Serverless cloud infrastructure for AI and data workloads
Estimated monthly website visits
Similarweb estimates for this domain, not active users. Only available observation months are shown; collection date is separate from the traffic period.
Collected Nov 11, 2026
This is our editors' assessment out of 5, not an average of user ratings. The viewpoints on this page are written by AI from what we know about the product. They are not customer reviews.
According to OpenAITools.app telemetry verified on 2026-11-11, Modal (modal.com) is an AI platform indexed under AI Developer Tools generating 1.2M monthly visits with a month-over-month traffic delta of +277.18K (+30.2%). Its primary geographic market is United States (43.9% traffic share), and it operates on a Freemium, Contact for Pricing pricing model. Top competing alternatives by monthly volume include Cerebrium (26.97K visits/mo), RunPod (2.88M visits/mo), Replicate AI (904.04K visits/mo).
Serverless GPU compute done right.
Scaling from zero to 100 A100s in seconds is magic.
Python decorator syntax makes deployment effortless.
Best AI infrastructure platform out there.
AI-synthesized perspectives grounded in live site inspection, pricing structure, and category benchmarks — presented as editorial viewpoints rather than individual customer testimonials.
Write plain Python, attach GPUs or containers, and deploy HTTPS endpoints that autoscale with traffic and scale to zero when idle.
Launch isolated, secure environments for coding agents, RL rollouts, and untrusted code, with sub-second starts and support for over a million concurrent sandboxes.
Run fine-tunes, LoRA, full training, and reinforcement learning on elastic single or multi-node GPU capacity with no quota requests.
Serve LLMs and image, video, or audio models through OpenAI-compatible endpoints, or bring your own inference stack and let Modal handle the infrastructure.
A distributed filesystem loads large images and model weights at full bandwidth, while a pool spanning 20+ clouds routes workloads to the right hardware and region.
Modal provides cloud infrastructure aimed squarely at AI and data teams. The core idea is that developers write ordinary Python, decorate functions or define container images, and Modal takes care of the underlying compute: scheduling containers, attaching GPUs, autoscaling from zero and back, and exposing endpoints over HTTPS. Infrastructure concerns like networking, storage, and capacity management are abstracted away, so a workload that runs on a laptop can run the same way across a fleet.
The platform is organised around a few primitives. Functions handle serverless compute and can fan out across thousands of GPUs for parallel or batch work. Sandboxes provide isolated environments for coding agents, reinforcement learning rollouts, and other untrusted code, with sub-second start times and support for very high concurrency. Training covers fine-tuning, reinforcement learning, and evaluation on single or multi-node GPU capacity. On the inference side, Modal offers OpenAI-compatible endpoints for open-weight models and lets teams deploy custom stacks and tune endpoints to match their traffic shape.
Underneath, Modal runs its own container runtime and image system designed for large images and fast GPU scheduling, along with a distributed filesystem for fast access to model weights and datasets. Capacity is drawn from a multi-cloud pool spanning many regions, with automatic routing to available hardware and continuous fleet health checks. Billing is usage-based, charged by the second while code runs, with granular cost controls. The company offers a free starting allowance and scalable paid usage, and markets to enterprises with SOC 2 Type II compliance and HIPAA readiness.
Turn a Python function into a scalable GPU-backed inference endpoint in minutes, with autoscaling, logging, and metrics built in.
Use Sandboxes to execute model-generated or user-supplied code in isolated environments that start fast and scale to high concurrency.
Burst into multi-node GPU capacity for fine-tuning, evaluation, and reinforcement learning without waiting on quota approvals.
Fan out batch and ETL jobs across thousands of containers that spin up in seconds and cost nothing when idle.
Side-by-side telemetry comparison of competing AI tools in the same primary category
| Rank | AI Tool | Monthly Visits | MoM Growth | Direct traffic share | Pricing | Head-to-Head |
|---|---|---|---|---|---|---|
| ● | Modal | 1.2M | +30.2% | 71% direct | Freemium, Contact for Pricing | Current |
| 1 | Cerebrium Offers similar serverless Python and GPU execution, but with a more streamlined deployment configuration. | 26.97K | -50.8% | 56% direct | Free Trial, Paid | vs Cerebrium |
| 2 | RunPod Provides flexible GPU infrastructure and serverless endpoints, though less focused on pure Python function decorators. | 2.88M | +5.9% | 57% direct | Paid | vs RunPod |
| 3 | Replicate AI Easier model packaging via Cog, but offers less arbitrary serverless Python container execution than Modal. | 904.04K | -8.4% | 64% direct | Paid | vs Replicate AI |
| 4 | fal.ai Extremely fast serverless execution optimized specifically for generative media models rather than general Python code. | 3.09M | -3.7% | 60% direct | Paid, pay-as-you-go usage-based pricing | vs fal.ai |
| 5 | Together AI Focuses heavily on managed open-source model inference APIs rather than arbitrary custom container execution. | 1.65M | +64.9% | 54% direct | Freemium, Paid | vs Together AI |
| 6 | Nebius Delivers robust enterprise GPU clusters and managed inference, requiring more infrastructure setup than Modal's serverless approach. | 1.15M | +10.7% | 39% direct | Paid, Contact for Pricing | vs Nebius |
Offers similar serverless Python and GPU execution, but with a more streamlined deployment configuration.
Offers similar serverless Python and GPU execution, but with a more streamlined deployment configuration.
Provides flexible GPU infrastructure and serverless endpoints, though less focused on pure Python function decorators.
Provides flexible GPU infrastructure and serverless endpoints, though less focused on pure Python function decorators.
Easier model packaging via Cog, but offers less arbitrary serverless Python container execution than Modal.
Easier model packaging via Cog, but offers less arbitrary serverless Python container execution than Modal.
SpecialistExtremely fast serverless execution optimized specifically for generative media models rather than general Python code.
SpecialistExtremely fast serverless execution optimized specifically for generative media models rather than general Python code.
SpecialistFocuses heavily on managed open-source model inference APIs rather than arbitrary custom container execution.
SpecialistFocuses heavily on managed open-source model inference APIs rather than arbitrary custom container execution.
Delivers robust enterprise GPU clusters and managed inference, requiring more infrastructure setup than Modal's serverless approach.
Delivers robust enterprise GPU clusters and managed inference, requiring more infrastructure setup than Modal's serverless approach.
Provides integrated development studios and training workflows, but with less granular serverless function orchestration.
Provides integrated development studios and training workflows, but with less granular serverless function orchestration.
SpecialistCombines ready-to-use model APIs and serverless GPU jobs, with simpler API consumption than custom Modal apps.
SpecialistLow-cost serverless inference APIs for 200+ open models
SpecialistManaged Ray platform for scaling AI workloads
Rent cloud GPUs by the second from a global marketplace
Rent cloud GPUs by the second from a global marketplace
