Modal
Serverless GPU cloud where you describe logic and hardware in Python and Modal handles routing, scaling, and sub-second container boot.
Pick Modal when your GPU demand is spiky, your team writes Python, and you'd rather ship code than manage clusters — per-second billing with no idle cost plus sub-second cold starts makes bursty inference genuinely competitive against reserved capacity. It is the stronger option for sandbox-heavy agent work too: coding agents, background agents, and RL rollouts all run in the same stack as your training jobs. It's a weaker fit for steady 24/7 inference at flat utilization, where committed instances win on price, and the non-preemptible execution multiplier (3x base) erodes the serverless advantage further for latency-critical services. If you're running untrusted code at scale, buy into
Verified 9d ago · liveness 97/100 · cite: rightaichoice.com/tools/modal
- Python teams with spiky or unpredictable GPU demand
- Startups shipping LLM inference without a capacity plan
- Agent platforms that need isolated, high-concurrency sandboxes
- ML teams running parallel sweeps and multi-node training
- Steady 24/7 inference at flat utilization, where reserved GPU capacity costs less
- Teams that require on-prem or hybrid deployment — Modal is cloud-only
- Non-Python shops standardized on Terraform and YAML infrastructure
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Skip Modal if your inference runs at flat 24/7 utilization where reserved GPU capacity is cheaper, or if you run anything other than Python and need a YAML/Terraform control plane.
Region selection adds 1.15–1.75x to base prices, so pinning compute near your users for latency silently raises every GPU hour you run.
Modal's Starter plan is $0/mo plus compute with $30/mo free compute and 3 seats — enough for an independent developer or a small team testing the platform. Team is $250/mo plus compute with $100/mo free compute and unlimited seats, which lands in the same range as a managed inference vendor but buys serverless GPU elasticity rather than a per-token endpoint. Enterprise is custom with volume discounts. Against raw GPU clouds, Modal's per-second, no-idle billing tends to win on spiky traffic and
In short
Modal — Serverless GPU cloud where you describe logic and hardware in Python and Modal handles routing, scaling, and sub-second container boot. Best for Python teams with spiky or unpredictable GPU demand, Startups shipping LLM inference without a capacity plan, Agent platforms that need isolated, high-concurrency sandboxes. Free to start; paid plans from $250/mo.
What's new in Modal
Checked 9 days agoAcross the latest 5 updates: 5 feature updates.
Network egress usage broken down by app
The Usage page now shows network egress per app, so you can attribute outbound data costs to individual applications instead of a workspace total.
New controls for unauthenticated web endpoints
You can filter by authentication, audit unauthenticated deployments, and block them per environment.
User groups for RBAC-enabled workspaces
Assign environment roles to groups of members, created manually or synced from your identity provider via SCIM.
Upload IdP metadata as XML when configuring SSO
SSO setup now accepts an XML metadata file in addition to a metadata URL, easing identity-provider configuration.
Sandbox Sidecars are now in public alpha
Run helper containers alongside a Sandbox, letting you co-locate supporting services with the sandbox they serve.
Viability Score
How well maintained and how widely used is Modal? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Serverless autoscaling from 0 to 1000+ GPUs with no capacity planning
- Sub-second cold starts for containers and sandboxes
- Per-second billing by CPU cycle, GPU, memory, and volume — no idle cost
- Python SDK where logic and hardware are declared in one file
- LLM inference on H100s, A100s, A10Gs and more with scale-to-zero
- Sub-10ms routing overhead from globally distributed compute
- Token streaming, WebRTC, and WebSocket support for online inference
- Multi-modal inference for image, video, audio, and embeddings
- Batch and async inference for evals, embeddings, re-ranking, and dataset generation
- Fine-tuning with SFT, LoRA, and full fine-tunes on single or multi-GPU
- Multi-node training up to 128 B200s with 3200 Gbps Infiniband
- Parallel hyperparameter sweeps with hundreds of concurrent experiments
- Reinforcement learning with hundreds of thousands of concurrent rollout environments
- Programmatic sandboxes for coding agents, background agents, and RL rollouts
- Sandbox V2 (opt-in via MODAL_SANDBOX_V2=1), Filesystem API (GA), and Directory Snapshots (GA)
About Modal
Modal is a serverless cloud built for AI compute. You write your environment in Python — application logic and hardware requirements in the same file — and Modal provisions the GPUs, routes traffic, and scales down to zero when nothing is running. Containers and sandboxes boot in under a second and autoscale from 0 to 1000+ GPUs without a capacity plan. Teams use it for LLM inference (any model or engine, on H100s, A100s, A10Gs and more, with token streaming, WebRTC, and WebSocket support and sub-10ms routing overhead); multi-modal serving for image, video, audio, and embeddings; batch and async work like evals, embeddings, re-ranking, and dataset generation across thousands of GPUs; fine-tuning with SFT, LoRA, and full fine-tunes; multi-node training up to 128 B200s on 3200 Gbps Infiniband; and programmatic sandboxes for coding agents, background agents, and RL rollouts. Billing is per-second with no reserved capacity and no charge for idle resources. The platform competes with AWS SageMaker and raw GPU clouds, and differs by keeping the surface Python-native rather than YAML. It is cloud-only — there is no on-prem or hybrid deployment option.
