Modal vs FluidStack: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Modal and FluidStack. Updated July 2026.
Provider Overview
Strengths & Best For
Modal is a serverless GPU cloud that lets Python developers run H100, A100, and T4 workloads with a simple decorator-based API and zero infrastructure management — cold starts measured in seconds. Per-second billing means you only pay for actual compute time, making it highly cost-efficient for bursty AI inference, LLM serving, and batch ML jobs. The go-to on-demand GPU cloud for ML engineers who want to ship fast without touching DevOps.
- Zero infra management
- Instant cold starts
- Python-native API
- Per-second billing
FluidStack aggregates H100, A100, and consumer GPU capacity from data centers across the US and EU, offering competitive bulk pricing and flexible contracts for AI training and LLM fine-tuning workloads. Spot GPU rental is available alongside on-demand and reserved options, making it a cost-effective choice for teams with variable compute needs. A strong pick for EU-based teams wanting broad GPU availability without committing to a single provider.
- Competitive pricing
- EU/US coverage
- Spot availability
- Flexible contracts
Live GPU Pricing
Region Coverage
Popular Comparisons
Modal — specialist provider
Modal is a serverless GPU cloud that lets Python developers run H100, A100, and T4 workloads with a simple decorator-based API and zero infrastructure management — cold starts measured in seconds. Per-second billing means you only pay for actual compute time, making it highly cost-efficient for bursty AI inference, LLM serving, and batch ML jobs. The go-to on-demand GPU cloud for ML engineers who want to ship fast without touching DevOps.
FluidStack — specialist provider
FluidStack aggregates H100, A100, and consumer GPU capacity from data centers across the US and EU, offering competitive bulk pricing and flexible contracts for AI training and LLM fine-tuning workloads. Spot GPU rental is available alongside on-demand and reserved options, making it a cost-effective choice for teams with variable compute needs. A strong pick for EU-based teams wanting broad GPU availability without committing to a single provider.
Billing model comparison
Modal uses a Per-second serverless billing model with a minimum commitment of None. FluidStack uses On-demand, Spot, Reserved billing with a None minimum. Both providers offer flexible billing options — compare the live pricing table above to find the best rate for your specific GPU model and workload duration.
Which workloads each provider suits best
Modal is best suited for: ML engineers, Serverless inference, Rapid prototyping, Python-first teams. Its key strengths are zero infra management, instant cold starts, python-native api. FluidStack is best suited for: Cost-sensitive training, EU-based teams, Flexible workloads. Its key strengths are competitive pricing, eu/us coverage, spot availability. Both providers target similar workload profiles — the live pricing table above is the most reliable way to determine which offers better value for your specific GPU model and region requirements.
Support tiers and region coverage
Modal offers Community → Enterprise support across 2 regions (US-East, US-West). FluidStack offers Standard → Enterprise support across 4 regions (US-East, US-West, EU-West and 1 more). FluidStack's broader region footprint gives it an advantage for latency-sensitive workloads or teams with data residency requirements in specific geographies.
Provider background: Modal vs FluidStack
Modal was founded in 2021 and is headquartered in New York, NY. FluidStack was founded in 2019 and is headquartered in London, UK. FluidStack has 2 years more operational history than Modal, which may matter for teams evaluating provider stability and long-term contract risk. Use the live pricing table above to compare current on-demand and spot rates for specific GPU models, and the region map to verify coverage in your target geography.