Fly.io vs Modal Comparison
Detailed comparison of features, pricing, and capabilities
Last updated May 1, 2026
Overview
Compare key metrics and features at a glance
Fly.io
https://fly.io
Fly.io is a platform that allows developers to deploy applications closer to their users by running them in micro-VMs on a global network of servers. It transforms Docker containers into micro-VMs that can run anywhere in the world, providing better performance through edge computing and simplified deployment processes. The platform specializes in running full-stack applications globally with minimal latency.
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 | Fly.io | Modal |
|---|---|---|
| Category | Platform as a Service (PaaS) | AI Cloud Infrastructure |
| Starting Price | Free | Free |
| Plans Available | 5 | 3 |
| Features Tracked | 16 | 20 |
| Founded | 2017 | 2021 |
| Headquarters | San Francisco, USA | New York, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| api | ||
| Fly Machines API | ||
| core | ||
| Auto-Scaling | ||
| Automatic Dependency Management | ||
| Batch Job Processing | ||
| Cron Jobs | ||
| Custom Container Runtime | ||
| Docker Container Deployment | ||
| Firecracker MicroVMs | ||
| Fly Launch | ||
| GPU-Backed Notebooks | ||
| Global Anycast Networking | ||
| High-Throughput Storage System | ||
| Managed Postgres | ||
| Managed Redis | ||
| Model Training and Fine-tuning | ||
| Multi-Cloud GPU Pool | ||
| Multi-Region Deployment | ||
| Multiple Language Support | ||
| Persistent Volumes | ||
| Private Network | ||
| Python-Native Code Definition | ||
| Scale to Zero Pricing | ||
| Serverless GPU Inference | ||
| Web Endpoints | ||
| integration | ||
| CI/CD Integration | ||
| Cloud Bucket Integration | ||
| External Database Connectivity | ||
| Key-Value Dictionaries | ||
| Networking Tools | ||
| Persistent Volumes | ||
| Task Queues | ||
| security | ||
| Sandboxes for Untrusted Code | ||
| Secrets Management | ||
| support | ||
| Integrated Logging and Monitoring | ||
| Prometheus Metrics | ||
| flyctl CLI | ||
Pricing
Compare pricing plans and value for money
Fly.io
From $0/mo
Price Components
- Compute (shared-cpu-1x 256MB): $0.0028/hour
- Persistent Volumes: $0.15/GB
- Volume Snapshots: $0.08/GB (10 included)
- Data Egress (NA/Europe): $0.02/GB
- Dedicated IPv4: $2/address
Best For
Developers and startups building latency-sensitive, full-stack or stateful apps like global APIs and databases that need edge deployment without vendor 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 Fly.io, Modal is being collected and will be available soon.
Strengths & Limitations
Key strengths and limitations of each service
Fly.io
Developers and startups building latency-sensitive, full-stack or stateful apps like global APIs and databases that need edge deployment without vendor lock-in.
- Deploys Docker containers as micro-VMs across global edge locations with Anycast networking for sub-100ms latency, outperforming centralized PaaS like Heroku.
- Supports stateful apps via Fly Volumes for persistent NVMe storage and managed Postgres with automated scaling, unlike ephemeral serverless competitors.
- Generous free Hobby tier covers compute, storage, and Anycast IPs with pure pay-as-you-go pricing, avoiding minimum fees of Render or DigitalOcean.
- 7th most popular deployment platform with 2.5% market share and 3M+ apps launched, backed by Series C funding and $11.2M 2024 revenue.
- Managed Postgres requires more hands-on management than fully managed DBaaS from AWS or DigitalOcean, lacking complete 'hands-off' automation.
- Enterprise support starts at $2500/month, pricier than Render's $29/user organization plans for similar scale.
- Smaller team of 51-200 employees may limit rapid feature development versus hyperscalers like AWS.
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
Fly.io
Modal
Comparison FAQ
Common questions about comparing Fly.io and Modal
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