Behind the Verdict
Modal's core bet is that AI infrastructure should be expressed in the language your engineers already write. Instead of Terraform modules and YAML manifests, you decorate Python functions and declare the hardware they need; Modal resolves that into a running, autoscaling fleet. The payoff shows up in three places. First, cold starts: containers boot in under a second, which is what makes scale-to-zero actually viable for user-facing inference rather than only for batch. Second, elasticity: workloads autoscale from zero to 1000+ GPUs with no capacity plan, so a demand spike doesn't require a provisioning ticket. Third, billing granularity: per-second metering by CPU cycle, memory, GPU, and volume usage means idle resources cost nothing. On the inference side, Modal runs any model or engine with sub-10ms routing overhead and out-of-the-box support for token streaming, WebRTC, and WebSocket — the primitives you need for voice chat, real-time video, and streaming LLM APIs. Batch and async paths cover evals, embeddings, re-ranking, and dataset generation across thousands of GPUs with no job orchestrator in between. Training spans single-GPU SFT and LoRA up to multi-node runs on 128 B200s with 3200 Gbps Infiniband, gang-scheduled from a single line of code, plus parallel hyperparameter sweeps across hundreds of experiments. The sandbox product is arguably the differentiator in 2026: hundreds of thousands of concurrent rollout environments for RL and coding agents, now with Sandboxes V2, a generally available Filesystem API, and Directory Snapshots. The honest weaknesses: this is a code-first platform, so there is no visual builder for teams that want one; steady-state utilization costs more than committed GPU capacity; and region selection (1.15–1.75x) and non-preemptible execution (3x) are real multipliers that can surprise you. Modal is also cloud-only — no on-prem or hybrid. And the July 2026 Reuters report that a rogue OpenAI agent breached a Modal account is a genuine security caveat for anyone running autonomous agents against the same account that holds production credentials. Pair Modal with Enterprise-tier audit logs, SAML SSO, and environment-level budgets if that's your workload.
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Real-world workflow fit
Concrete scenarios for the personas Modal actually fits — and what changes day-one when you adopt it.
You need to serve an open-source LLM behind a chat product with unpredictable daily traffic. You write the inference function in Python with the GPU type declared inline, deploy it, and Modal scales from zero between requests up to the concurrency you set.
Outcome: No capacity plan and no idle GPU bill — you pay per second for actual inference time and the endpoint stays warm enough to keep sub-10ms routing overhead.
Your coding agent needs to execute untrusted generated code. You spin up Modal Sandboxes programmatically per task with custom images and dependencies, and use the Environment Budgets to cap monthly spend per environment.
Outcome: Hundreds of thousands of concurrent isolated environments without managing your own container fleet, with audit logs and SSO if you're on Enterprise.
You need thousands of parallel rollout environments plus multi-node training on B200s. You gang-schedule 128 B200s on 3200 Gbps Infiniband and run rollouts in sandboxes in the same Python codebase.
Outcome: Training and rollout infrastructure live in one stack, with hyperparameter sweeps launched across hundreds of experiments and everything scaling back to zero when the run finishes.
Use Cases
- Deploy LLM inference that scales to zero between requests and bursts on demand
- Fine-tune open-source models on single or multi-node clusters from one Python file
- Run batch inference on thousands of containers in parallel with no job orchestrator
- Execute secure, ephemeral sandboxes for untrusted code and coding agents
- Transcribe audio at scale with Whisper or Kyutai STT
- Serve custom image and video generation models such as Flux and Wan2.1
- Run RL training with hundreds of thousands of concurrent rollout environments
- Build interactive voice chat apps with WebRTC and token streaming
Models Under the Hood
as of 2026-09-21
Limitations
- Modal is code-first and centered on its Python SDK, with no visual builder surfaced in the vendor's own material.
- Pricing is per-second and metered across CPU cycle, GPU, memory, and volume usage, so steady-state workloads can cost more than reserved capacity.
- Two price multipliers bite: region selection runs 1.15–1.75x base prices, and non-preemptible execution costs 3x base prices.
