Comparing AI cloud providers: CoreWeave, Lambda, Cerebras, Etched, Modal, Foundry
Explore the top AI-focused cloud providers: CoreWeave, Lambda, Cerebras, Modal, and Etched. Compare features, performance, and best-suited applications for enterprise AI.
Key Takeaways
CoreWeave excels in rapid scaling and diverse GPU options, making it ideal for VFX rendering, large-scale simulations, and generative AI workloads.
Lambda Labs specializes in training and deploying large language models, offering powerful multi-GPU instances for intensive machine learning tasks.
Cerebras' Wafer-Scale Engine provides unparalleled performance for high-performance computing scenarios, particularly in natural language processing and scientific simulations.
Modal's serverless platform simplifies AI deployment, making it well-suited for generative AI applications, computational biotech, and automated transcription services.
Etched's Sohu chip, designed specifically for transformer models, offers impressive performance for large language model inference and high-throughput AI services.
Last year, I started Multimodal, a Generative AI company that helps organizations automate complex, knowledge-based workflows using AI Agents. Check it out here.
As AI models grow more complex and data-intensive, traditional cloud solutions are struggling to keep pace. Capital expenditure on AI-focused cloud infrastructure is projected to surge by 30%, with AI servers accounting for 66% of overall servers.
Besides the hyperscalers, there are several AI cloud providers emerging as great alternatives. Let’s compare them across features and applications,
Coreweave
CoreWeave's platform is built on three pillars:
1. Kubernetes-native cloud: CoreWeave leverages Kubernetes to deliver bare metal performance without the infrastructure. This means you can spin up resources in as little as 5 seconds, making it excellent for data scientists and AI researchers who need on-demand computing power.
2. GPU compute: With access to a broad range of NVIDIA GPUs, including the latest models like the H100 Tensor Core, CoreWeave offers unparalleled flexibility for GPU-intensive workloads.
3. Specialized networking: CoreWeave's Cloud Native Networking system, coupled with NVIDIA Quantum InfiniBand, delivers blazing-fast performance with up to 400 GB/s of throughput for the most demanding applications.
Best suited applications
1. Machine learning model training and fine-tuning: With its state-of-the-art distributed training clusters, CoreWeave claims to offer training speeds up to 35x faster than traditional cloud providers.
2. Visual effects (VFX) and rendering: CoreWeave's architecture allows for the elimination of render queues, accelerating artist workflows in the entertainment industry.
3. Large-scale simulations: The platform's high-performance computing (HPC) capabilities make it ideal for complex simulations in fields like financial analytics and life sciences.
4. Generative AI workloads: CoreWeave's specialized infrastructure is particularly well-suited for the compute-intensive demands of generative AI, offering both the power and scalability needed for these cutting-edge applications.
By focusing on GPU-accelerated cloud services, Coreweave has created a niche that caters to the most demanding compute workloads. Their claim of being up to 80% less expensive than generalized public clouds is a game-changer for businesses looking to scale their AI and machine learning operations.
Lambda
Lambda's platform consists of two main features:
1. On-demand GPU clusters: Lambda offers a 1-Click Cluster solution, allowing users to spin up powerful GPU resources in a matter of hours, not months. These clusters feature NVIDIA H100 Tensor Core GPUs interconnected with InfiniBand, delivering lightning-fast performance for the most demanding AI tasks.
2. Physical GPU workstations: For teams that require on-premises solutions, Lambda provides high-performance workstations equipped with cutting-edge NVIDIA GPUs.
Their cloud comes pre-configured with popular machine learning frameworks like TensorFlow and PyTorch, along with NVIDIA CUDA and cuDNN, making it a turnkey solution for data scientists and AI researchers.
Best suited applications
1. Training and deploying large language models: With access to multi-thousand GPU clusters, Lambda provides the computational muscle needed to train and fine-tune massive language models.
2. Generative AI development: The platform's high-performance computing capabilities make it ideal for developing cutting-edge generative AI applications, from text generation to image synthesis.
3. Intensive machine learning tasks: Lambda's GPU-accelerated cloud is perfect for tackling complex machine learning workloads, including deep learning and computer vision tasks.
Their 1-Click Clusters allow teams to access datacenter-scale AI compute without long-term contracts or complex negotiations. This accessibility is a game-changer for AI startups and research teams looking to prove their concepts quickly and cost-effectively.
Cerebras
1. Wafer-Scale Engine (WSE): Cerebras' WSE is a marvel of engineering, packing an entire wafer's worth of compute into a single chip. This approach yields:
- Over 2.6 trillion transistors
- 850,000 AI-optimized cores
- 40 GB of on-chip memory
2. CS-2 AI Supercomputer: Built around the WSE, the CS-2 system offers:
- 123 times more cores than leading GPUs
- 1,000 times more high-bandwidth memory
- 12,000 times more memory bandwidth
These specifications translate into unprecedented processing power for AI workloads, enabling researchers and data scientists to tackle problems that were previously computationally infeasible.
