Execute millions of parallel tasks with automatic scaling, serverless GPU support, pay-per-use pricing, and zero infrastructure management. Serverless Fleets makes large-scale parallel computing effortless.
Access powerful GPU acceleration without managing infrastructure. Deploy GPU workers on-demand for machine learning, scientific computing, and compute-intensive workloads.
fleet create ml-training \
--image registry.io/ml-model:latest \
--gpu nvidia-tesla-v100 \
--gpu-count 4 \
--tasks 1000 \
--max-concurrent 50
# Serverless GPUs provision automatically
# No cluster management required
# Pay only for GPU seconds usedA fully managed platform for running large-scale parallel workloads. Automatically provisions and scales worker nodes (including GPUs) to execute your containerized tasks efficiently, from a single task to millions in parallel.
Dynamically provisions worker nodes based on your workload requirements. Scale from single tasks to millions without manual intervention.
Deploy GPU-accelerated workers on-demand without infrastructure setup. Perfect for ML training, scientific computing, and compute-intensive workloads.
Pay-per-use pricing model charges only for actual compute resources consumed. No idle costs, no upfront commitments.
Built-in security features including encryption at rest and in transit, IAM integration, and compliance with industry standards.
Simple CLI and API interfaces. Integrate with your existing CI/CD pipelines and development workflows seamlessly.
Deploy your fleets across multiple regions worldwide. Low-latency access and high availability for your critical workloads.
Train ML models at scale with serverless GPU workers. Hyperparameter tuning, distributed training, and model evaluation across thousands of configurations.
Process massive datasets in parallel. ETL operations, data transformations, and analytics pipelines that scale automatically with your data volume.
Run simulations, molecular modeling, and computational research with serverless GPUs. Scale your scientific workloads without infrastructure constraints.
Transcode videos, process images, and generate thumbnails at scale. Handle media workflows with parallel processing for faster turnaround.
Get started with Serverless Fleets in four simple steps
Package your application as a container image and push it to a registry. Use any language or framework you prefer.
docker build -t my-fleet-app .
docker push registry.io/my-fleet-app:latestSpecify the tasks you want to execute, resource requirements (including GPUs), and concurrency settings.
fleet create my-fleet \
--image registry.io/my-fleet-app:latest \
--cpu 2 --memory 4G \
--gpu nvidia-tesla-v100 \
--max-concurrent 100Serverless Fleets automatically provisions worker nodes (including GPUs) and distributes your tasks for optimal performance.
You're charged only for the actual compute time consumed by your tasks. No idle costs, no infrastructure overhead.
Only pay for the compute resources your fleets actually consume. No upfront costs, no minimum fees, no idle charges.
| Resource | Unit | Price |
|---|---|---|
| vCPU | per second | $0.00003431 |
| Memory | per GB second | $0.00000356 |
| GPU (Serverless) | per hour | Variable by GPU type |
Running 100 tasks, each requiring 2 vCPU and 4 GB memory, with an average runtime of 0.5 seconds:
Launch your first fleet in minutes with our step-by-step guide. No prior experience required.
Start NowComprehensive documentation covering all features, APIs, and best practices for Serverless Fleets.
Read DocsExplore real-world examples and tutorials on GitHub. Learn from working code and best practices.
View on GitHubJoin thousands of developers using Serverless Fleets to run parallel workloads at scale with serverless GPU support. Start with our free tier today.