Modal

Modal is a serverless Python and GPU cloud built for AI teams: you decorate a function with @app.function(), run modal deploy, and your code is live behind an HTTPS endpoint with sub-second cold starts and autoscaling from zero to thousands of containers. We rate it 86/100 — the best developer experience in the serverless GPU category for Python-native teams, let down only by a pricing model whose regional and non-preemption multipliers make production bills in the US comfortably 3–4x the headline per-second rates.

What is Modal?

Modal was founded in January 2021 by Erik Bernhardsson (former Spotify and Better.com engineering leader, author of the Annoy nearest-neighbor library) and joined that August by co-founder and CTO Akshat Bubna. The company's pitch is "the cloud for AI, without the DevOps" — bring your Python, keep your laptop workflow, and let Modal handle containers, schedulers, GPU provisioning, storage and networking.

Under the hood Modal ships a custom container runtime, a lazy-loading filesystem and an intelligent scheduler that together deliver cold starts measured in hundreds of milliseconds even for multi-gigabyte model weights. The public SDK at github.com/modal-labs/modal-client is Apache-2.0 licensed and the modal-examples repo has crossed 1,100+ stars. On the business side, Modal raised a $7M Seed in 2022, a $16M Series A led by Redpoint Ventures, and an $87M Series B in July 2025. Named production customers include Ramp, Scale AI, Substack, Suno, Cohere and Quora/Poe.

Key Features of Modal

• Sub-second cold starts on GPU: Modal's custom lazy-loading filesystem pulls weights and dependencies on demand, so a container running a 70B Llama derivative can be serving traffic within roughly 1–2 seconds of a cold request — numbers most Kubernetes-based serverless competitors cannot match.
• Python-native, no Dockerfiles: Base images, system packages, pip/uv installs, secrets, volumes and schedules are declared as Python objects. You describe the environment in your app.py, Modal builds and caches layers for you.
• GPU fleet on demand: T4, L4, L40S, A10G, A100 (40/80 GB), H100, H200 and B200-class accelerators are available per-second with no minimum commitment — useful both for hobby inference and for 1,000-GPU batch jobs that would be impossible to rent elsewhere without a contract.
• Real scale-to-zero billing: You pay per second of function execution only. Idle containers and cold-start ramp-up are on Modal's dime, a distinction that matters a lot compared to always-on GPU instance rental on AWS or GCP.
• Sandboxes, Volumes and Queues: Beyond functions, Modal exposes gVisor-backed sandboxes (ideal for running LLM-generated code), distributed Volumes up to 16 TB with POSIX-like semantics, and Dicts/Queues for simple shared state — the pieces most teams would otherwise stitch together from S3 + Redis + ECS.
• Web endpoints and cron schedules: Any function can become an HTTPS endpoint (FastAPI-compatible) or a scheduled job via @app.schedule, with streaming support and automatic websocket termination at Modal's edge.

What Users Say About Modal

Sentiment on Hacker News and r/MachineLearning skews strongly positive, with the same phrase recurring: "I hate DevOps and just want to ship — Modal, it's not even close." Substack, Ramp and early Suno engineers have written publicly about picking Modal over AWS SageMaker and Google Vertex specifically because the iteration loop is tight enough to treat infra changes like normal code commits.

The honest complaints are real. The most-upvoted critique on the WaveSpeed and Blaxel comparison blogs is that Modal is great infrastructure but you still build the product yourself — there is no "one-click hosted Stable Diffusion" or managed fine-tuning service. Several threads also call out that regional multipliers (1.25x–2.5x) and the non-preemptible surcharge (up to 3x) stack to a combined 3.75x multiplier for a production US workload, which is rarely reflected in the marketing pricing page. GPU availability during peak H100 demand has been a second recurring pain point.

Modal Pricing

Modal uses a freemium model with per-second usage billed on top. Self-hosting is not an option — this is a managed cloud.

