CoreWeave vs Modal vs Anyscale For Distributed AI Workloads
Compare CoreWeave, Modal, and Anyscale for distributed AI workloads—exploring performance, scalability, cost efficiency, and best-suited applications.
Key Takeaways
CoreWeave offers a Kubernetes-native, high-performance infrastructure with NVIDIA GPUs and InfiniBand networking, excelling in AI model training, VFX rendering, and simulations.
Modal simplifies serverless AI development with dynamic autoscaling, Python-based workflows, and second-by-second billing, ideal for flexible GPU workloads and real-time applications.
Anyscale, powered by Ray, provides robust distributed computing with RayTurbo optimization, seamless hybrid cloud integration, and advanced orchestration tools like Apache Airflow.
Each platform targets specific AI workload demands: CoreWeave for sustained high-throughput tasks, Modal for bursty workloads, and Anyscale for complex distributed experiments.
Choosing the right platform depends on workload complexity, scalability needs, cost considerations, and integration with existing tools and frameworks.
In 2023, I started Multimodal, a Generative AI company that helps organizations automate complex, knowledge-based workflows using AI Agents. Check it out here.
Today, there is an unprecedented demand for distributed AI workloads as businesses seek to process vast datasets and deploy advanced models at scale.
From generative AI to real-time inference, these workloads require immense computational power, low latency, and seamless scalability. A recent survey revealed that 43% of new data centers are now dedicated to AI. Choosing the right platform is critical—not only to optimize performance and costs but also to ensure compatibility with specific workload requirements and long-term scalability.
Let’s compare CoreWeave, Anyscale, and Modal for distributed AI workloads and figure out which applications each works best for.
CoreWeave
Technical Highlights
CoreWeave is a Kubernetes-native cloud provider purpose-built for AI workloads, offering unmatched performance and flexibility. Its infrastructure is optimized for high-performance compute, enabling users to train and deploy AI models with speed and efficiency. Key features include:
Broad GPU Options: Access to NVIDIA’s latest GPUs, including H100 Tensor Core, ensures support for demanding AI applications like generative AI and large-scale simulations.
Rapid Resource Provisioning: Spin up instances in as little as 5 seconds, enabling on-demand scalability for developers and teams.
Specialized Networking: Powered by NVIDIA Quantum InfiniBand, CoreWeave delivers ultra-low latency and up to 400 GB/s throughput, critical for distributed training at scale.
AI Object Storage: High-speed storage provides up to 2 GB/s per GPU, minimizing bottlenecks during data-intensive training or inference tasks.
Best Suited Applications
CoreWeave excels in scenarios requiring massive scale and compute-intensive workloads. Its unified AI platform supports:
Large-scale AI model training and fine-tuning: Ideal for foundational model development or hyperparameter tuning.
Visual Effects (VFX) and Rendering: Accelerates artist workflows with real-time rendering capabilities.
Complex Simulations: Supports industries like financial analytics and life sciences with robust infrastructure for simulations.
Generative AI Workloads: Perfect for cutting-edge applications like LLMs or multimodal AI models requiring high scalability and reliability.
Modal
Technical Highlights
Modal redefines serverless computing by empowering developers to focus on code while it manages the infrastructure behind the scenes. Designed for flexibility, its features include:
Serverless Compute Platform: Run Python functions at scale without worrying about infrastructure complexity.
Autoscaling Capabilities: Dynamically adjusts resources based on workload demand, ensuring cost efficiency and high machine utilization.
High Resource Limits per Container: Supports up to 64 CPUs, 336 GB of memory, and 8 NVIDIA H100 GPUs, making it ideal for heavy-duty AI tasks like training or inference pipelines.
Second-by-second Billing: Scales to zero when idle, ensuring developers only pay for what they use.
Best Suited Applications
Modal is tailored for developers seeking simplicity without sacrificing power or scalability. It shines in:
Data-intensive Video Processing: Efficiently handles large-scale video analysis or transformation tasks.
Custom ETL Jobs and Periodic Tasks: Automates data pipelines with scheduled Python functions.
Flexible GPU Workloads: Supports diverse AI applications requiring GPU acceleration.
Real-time Production AI Workloads: Ideal for deploying web services or APIs that need consistent performance under varying loads.
Anyscale
Technical Highlights
Built on the open-source Ray framework, Anyscale transforms distributed computing with a focus on ease-of-use, scalability, and developer productivity. Highlights include:
RayTurbo Optimization: Enhances Ray’s performance with improved reliability, cost efficiency, and fault tolerance at scale.
Unified Development Environment: Anyscale Workspaces integrate seamlessly with tools like Jupyter Notebooks and VS Code, enabling developers to build, test, and deploy without context switching.
Customizable Deployments: Supports public clouds (AWS/GCP), on-premises setups, or Kubernetes clusters, giving users complete control over their infrastructure.