Best suited applications
Cerebras' technology excels in several domains:
1. High-Performance Computing: The CS-2's massive parallelism makes it ideal for complex simulations and modeling in fields like particle physics and fluid dynamics.
2. Natural Language Processing: With its ability to handle enormous datasets and models, the CS-2 is perfectly suited for training and running large language models.
3. Medical AI and drug discovery: The system's processing power enables rapid analysis of genetic data and molecular simulations, accelerating the drug discovery process.
4. Weather forecasting: The CS-2's computational capabilities allow for more detailed and accurate climate models, improving long-term weather predictions.
Cerebras condenses what would typically require a cluster of GPUs into a single, power-efficient system. This not only simplifies infrastructure management but also dramatically reduces energy consumption.
Modal
1. Serverless platform: Modal eliminates the need for provisioning and managing virtual machines or GPU instances. Its serverless design allows users to focus entirely on their machine learning models and AI workloads, while the platform handles scaling and resource allocation automatically.
2. Automated scaling: Whether you're running a small batch process or a large-scale generative AI model, Modal dynamically adjusts resources to meet demand, ensuring cost-effective solutions with minimal effort.
Modal integrates effortlessly with popular machine learning frameworks and tools, offering pre-configured environments that work out-of-the-box. This makes it an attractive option for teams seeking low-latency performance without the overhead of traditional cloud services.
Best suited applications
1. Generative AI: Modal's serverless infrastructure is ideal for running inference workloads for generative AI models, such as those used in text generation or image synthesis.
2. Computational biotech: With its ability to handle data-intensive applications, Modal supports bioinformatics tasks like genomic analysis and protein structure prediction.
3. Automated transcription: The platform is well-suited for scalable transcription services, leveraging NLP models to convert speech into text efficiently.
4. Job queues and batch processing: Modal excels in managing high-performance computing tasks like batch jobs, enabling smooth execution of data analytics pipelines or large-scale simulations.
Modal empowers organizations to deploy AI models and deep learning applications with ease. Its serverless design reduces operational complexity while offering robust performance for data-intensive workflows. For businesses looking to streamline their AI development lifecycle, Modal provides a good alternative to traditional cloud platforms like Google Cloud or Microsoft Azure.
Etched
Here are some key features of Etched:
1. Transformer-specific ASIC: Sohu is purpose-built for transformer architectures, allowing it to achieve unprecedented efficiency and performance.
2. Impressive performance: Etched claims Sohu can process up to 500,000 tokens per second for Llama 70B, significantly outperforming NVIDIA's H100 GPUs.
3. High FLOPS utilization: By focusing solely on transformer models, Sohu achieves over 90% FLOPS utilization, compared to ~30% on general-purpose GPUs.
4. Scalability: An 8xSohu server reportedly replaces 160 H100 GPUs, offering substantial space and energy savings.
5. Advanced memory: Each Sohu chip features 144 GB of HBM3E memory, enabling support for massive AI models.
Best suited applications
1. Large language model inference: Sohu's architecture is optimized for running inference on transformer-based language models, making it ideal for powering chatbots and other NLP applications.
2. Transformer-based AI models: The chip's specialized design allows it to efficiently run a wide range of transformer models from companies like Google, Meta, Microsoft, and OpenAI.
3. High-throughput AI services: With its ability to handle large batch sizes without performance degradation, Sohu is well-suited for services requiring real-time AI processing at scale.
By sacrificing flexibility for raw performance, Sohu offers a compelling solution for organizations looking to scale their AI operations efficiently. However, this specialization also carries risks, as the chip's usefulness depends on transformers remaining the dominant AI architecture.
Comparative analysis
Let's dive into the nitty-gritty of how these AI-focused cloud providers stack up against each other.
Performance and scalability
When it comes to raw horsepower, these providers are all packing some serious muscle. CoreWeave's Kubernetes-native infrastructure allows for lightning-fast spin-up times, with new instances launching up to 35 times faster than traditional virtual machines. This is a game-changer for workloads that need to scale rapidly.
Lambda Labs, on the other hand, offers multi-GPU instances with up to 8 NVIDIA H100 Tensor Core GPUs, delivering mind-blowing performance for the most demanding AI tasks. Their HGX H100 and B200 GPUs are deployed with a whopping 3200 Gbps of bandwidth, ensuring your models train at breakneck speeds.