Plan	Price	Key Limits
Starter	$0/month	$30/month compute credits, 3 workspace seats, 100 containers, 10 concurrent GPUs
Team	$250/month	$100 included compute credits, unlimited seats, 1,000 containers, 50 concurrent GPUs
Enterprise	Contact	Dedicated capacity, SOC-2 & HIPAA, private cloud, SAML SSO, custom MSA

Indicative GPU rates, in US, preemptible: H100 ~$0.001097/sec (~$3.95/hr), A100 80GB ~$0.000694/sec (~$2.50/hr), A100 40GB ~$0.000583/sec (~$2.10/hr), L4 ~$0.000222/sec (~$0.80/hr), T4 ~$0.000164/sec (~$0.59/hr). Regional multipliers apply outside the base region, and non-preemptible commitments carry up to a 3x surcharge.

Who Should Use Modal?

Best for: AI engineers, ML researchers and small-to-mid infra teams shipping inference, fine-tuning, batch ETL or agent workloads who want to keep a Python-first mental model and avoid owning a Kubernetes cluster. Especially strong for LiteLLM-style routers, Bolt-style code execution backends, and any workload that bursts from zero to hundreds of GPUs unpredictably.

Not ideal for: teams with steady 24/7 GPU utilization (a reserved A100 on Lambda Labs or CoreWeave will beat Modal on cost), workflows that are not Python, or companies whose compliance posture forbids routing training data through a third-party cloud — Modal does not offer a self-hosted control plane.

Pros and Cons

Pros:

• Best-in-class cold starts on serverless GPU, consistently measured in the 1–2 second range on real reviewer benchmarks.
• Everything-is-Python developer ergonomics — no YAML, no Dockerfile, no Terraform for the basics.
• Apache-2.0 SDK and permissive open-source modal-examples repo for real-world patterns.
• Generous $30/month free compute is enough to prototype a small AI product end-to-end.
• Customer list (Ramp, Suno, Substack, Cohere) validates the platform at production scale.

Cons:

• Real-world US production pricing is 3–4x the headline per-second numbers once multipliers stack.
• No self-host path — a hard stop for some regulated industries.
• GPU availability can get tight during H100 demand spikes, despite a spot-fallback mechanism.
• You still build the product: no managed Stable Diffusion, no managed fine-tuning-as-a-service.
• Python-only — JavaScript/Go teams have to wrap their logic in a Python entrypoint.

Alternatives to Modal

RunPod Serverless is the most direct competitor and often cheaper on raw GPU hours, but its developer experience is rougher and cold starts for large models lag. Beam and Cerebrium target the same Python-serverless niche with slightly different trade-offs — Cerebrium in particular competes hard on price. For steady 24/7 GPU workloads, Lambda Labs and CoreWeave reserved instances are cheaper. Teams who want managed inference rather than infrastructure tend to pick Together AI, Fireworks, or OpenRouter for pure LLM serving.

Verdict: Is Modal Worth It?

For a Python-first AI team that values iteration speed more than the last dollar of GPU cost, yes — Modal is the clearest pick in the serverless GPU category today. The $30/month free credits let you build a real product before paying anything, and the SDK is genuinely good enough that most teams stop wishing for a Dockerfile. If you already operate Kubernetes at scale and run GPUs 24/7, Modal's premium over reserved hardware will not pay for itself. We land at 86/100: a few points docked for the opaque multiplier pricing and the lack of any self-host option, but otherwise the most complete serverless compute product shipping in 2026.

Frequently Asked Questions

Is Modal free?

Yes. The Starter plan includes $30 of compute credits every month at no cost — enough to run meaningful GPU inference and batch jobs for prototyping. Paid plans start at $250/month for teams who need higher concurrency limits.

What GPUs does Modal support?

Modal offers per-second access to T4, L4, L40S, A10G, A100 (40GB and 80GB), H100, H200 and B200-class GPUs across US and EU regions. You select the GPU type directly in code, e.g. gpu="H100".

How does Modal compare to RunPod?

Modal wins on developer experience, cold-start latency and integrated storage/queue primitives; RunPod typically wins on raw GPU hourly price and has a broader community template marketplace. Many teams prototype on Modal and later move steady workloads to RunPod reserved capacity.

Is Modal open source?

The Python SDK at github.com/modal-labs/modal-client is Apache-2.0 licensed, as is the modal-examples repo. The platform itself is proprietary and managed — there is no self-host control plane.

Who uses Modal in production?

Named production customers include Ramp, Scale AI, Substack, Suno, Cohere and Quora/Poe. Modal has also publicly stated it is the top infrastructure pick inside the Vercel AI Accelerator.