Built-in Observability Tools: Provides real-time monitoring and job scheduling to simplify management of distributed workloads.
Best Suited Applications
Anyscale is ideal for teams managing complex workflows across development and production environments. Its strengths include:
Distributed Data Processing with Ray Data: Scales Python-based pipelines effortlessly across nodes for efficient data handling.
Large-scale Model Training & Hyperparameter Tuning: Optimized for iterative experimentation at massive scale.
Serving Complex ML Applications: Handles high-volume requests with low latency using Ray Serve’s advanced capabilities.
Generative AI Workloads (LLM Fine-tuning): Supports fine-tuning large language models or retrieval-augmented generation (RAG) pipelines with precision orchestration tools like RayTurbo.
Comparison
Infrastructure Flexibility
When it comes to infrastructure flexibility, CoreWeave AI, Modal, and Anyscale each offer distinct advantages tailored to specific AI workloads.
CoreWeave excels with its Kubernetes-native architecture, enabling seamless orchestration of GPU resources across bare-metal nodes. This design is ideal for distributed training frameworks like PyTorch Elastic and Horovod, providing unparalleled control over resource allocation and scaling.
Modal, on the other hand, simplifies infrastructure management with its serverless platform, allowing developers to execute Python functions without worrying about provisioning or lifecycle management.
Anyscale stands out by leveraging Ray’s distributed computing capabilities to integrate seamlessly across public clouds, private clouds, or hybrid environments, offering unmatched flexibility for scaling AI models and workloads anywhere.
Performance and Scalability
For high-performance compute at scale, CoreWeave leads with its optimized GPU clusters featuring NVIDIA H100 GPUs and Quantum InfiniBand networking. These features significantly reduce communication overhead during distributed training, achieving up to 20% higher performance compared to general-purpose cloud providers.
Modal focuses on rapid scalability, enabling sub-second container starts and scaling to hundreds of GPUs in seconds—ideal for bursty workloads like generative AI inference or batch processing.
Anyscale leverages RayTurbo for enhanced autoscaling performance, launching clusters of thousands of nodes in under a minute while dynamically optimizing resource utilization.
Each platform enables massive scale but targets different workload demands: CoreWeave for sustained high-throughput tasks, Modal for flexible scaling, and Anyscale for distributed experiments and production pipelines.
Developer Experience
CoreWeave’s Kubernetes Service (CKS) provides developers with direct access to GPU-accelerated infrastructure while integrating tools like Slurm for efficient workload scheduling.
Modal simplifies the developer experience by abstracting away infrastructure complexities, allowing users to focus solely on writing code via Python decorators or pre-configured environments.
Anyscale takes developer experience further with its unified Workspaces environment, enabling seamless transitions from local development to cloud-scale deployments without modifying code.
All three platforms integrate popular frameworks such as TensorFlow and PyTorch, but Anyscale’s integration with Apache Airflow adds advanced workflow orchestration capabilities for managing complex pipelines.
Cost Optimization
Cost optimization is a priority across all platforms but is approached differently. CoreWeave achieves efficiency through purpose-built infrastructure that minimizes idle costs while maximizing GPU utilization during training and inference.
Modal employs a serverless pricing model that bills users by the second, ensuring cost efficiency for bursty workloads or batch processing tasks.
Anyscale leverages spot instances and dynamic cluster resizing to lower costs while maintaining reliability during peak demand. Additionally, Anyscale’s ability to optimize resource allocation across heterogeneous instance types (GPUs, TPUs) ensures customers only pay for what they need—making it particularly attractive for organizations managing diverse workloads.
Integration with Existing Tools
CoreWeave integrates seamlessly with Kubernetes-based workflows and distributed training frameworks like PyTorch Elastic, making it ideal for teams already leveraging containerized environments.
Modal supports direct deployment of custom models alongside popular frameworks like Stable Diffusion or GPT-J, offering pre-configured environments that work out-of-the-box.
Anyscale shines in its integration capabilities by embedding Ray into tools like Apache Airflow, enabling teams to orchestrate distributed tasks within familiar ecosystems while benefiting from RayTurbo’s performance optimizations.
These integrations reduce complexity and streamline workflows across development and production environments.
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.
Wrap Up
While all three platforms excel in supporting AI workloads at scale, the choice ultimately depends on specific requirements:
CoreWeave AI is best suited for high-throughput training and inference tasks requiring specialized infrastructure optimized for efficiency and performance.
Modal offers unmatched simplicity and flexibility for serverless applications like generative AI inference or batch processing.
Anyscale provides robust distributed computing capabilities ideal for teams managing complex workflows or scaling experiments across diverse infrastructures.
I’ll come back next week with more such insights and comparisons.
Until then,
Ankur.