Modal vs Together AI: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Modal and Together AI. 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
Together AI provides dedicated H100 and A100 GPU clusters with fast networking, purpose-built for open-source LLM training, fine-tuning, and high-throughput AI inference. On-demand GPU cloud access is paired with a developer-friendly platform that supports popular open models out of the box, reducing time-to-deployment for AI teams. A strong choice for startups and researchers who want managed GPU infrastructure without hyperscaler overhead.
- Inference-optimized
- Open-source LLM support
- Fast networking
- Developer-friendly
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.
Together AI — specialist provider
Together AI provides dedicated H100 and A100 GPU clusters with fast networking, purpose-built for open-source LLM training, fine-tuning, and high-throughput AI inference. On-demand GPU cloud access is paired with a developer-friendly platform that supports popular open models out of the box, reducing time-to-deployment for AI teams. A strong choice for startups and researchers who want managed GPU infrastructure without hyperscaler overhead.
Billing model comparison
Modal uses a Per-second serverless billing model with a minimum commitment of None. Together AI uses On-demand, 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. Together AI is best suited for: LLM inference, Fine-tuning open models, AI startups. Its key strengths are inference-optimized, open-source llm support, fast networking. 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). Together AI offers Community → Enterprise support across 2 regions (US-East, US-West). Both providers have comparable region coverage — choose based on which specific regions overlap with your user base or data residency requirements.
Provider background: Modal vs Together AI
Modal was founded in 2021 and is headquartered in New York, NY. Together AI was founded in 2022 and is headquartered in San Francisco, CA. Modal has 1 years more operational history than Together AI, 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.