FluidStack vs Modal Comparison
Detailed comparison of features, pricing, and capabilities
Last updated May 1, 2026
Overview
Compare key metrics and features at a glance
FluidStack
https://www.fluidstack.io
FluidStack is a cloud GPU infrastructure provider that aggregates underutilized GPU capacity from data centers worldwide to offer on-demand and reserved GPU compute at competitive prices. The platform enables AI companies, researchers, and developers to access large-scale GPU clusters for training and inference workloads, including support for high-performance interconnects like InfiniBand. FluidStack differentiates itself by sourcing capacity from a distributed network of partner data centers, providing cost-effective alternatives to hyperscale cloud providers for AI/ML workloads.
Modal
https://modal.com
Modal is a cloud infrastructure platform that allows developers and data scientists to run code in the cloud without managing servers or infrastructure. It provides a Python-native interface for running serverless functions, training machine learning models, and deploying AI applications with on-demand GPU and CPU compute. Modal handles scaling, containerization, and dependency management automatically, enabling teams to go from local code to production cloud workloads with minimal configuration.
Quick Comparison
| Detail | FluidStack | Modal |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | Contact Sales | Free |
| Plans Available | 1 | 3 |
| Features Tracked | 16 | 20 |
| Founded | 2019 | 2021 |
| Headquarters | London, United Kingdom | New York, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| core | ||
| Automatic Dependency Management | ||
| Batch Job Processing | ||
| Cron Jobs | ||
| Custom Container Runtime | ||
| Dedicated GPU Clusters | ||
| Fully Managed Clusters | ||
| GPU-Backed Notebooks | ||
| H100/H200/B200/GB200 Support | ||
| High-Throughput Storage System | ||
| InfiniBand Interconnects | ||
| Kubernetes Support | ||
| Low-Latency Inference | ||
| Model Training and Fine-tuning | ||
| Multi-Cloud GPU Pool | ||
| Python-Native Code Definition | ||
| Rapid Deployment | ||
| Scale to Zero Pricing | ||
| Serverless GPU Inference | ||
| Slurm Support | ||
| Transparent Pricing | ||
| Web Endpoints | ||
| custom | ||
| Custom Data Centers | ||
| integration | ||
| Cloud Bucket Integration | ||
| Distributed Data Access | ||
| External Database Connectivity | ||
| Key-Value Dictionaries | ||
| Networking Tools | ||
| Persistent Volumes | ||
| Task Queues | ||
| security | ||
| Sandboxes for Untrusted Code | ||
| Secure Access Controls | ||
| Single-Tenant Isolation | ||
| support | ||
| 15-Minute Response SLA | ||
| 99% Uptime SLA | ||
| Integrated Logging and Monitoring | ||
| Proactive Monitoring | ||
Pricing
Compare pricing plans and value for money
FluidStack
Contact Sales
Best For
AI companies and researchers needing rapid, cost-effective, fully managed large-scale dedicated GPU clusters for training without hyperscaler lock-in.
Modal
From $0/mo
Price Components
- base_fee: $0/month (30 included)
- seats: $0/user (3 included)
- CPU: $0.0000131/core-second
- Memory: $0.00000222/GiB-second
- Nvidia B200: $0.001736/second
Best For
Python-focused ML teams and startups needing rapid GPU-accelerated model training and inference without managing Kubernetes, containers, or infrastructure scaling.
Integrations
See which third-party services are supported
Supported Integrations
Coming Soon
Integration comparison data for FluidStack, Modal is being collected and will be available soon.
Strengths & Limitations
Key strengths and limitations of each service
FluidStack
AI companies and researchers needing rapid, cost-effective, fully managed large-scale dedicated GPU clusters for training without hyperscaler lock-in.
- Rapid deployment of multi-thousand GPU clusters in as little as 48 hours with zero-setup management.
- Single-tenant isolation at hardware, network, and storage levels eliminates noisy neighbors unlike hyperscalers.
- Supports latest NVIDIA H100/H200/B200/GB200 GPUs with InfiniBand and 99% uptime SLA.
- 24/7 engineering support via Slack with 15-minute response times and proactive monitoring.
- Enterprise-only pricing requires contacting sales, lacking transparent pay-as-you-go rates.
- Small team of 11-50 employees and seed funding may limit scalability versus larger competitors.
- Aggregated capacity from partner data centers could introduce variability in global availability.
Modal
Python-focused ML teams and startups needing rapid GPU-accelerated model training and inference without managing Kubernetes, containers, or infrastructure scaling.
- Python-native serverless platform eliminates manual containerization and dependency management, reducing deployment friction for ML engineers and data scientists
- On-demand access to high-performance GPUs (A100, H100) with per-second billing removes upfront infrastructure costs and commitment lock-in common with traditional cloud providers
- Automatic horizontal scaling to thousands of parallel containers with zero-to-scale capability enables cost-efficient handling of bursty AI workloads without manual orchestration
- Limited to Python ecosystem, excluding teams using Go, Node.js, or other languages that dominate in serverless and edge computing markets
- Series B funding and 11-50 employee count signal smaller scale and fewer enterprise resources compared to hyperscalers (AWS, Google Cloud, Azure) controlling 65% of AIaaS market revenue
Company Info
Company details and background
FluidStack
Modal
Comparison FAQ
Common questions about comparing FluidStack and Modal
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