serverless inference · serverless · Layer 2.5
Modal
Python teams who want infrastructure to disappear and are happy writing to a framework to get it.
low portability Sticky. Leaving means rewriting against a different interface, not changing a hostname.
What you are buying
- Business model
- You never see a GPU. You send a request and pay per token or per second of execution.
- How you reach it
- You supply code; scaling and idle time are the provider's problem.
- Accelerators
- H100, A100, L40S, T4, and others
- Regions
- US, EU
- Pricing model
- Per second of GPU time, scale to zero, no idle charge.
- Getting started
- Self-serve, generous free tier historically.
- Capacity
- Managed pool.
- Ownership
- Private.
Verified 2026-09-09. Fields reading “not published” are exactly that — we do not estimate a figure a vendor withholds.
The counterparty
Low financially; high in code. Modal's programming model is genuinely delightful and genuinely non-portable — that is the trade.
A multi-year GPU commitment is a credit decision wearing a cloud contract. This is the section no benchmark covers and the one that decides what happens to your workload in 2028.
The catch
Your deployment becomes Modal-shaped. Migrating off is a rewrite, not a redeploy. Price that in on day one, not year two.
The economics
Per-second billing with no idle cost is the cheapest possible shape for bursty inference. It is the wrong shape for a job that runs continuously.
No rate is quoted on this page on purpose. Published list prices at this layer move weekly and essentially nobody signing a real contract pays them. Dated, sourced figures live in the GPU cost calculator, where the cheapest and dearest published on-demand price for the identical H100 sit 5.6× apart.
The layers underneath this one
Whatever you rent here is a chip in a building that needs power. In 2026 megawatts, not silicon, are the binding constraint on the whole industry — a frontier rack draws 120–200 kW against a 2026 average near 27 kW, and the US interconnection queue exceeds 2,600 GW.
Layer 2 — Silicon · Layer 1 — Energy · The interconnection queue · Tokens per watt
Compared against
- Modal vs Baseten — Scale-to-zero, and which one's cold start you can live with.Compare with Baseten
- Replicate vs Modal — Ship a feature in an afternoon, or own the deployment model.Compare with Replicate
- Fly.io vs Modal — Fly.io retired its GPUs on 31 Jul 2026; Modal still rents them, billed by the second.Compare with Fly.io
Related providers
- Baseten — serverless inference, low portability
- Fireworks AI — serverless inference, low portability
- Replicate — serverless inference, low portability
- Together AI — serverless inference, low portability
- Akash Network — marketplace, medium portability
- AWS (P5, P6, G6) — hyperscaler, medium portability