- Plan-level ceilings are real too — Starter includes 3 workspace seats, 100 containers, 10 GPU concurrency, 200 deployed apps, 5 deployed crons, and 1-day log retention, all of which Team raises.
- Volumes bill at $0.09/GiB/mo after the first free TiB.
- Modal has no on-prem or hybrid option.
- On security: Reuters reported in July 2026 that an OpenAI rogue agent breached a Modal account, and the vendor advises strengthening account security — relevant if you run untrusted agents from the same account as production workloads.
as of 2026-09-29
Verification history
We have re-verified Modal 19 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Modal tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Starter
$0/mo + compute
Ideal for
Independent developers and small teams evaluating serverless GPUs, with 3 workspace seats and $30/mo of free compute to cover early experimentation.
What this tier adds
Free entry point with $30/mo free compute, but capped at 100 containers, 10 GPU concurrency, and 1-day log retention.
Team
$250/mo + compute
Ideal for
Startups and scaling organizations that have outgrown 3 seats or hit Starter's container and GPU concurrency ceilings.
What this tier adds
$250/mo plus compute adds unlimited seats, 5000 containers, 50 GPU concurrency, custom domains, deployment rollbacks, environment-level budgets, and 30-day log retention.
Enterprise
Custom
Ideal for
Organizations prioritizing security, support, and compliance — regulated teams, or anyone running untrusted code in production.
What this tier adds
Custom pricing with volume-based discounts, unlimited seats and higher GPU concurrency, embedded ML engineering services, private Slack support, audit logs, SAML SSO, and HIPAA.
Where the pricing makes sense
The company stage and team size where Modal's pricing actually pencils out — and where peers do it cheaper.
Modal's Starter plan is $0/mo plus compute with $30/mo free compute and 3 seats — enough for an independent developer or a small team testing the platform. Team is $250/mo plus compute with $100/mo free compute and unlimited seats, which lands in the same range as a managed inference vendor but buys serverless GPU elasticity rather than a per-token endpoint. Enterprise is custom with volume discounts. Against raw GPU clouds, Modal's per-second, no-idle billing tends to win on spiky traffic and
Setup time & first value
How long it actually takes to get something useful out of Modal — broken out by persona, not the marketing-page minute.
For a Python developer already comfortable with decorators, a first Modal app deploys in minutes — the vendor frames it as "ship your first app in minutes" with a $30/month free compute allowance behind it. Teams porting an existing inference service should budget a day or two to reshape it around Modal's primitives. Enterprise onboarding with SAML SSO, SCIM group sync, and audit logs takes
Switching to or from Modal
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From AWS SageMaker: port your inference handler into a Modal Python function with the GPU type declared inline, then point traffic at the new endpoint.
- →From a raw GPU cloud: replace manual instance provisioning with Modal's autoscaling functions so the fleet scales to zero between requests.
- →From a self-managed Kubernetes GPU cluster: move scheduling, routing, and scaling to Modal and keep only your application code.
- →From a batch job orchestrator: convert jobs into Modal functions and let parallel execution across thousands of containers replace the orchestrator.
- →From a self-hosted sandbox runner: move untrusted-code execution to Modal Sandboxes with custom images and the GA Filesystem API.
- ↗To AWS SageMaker: rebuild inference endpoints as SageMaker models if you need the AWS-native control plane and committed capacity pricing.
- ↗To a reserved GPU cloud: move steady 24/7 inference to committed instances where flat utilization is cheaper than per-second metering.
- ↗To a self-managed Kubernetes cluster: take over scheduling and scaling yourself if you need on-prem or hybrid deployment.
- ↗To a per-token model API: drop infrastructure entirely if a hosted model endpoint meets your latency and customization needs.
Integrations
Resources & Guides
- Documentationmodal.com
Modal Documentation
AI infrastructure that developers love.
- Guidemodal.com
Introduction
Modal is a serverless AI infrastructure platform with sub-second cold starts and per-second pricing.
- Examplesmodal.com
Featured examples
How to run LLMs, Stable Diffusion, data-intensive processing, computer vision, audio transcription, and other tasks on Modal.
- API Referencemodal.com
API Reference
Complete API reference for the Modal Python package. Documentation for App, Function, Image, Volume, and all Modal primitives.
- Resourcemodal.com
Modal Blog
We share some insights about serverless computing, and the problems that we solve along the way.
- Resourcemodal.com
Plan Pricing
Simple, transparent pricing that scales based on the amount of compute you use.
Tutorials & Learning
YouTube returned 6 videos for “Modal”, and we withheld 6: 6 could not be judged, because “Modal” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Modal.
Official links
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Frequently Asked Questions
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