Cerebras takes a different approach with its Wafer-Scale Engine (WSE), packing an entire wafer's worth of compute into a single chip. We're talking over 2.6 trillion transistors and 850,000 AI-optimized cores. For certain workloads, this can offer unparalleled performance and scalability.
Modal and Foundry focus on automated scaling and real-time compute markets, respectively. This makes them particularly adept at handling fluctuating workloads and optimizing resource utilization on the fly.
Pricing models and cost-efficiency
Here's where things get interesting. CoreWeave claims to be up to 80% more cost-effective than major cloud providers, thanks to their efficient infrastructure and usage-based billing. They're like the savvy shopper of the AI cloud world, helping you squeeze every ounce of performance out of your budget.
Lambda Labs takes a more straightforward approach with transparent, competitive pricing. Their on-demand GPU clusters start at $1.25 per hour for A100 PCIe instances, with high-end H100 GPUs available at $2.49 per hour. This simplicity can be a breath of fresh air for teams that want predictable costs.
Cerebras, given its specialized hardware, likely comes with a heftier price tag. However, for the right workloads, the performance gains could more than justify the cost. It's like investing in a Formula 1 car - expensive, but unbeatable on the right track.
Modal and Foundry's pricing models are designed to optimize costs for serverless and real-time compute needs, respectively. They shine in scenarios where traditional pricing models might lead to overprovisioning or underutilization.
Ease of use and developer experience
CoreWeave's Kubernetes-native approach might have a steeper learning curve for teams not familiar with container orchestration. However, for those who are, it offers unparalleled flexibility and control.
Lambda Labs prides itself on its developer-friendly platform. Their cloud comes pre-configured with popular machine learning frameworks like TensorFlow and PyTorch, making it a turnkey solution for data scientists and AI researchers.
Cerebras, while offering incredible performance, likely requires specialized knowledge. It's not for the faint of heart, but for teams pushing the boundaries of AI, it could be a powerful tool.
Modal's serverless platform aims to simplify the development experience, allowing teams to focus on their AI models rather than infrastructure management. Foundry's real-time compute market offers a unique approach to resource allocation that could streamline operations for certain workloads.
Specialization vs. flexibility
Each provider strikes a different balance between specialization and flexibility. CoreWeave offers a broad range of GPUs and a flexible infrastructure, making it suitable for a wide variety of AI workloads. Lambda Labs specializes in AI and machine learning, with a focus on providing the latest NVIDIA GPUs and AI-optimized environments.
Cerebras is the most specialized of the bunch, with its custom-built WSE offering unparalleled performance for certain AI tasks. Modal and Foundry specialize in serverless and real-time compute, respectively, offering unique solutions for specific types of AI workloads.
Applications
CoreWeave shines in scenarios requiring rapid scaling and diverse GPU options. It's ideal for VFX rendering, large-scale simulations, and generative AI workloads that need to scale quickly.
Lambda Labs is perfect for teams focused on training and deploying large language models, developing cutting-edge generative AI, and tackling intensive machine learning tasks.
Cerebras excels in high-performance computing scenarios, particularly in fields like natural language processing, medical AI, and complex scientific simulations.
Modal is best suited for generative AI applications, computational biotech, and automated transcription services that benefit from its serverless architecture.
Foundry's real-time compute market makes it ideal for AI model training and fine-tuning, AI inference at scale, and hyperparameter tuning tasks that require dynamic resource allocation.
Choosing the right provider depends on your specific needs, budget, and the nature of your AI workloads. It's not just about raw performance or cost - it's about finding the perfect fit for your unique AI journey.
I also host an AI podcast and content series called “Pioneers.” This series takes you on an enthralling journey into the minds of AI visionaries, founders, and CEOs who are at the forefront of innovation through AI in their organizations.
To learn more, please visit Pioneers on Beehiiv.
Wrapping up
As the AI landscape evolves, businesses must carefully consider their choice of cloud provider. Key factors to weigh include:
1. Workload specificity: Match your AI tasks to the provider's strengths. For instance, CoreWeave excels in rapid scaling, while Cerebras shines in high-performance computing.
2. Cost-efficiency: Evaluate not just raw pricing, but performance-per-dollar. Google's TPU v5e and AWS Inferentia2 offer compelling options for budget-conscious organizations.
3. Scalability: Ensure the provider can grow with your needs. Lambda's multi-GPU instances and Google's TPU pods offer impressive scalability for large-scale AI projects.
4. Ecosystem integration: Consider how well the provider integrates with your existing tools and workflows. Azure's seamless integration with Microsoft services can be a significant advantage for some organizations.
5. Future-proofing: Look for providers investing in next-gen AI hardware, like NVIDIA's H200 GPU or custom ASICs, to stay ahead of the curve.
I’ll come back next week with more such comparisons and analysis.
Until then,
Ankur.