where a team’s needs lie on the spectrum between general cloud needs and pure GPU compute. And for teams that need performance, cost efficiency, and data sovereignty in India, we cover where Neysa fills gaps that neither AWS nor CoreWeave addresses.
AWS for AI and Machine Learning Use Cases
AWS has the broadest service catalog of any cloud provider, and its AI stack reflects that.
AWS’ GPU instances include 5 with 8x H100s andP5en with 8x H200s, alongside AWS’s own Trainium chips.
The platform layer centers on SageMaker for end-to-end ML workflows and Bedrock for managed access to foundation models from Anthropic, Meta, Mistral, and Amazon’s own Nova family.
For data teams, the pipeline into ML is well-established: S3 for storage, Glue for ETL, Redshift for warehousing, EMR for Spark, and SageMaker Feature Store for feature management. If your data already lives in AWS, the path from raw data to trained model has fewer gaps than on most platforms.
SageMaker is arguably the most feature-rich ML platform available today. HyperPod enables fault-tolerant distributed training with automatic checkpointing and recovery. Trainium delivers 30-54% lower training costs than comparable GPU instances, but only for workloads that can move off CUDA.
The newest hardware includes Blackwell GPU instances (P6-B200, P6-B300) through EC2 Capacity Blocks, and Trainium3, launched in December 2025.
What to look out for:
- GPU lead times still take days to weeks for H100/H200
- Egress costs of $0.09/GB for the first 10 TB add up fast for inference-heavy workloads
- Configuring VPCs, IAM roles, security groups, and networking for GPU clusters requires deep AWS expertise
- Trainium creates silicon lock-in. Code written for Trainium doesn’t port to CUDA.
AWS GPU pricing:
| Instance | GPUs | On-demand/hr | Per GPU/hr |
| P5.48xlarge | 8x H100 80GB | $55.04 | $6.88 |
| P5en.48xlarge | 8x H200 141GB | $63.30 | $7.91 |
| P5e.48xlarge (Capacity Block) | 8x H200 | $39.80 | $4.98 |
CoreWeave for AI and Machine Learning
Where AWS offers breadth, CoreWeave offers depth: bare-metal GPU access, Kubernetes-native orchestration, and a deep NVIDIA partnership reinforced by NVIDIA’s $2 billion investment at $87.20 per share in January 2026.
The company operated 43 data centers as of the end of 2025, with a fleet of 250,000 GPUs. It became one of the first providers to offer GB200 NVL72 at scale, with Blackwell instances scaling to up to 110,000 GPUs connected via Quantum-2 InfiniBand. Key customers include OpenAI (total contract value up to $22.4 billion), IBM, Mistral AI, and Cohere.
CoreWeave’s software stack includes CKS (CoreWeave Kubernetes Service) for container orchestration, Mission Control for AI-native operations, and SUNK (Slurm on Kubernetes) for teams that prefer traditional HPC scheduling.
What to watch out for:
- Availability is limited to the US, Canada, and parts of Europe.
- No native data warehouse, serverless functions, or managed ML platform comparable to SageMaker. Teams must build or bring their own tooling for everything beyond GPU compute.
CoreWeave GPU pricing
| GPU Config | On-demand/hr | Per GPU/hr |
| HGX H100 (8x 80GB) | $49.24 | $6.16 |
| HGX H200 (8x 141GB) | $50.44 | $6.31 |
| HGX B200 (8x 180GB) | $68.80 | $8.60 |
| H100 PCIe (single) | $4.25 | $4.25 |
AWS vs CoreWeave Compared
ML Platforms and Developer Tooling
AWS offers SageMaker, covering data labeling, training, hyperparameter tuning, deployment, and model monitoring. Bedrock adds serverless access to dozens of foundation models. These tools abstract away infrastructure management and let data scientists focus on experiments.
CoreWeave provides the infrastructure layer but leaves platform tooling to the user. Teams deploy their own MLflow, Weights & Biases, or Kubeflow instances on CKS. Mission Control handles cluster operations, and SUNK bridges the gap for teams migrating from HPC environments.
The tradeoff: AWS’s managed tools reduce setup time but introduce opinions about workflow. CoreWeave’s open approach gives full control but requires more engineering effort to build and maintain the ML platform layer.
GPU Infrastructure and Compute
Both platforms offer H100, H200, and Blackwell GPUs. AWS bundles them into fixed instance types. CoreWeave prices GPUs individually with separate CPU and RAM charges.
For raw per-GPU cost, the numbers are closer than you might think:
| AWS | CoreWeave | |
| H100 (8-GPU node) | $6.88/GPU-hr | $6.16/GPU-hr |
| H200 (8-GPU node) | $4.98/GPU-hr (Capacity Block) | $6.31/GPU-hr |
AWS also offers Trainium as a cost-effective non-NVIDIA alternative. CoreWeave’s advantage is in Blackwell availability at scale and InfiniBand interconnect for large training clusters.
Data Infrastructure and ML Pipelines
AWS wins this category decisively. S3, Redshift, Glue, Athena, EMR, and Lake Formation form a complete data pipeline from ingestion through analysis. SageMaker Feature Store and Data Wrangler connect directly to these services.
CoreWeave provides fast storage attached to GPU nodes but nothing comparable for data processing. Teams must run their own Spark clusters, set up external data warehouses, or pipe data from AWS/GCP storage into CoreWeave compute.
If your ML workload involves complex data transformations, feature engineering, or real-time data feeds, AWS provides these natively. If your bottleneck is purely GPU compute on pre-processed data, CoreWeave’s minimal data layer is sufficient.
