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Modal Review 2026 — Serverless GPU Cloud for AI Inference and Training
Modal is the serverless GPU cloud for AI developers. Run vLLM, Stable Diffusion, and model fine-tuning with Python decorators. Free $30/month credit. No DevOps required.
In inglese
Modal lets developers run Python-based workloads in the cloud without managing servers. It provides elastic compute, GPU access, autoscaling, logging, storage, scheduled functions, inference endpoints, training jobs, and isolated Sandboxes for untrusted code.
It is used by AI teams, researchers, and developers building inference services, model-training pipelines, batch workflows, coding agents, and data-processing applications. Usage is metered by compute and storage; paid plans add workspace fees and higher limits.
Features
- Deploy and autoscale LLM, audio, image, and video inference
- Run fine-tuning, reinforcement learning, and multi-node training jobs
- Execute batch and asynchronous workloads across GPUs
- Create isolated, ephemeral Sandboxes for untrusted code
- Define cloud applications and infrastructure in Python
- Use persistent Volumes, distributed queues, dictionaries, and Secrets
- Monitor functions, containers, and Sandboxes with logs and metrics
- Deploy applications through the Modal CLI and SDK
Use cases
- Deploy an autoscaling LLM inference API
- Fine-tune open-source models on rented GPUs
- Run large batch transcription or embedding jobs
- Execute coding agents in isolated cloud Sandboxes
- Launch parallel reinforcement-learning environments
- Process scientific or computational-biology workloads
Pros
Cons
Latest updates
- Network egress usage broken down by app
The Usage page now shows network egress per app.
- New controls for unauthenticated web endpoints
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.
- Zoom into custom time ranges in Container Metrics
Container Metrics now support zooming into custom time ranges with finer granularity.
Capabilities
- Agent that edits files — “Autonomous agents with the right tools, context, and credentials already in place — running securely in a full, isolated dev environment.” source
- Choice of models — “Run any model or inference engine on H100s, A100s, A10Gs and more.” source
- API — “Serve your own LLM API” source
- Official SDKs — “Modal SDK” source
- Runs models for you — “Deploy and scale inference for LLMs, audio, image/video generation.” source
- Builds agents and workflows — “Designed to scale agents.” source
- Traces and evaluates — “Debug fast by zooming into metrics, logs, and live statuses of specific inference calls.” source
Get it
Pricing
- Starting price
- $250/mo
- Prices checked
- 2026-09-25
Starter
Free
- $30 / month free compute
- 3 workspace seats included
- 100 containers + 10 GPU concurrency
- Scheduled and Web Functions (limited)
- Real-time metrics and logs
- Region selection
Team
- $250 + compute / month
- $100 / month free compute
- Unlimited seats
- 5000 containers + 50 GPU concurrency
- Unlimited Scheduled Functions
- Custom domains
- Static IP proxy
Enterprise
Price on request
- Volume-based discounts
- Unlimited seats
- Higher GPU concurrency
- Embedded ML engineering services
- Environment-level budgets
- Support via private Slack