Modal

Serverless GPUs, sandboxes and inference, driven from Python

Agents & Automation Usage_based Has API
Researched · Published · Reviewed
RECATOOLS Score
7.8 / 10
Capability
8
Value for money
7
Ease of use
8
ASEAN readiness
5
API quality
8
Founded
2021
HQ
New York, USA

Overview

Modal is a serverless cloud for AI workloads: developers declare containers and GPUs in Python and Modal handles images, scheduling and scale-to-zero. It runs inference, training, batch jobs and isolated sandboxes for agent-written code, billed per second. Starter is free with $30 of compute a month; Team is $250 a month plus usage.

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Pricing

Pricing shown for reference only. These figures reflect RECATOOLS research as of 14 Oct 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.

Team
$250/mo + compute
$100 of compute credit a month
  • Unlimited seats
  • 5,000 containers, 50 GPU concurrency
  • Custom domains, static IP proxy, deployment rollbacks, RBAC
Enterprise
Custom
Get in touch
  • Volume-based discounts
  • Audit logs, Okta SSO and HIPAA
  • Support via private Slack; AWS and GCP marketplace spend
GPU compute
From $0.000164/sec
T4; L4 $0.000222, A100 80GB $0.000694, H100 $0.001097, B200 $0.001736, B300 $0.001972 a second
  • CPU $0.0000131 per physical core per second
  • Memory $0.00000222 per GiB per second
  • Region pinning 1.15-1.75x; non-preemptible 3x

Use cases

Model training Fine-tuning Inference workloads Batch ML

What you can produce with Modal

  • A Python function deployed to cloud GPUs with a decorator, with no Dockerfile or Kubernetes
  • Scale-to-zero inference endpoints billed only for the seconds they run
  • Fine-tuning and training jobs on A100, H100 or B200 GPUs, including multi-node Modal Clusters
  • A batch job fanned out across hundreds of parallel containers with a single map call
  • Isolated sandboxes, or full Linux VM sandboxes, for running code written by AI agents
  • Scheduled cron jobs and web endpoints in the same Python codebase as GPU functions
  • Persistent volumes and secrets attached to functions for model weights, datasets and keys
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RECATOOLS Verdict

Modal remains the cleanest way to get Python code onto GPUs without touching Kubernetes: decorate a function, name a GPU, and it scales up in seconds and back to zero. Per-second billing, a $30 monthly credit and strong sandbox primitives make it a good fit for bursty inference, fine-tuning, batch jobs and agent code execution.

Watch the multipliers: pinning a region adds 15% to 75%, non-preemptible runs cost three times base, and hosted Shared Endpoints bill per token outside the credit. An ap-southeast region exists for ASEAN latency, but no data-residency commitment is published. Steady 24/7 workloads may be cheaper on reserved GPUs.

Desk research by RECATOOLS from public sources, written with AI assistance — not a hands-on test. How we review

What we found

Modal is a serverless cloud for AI workloads, run by Modal Labs, Inc. from New York, with engineers in Stockholm, staff in San Francisco and, since September 2026, a sales office in London. Developers write ordinary Python, add decorators to declare containers and GPUs, and Modal builds the images, schedules the work and scales to zero when it stops. Its pricing page says containers start in one to two seconds, and its Series C post credits GPU memory snapshotting with cutting cold starts a hundredfold. Those are Modal's own figures, not measured here.

The product now reaches well past functions. Sandboxes, isolated environments for running code that agents write, make up more than a third of revenue by Modal's account. At its first Runtime conference on 1 October 2026 it added VM Sandboxes that give an agent a full Linux machine, made multi-node Modal Clusters generally available and previewed Endpoint Candidates for testing serving setups against mirrored traffic. In July 2026 an agent intrusion reported by Hugging Face named Modal as the launchpad; Modal said a customer had exposed an unauthenticated code-execution endpoint and that its own isolation held.

Billing is by the second. A T4 costs $0.000164 a second and an H100 $0.001097, about $3.95 an hour, rising to $0.001972 for a B300. Starter is free with $30 of compute a month and three seats; Team is $250 a month with $100 of compute and unlimited seats. Pinning containers to a region costs 1.15 to 1.75 times base, and the options include ap-southeast, which earlier versions of this page said did not exist. Non-preemptible execution costs three times base. Since 1 September 2026 Shared Endpoints, Modal's hosted models, are billed per token from the first request, outside the monthly credit.

Modal raised $355 million at a $4.65 billion valuation in May 2026, led by General Catalyst and Redpoint, and says annualised revenue passed $300 million. It suits spiky GPU demand; steady round-the-clock loads should price reserved capacity too.

Compiled by RECATOOLS from public sources: vendor documentation and pricing, code repositories and published reporting.

About this listing

Researched on
Published on
Last reviewed

This entry was compiled from publicly available data including Modal's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Modal unless explicitly stated.

Data accuracy

Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.

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