Scalability and Deployment
For large-scale training, CoreWeave’s 110,000-GPU Blackwell clusters with InfiniBand interconnect deliver among the highest density available in public cloud. The Kubernetes-native design makes scaling from one node to hundreds a configuration change.
AWS scales differently. UltraClusters handle large training runs, while Inferentia targets high-throughput inference at lower cost. AWS’s 30+ region global footprint means teams can deploy inference endpoints close to end users anywhere, which CoreWeave cannot match.
Security and Compliance
AWS holds the broadest compliance portfolio: FedRAMP High, HIPAA, SOC 1/2/3, ISO 27001, PCI-DSS, and dozens more. Government, healthcare, and financial institutions rely on these.
CoreWeave maintains SOC2 and ISO 27001 with single-tenant node isolation. These cover most enterprise requirements but fall short of AWS’s compliance depth for regulated industries.
Neither platform offers data sovereignty in India or other emerging markets.
When to Choose AWS
AWS is the logical choice when your AI workloads need to integrate deeply with AWS’ ecosystem. The primary advantage here is consolidation; managing everything from S3 storage to SageMaker through a single set of IAM policies reduces architectural friction.
It is particularly well-suited for regulated industries that require specific compliance portfolios or for teams that want to leverage proprietary silicon like Trainium and Inferentia to optimize inference costs.
When to Choose CoreWeave
CoreWeave is built for scenarios where GPU compute is the primary bottleneck.
For teams training large-scale models that require the latest NVIDIA generations and high bisection bandwidth, CoreWeave offers a level of hardware focus that generalist clouds often lack.
Since the platform is Kubernetes-native, it is an ideal fit for AI-first organizations that prefer to manage their own ML platform layer and need a compute-heavy environment that can scale rapidly without the overhead of 200+ unrelated services.
Why AI Teams Are Looking Beyond Both
Both platforms come with their own sets of hurdles.
With AWS, you are navigating over 200 services, which adds a lot of configuration work, and getting your hands on GPUs can be hit or miss depending on the day.
CoreWeave’s Kubernetes-only approach means you are essentially building your own machine learning platform from scratch.
Plus, both can get expensive quickly, and neither really addresses the data sovereignty needs of teams operating outside the US or Europe.
Average GPU utilization across cloud platforms sits between 20-40%. Teams pay for 100% of provisioned GPU time but use a fraction of it. The gap between “having GPU access” and “running a productive training job” remains wider than it should be on both platforms.
A growing number of ML teams want the managed experience of a full-stack platform, the cost efficiency of specialized infrastructure, and data sovereignty that global hyperscalers cannot guarantee. Neither AWS nor CoreWeave delivers all three.
When Neysa Is the Better Choice
Neysa is an AI Neocloud based in India, backed by Blackstone with up to $1.2 billion in funding. It offers a full-stack platform combining GPU infrastructure (Velocis), AI security (Aegis), and sovereign cloud capabilities.
Training large AI models
Neysa uses a RoCE-based fabric with 1:1 bisection bandwidth, so you aren’t hitting the usual networking bottlenecks. What is also interesting here is the hardware flexibility. AWS is great but keeps you locked into NVIDIA or their own custom chips, and CoreWeave is strictly an NVIDIA shop. Neysa lets you mix it up by supporting everything from NVIDIA’s H100s and L40s to AMD’s MI300X, giving you the kind of silicon diversity you just can’t get on the bigger clouds right now.
Cost-efficient GPU compute
Neysa’s pricing undercuts AWS and CoreWeave:
| GPU | Neysa On-demand | Neysa Reserved (36-mo) | AWS | CoreWeave |
| H100 SXM (1 GPU) | $4.39/hr | ~$2.44/hr | $6.88/hr | $4.25-$6.16/hr |
| H200 SXM (1 GPU) | $4.73/hr | ~$2.56/hr | $7.91/hr | $6.31/hr |
| Bare metal 8xH100 | ~$2.73/GPU-hr | ~$2.13/GPU-hr | $6.88/GPU-hr | $6.16/GPU-hr |
No egress fees, and vCPU, RAM, and storage are included free.
INR billing eliminates foreign exchange exposure for Indian teams.
Data sovereignty and compliance
Keeping your data within Indian jurisdiction is the easiest way to align with the IndiaAI Mission and avoid the compliance headaches of US based cloud providers. Plus, generic cloud security tools often miss AI specific vulnerabilities. That is why the Aegis suite is built directly in to handle prompt injection defense, data exfiltration, and policy guardrails.
Full-stack platform, not just GPUs
CoreWeave is brilliant for teams that want to build their own Kubernetes environment, and AWS is unmatched for custom service configurations. Neysa’s Velocis platform is designed for teams that just want to build. It comes pre loaded with PyTorch, TensorFlow, and Hugging Face alongside built in analytics, meaning you go from initial idea to active training in minutes.
Conclusion
Choose AWS if: You need to plug into a massive cloud ecosystem, roll out globally, or check off a long list of compliance certifications.
Choose CoreWeave if: You care most about pure GPU power and the latest NVIDIA chips, your team runs on Kubernetes, and your data is already housed off-site.
But for AI teams that need the managed experience of a full-stack platform, competitive pricing, and data sovereignty in India, Neysa combines these into a single offering. With 36-64% lower GPU pricing versus general-purpose cloud, sovereign data residency, an integrated AI platform, and silicon diversity across NVIDIA and AMD, it serves teams that want enterprise-grade capability without the complexity, cost, or compliance uncertainty.
