Comparar
20 de abril de 2026
AnyCap vs
Modal
AnyCap vs Modal is a question about which problem you are actually solving. Modal is a serverless cloud built for ML engineers: you write Python functions, Modal runs them on GPU, and you get fast, scalable compute without managing infrastructure. The target is data scientists, researchers, and backend teams that need to train, fine-tune, or deploy their own models. AnyCap solves a different problem. It packages existing multimodal capabilities, image generation, video generation, vision, web search, storage, and publishing, into a runtime that any coding agent can invoke through a single CLI and a single login. The target is agent operators who want to give their existing agent workflows new capabilities without building GPU infrastructure themselves. These products rarely compete directly. If your team needs custom model execution or wants to fine-tune a model on your own data, Modal provides the compute layer. If your team uses agents and wants those agents to access best-in-class media models without a new infrastructure project, AnyCap is the runtime layer. The clearest overlap is image and video generation, but even there the relationship is complementary: Modal could host a custom model, while AnyCap provides the agent-facing CLI to invoke production-ready models through one interface. Understanding which layer your workflow is actually missing determines which product you need.
Answer-first summary
Choose Modal when your team needs to run custom Python workloads on serverless GPU, deploy proprietary models, or fine-tune foundational models with full compute control. Choose AnyCap when your developers use coding agents and want those agents to access image, video, vision, and search capabilities through one portable runtime without building or managing GPU infrastructure. The practical split: if the gap is custom compute, use Modal. If the gap is agent capability access, use AnyCap. Most teams that use both are solving two separate problems at different layers of the same workflow.
Side-by-side comparison
Dimensão
AnyCap
Modal
Primary job
Agent capability runtime that gives existing agents a shared execution layer for image, video, vision, web, storage, and publishing.
Serverless GPU cloud for ML engineers to run custom Python functions, deploy model containers, and scale compute-heavy workloads without managing servers.
Integration target
Coding agents: Codex, Cursor, Claude Code, Manus, and other agent surfaces that need a portable capability runtime.
ML engineers and backend teams writing Python code who need on-demand GPU compute, fine-tuning pipelines, or custom model deployment.
Capability scope
Image, video, music, media understanding, grounded web retrieval, Drive storage, and Page publishing through one command surface.
Serverless compute across GPUs and CPUs, custom containers, persistent volumes, cron-scheduled functions, and Python-native ML pipelines.
Authentication model
Single login via `anycap login`. Credentials travel with the CLI across all agent environments.
Modal token stored in project config. Python SDK handles authentication inside Modal function definitions.
Invocation pattern
CLI commands invoked by agent: `anycap image generate`, `anycap video generate`. Output goes to agent context or Drive.
Python function decorated with `@app.function`. Called like any async Python function from application code or CI.
Best fit
Best when agents need multimodal capabilities and artifact delivery without custom infrastructure.
Best when teams need custom model hosting, GPU-accelerated Python workloads, or proprietary fine-tuning pipelines.
Why teams choose AnyCap
One runtime serves multiple agent shells without rebuilding capability integrations for each environment.
Capabilities extend from generation into understanding, web retrieval, storage, and publishing, the full output lifecycle, not just an API call.
No GPU knowledge required. Agents get access to production-grade media models through a CLI that any team member can use.
Why teams choose Modal
Full control over the compute layer. Teams can run any Python code, use custom containers, and deploy proprietary models at scale.
Modal handles cold start optimization, auto-scaling, and GPU provisioning, making serverless ML viable for production inference workloads.
The Python-native SDK integrates naturally into existing ML pipelines and backend services without introducing a new CLI tool.
Best fit by use case
Choose AnyCap if
Your coding agents need to generate or analyze media.
AnyCap is stronger when the goal is giving Cursor, Claude Code, or Codex access to image generation, video generation, and vision inside existing agent workflows, without a separate infrastructure project.
Choose Modal if
You need to run custom Python on GPUs.
Modal is the right choice when your team needs to run ML training jobs, custom inference functions, or fine-tuning pipelines on serverless GPU compute with Python-native control.
Choose AnyCap if
The workflow includes delivery after generation.
AnyCap is stronger when generated content needs to become a share link, a hosted page, or a Drive asset right after creation, not just an API response that your application code must then route manually.
Choose Modal if
You are building or fine-tuning your own models.
Modal is the cleaner fit when your product roadmap includes proprietary model development, custom container deployments, or experiments that require iterating on model weights directly.
How this comparison was reviewed
The Modal side of this page was reviewed against public Modal documentation available on April 20, 2026. The claims are intentionally narrow: Modal supports serverless Python functions on GPU, custom containers, persistent storage, cron scheduling, and Python SDK authentication.
The AnyCap side is based on published AnyCap pages for the CLI, installation, capability runtime, Drive, and pricing. Only public claims already visible on the product surface are used.
Methodology note
This page compares primary use cases, not total product breadth. Both products may add overlapping features over time. If Modal launches agent-facing CLI tooling or AnyCap adds serverless compute, this page should be updated.
Source notes
Modal getting started
Modal getting started — Core workflow: defining Modal functions, running them, and managing compute.
Modal GPU guide
Modal GPU guide — How to request GPU resources in Modal functions.
AnyCap image generation
AnyCap image generation — The public image generation surface exposed through the AnyCap runtime.
AnyCap video generation
AnyCap video generation — The public video generation workflow available through one CLI command.
Install AnyCap
Install AnyCap — Setup flow for agent environments that need a portable capability runtime.
Related pages
Compare
AnyCap vs fal.ai
Compare AnyCap to a generative media API platform with queue-backed inference and webhook support.
Compare
AnyCap vs Replicate
Compare AnyCap to another model hosting and inference platform with similar GPU infrastructure semantics.
Product
Video Generation
See the public video workflow that an agent gets through AnyCap without any GPU infrastructure.
Start here
Install AnyCap
Validate the runtime directly in your own agent workflow instead of staying in comparison mode.
FAQ
Is Modal a direct AnyCap replacement?
No. Modal is a serverless GPU cloud built for ML engineers who need custom Python compute, model deployment, and fine-tuning pipelines. AnyCap is a capability runtime built for agent operators who want to give existing coding agents access to image, video, vision, and search through one shared CLI. These products address different layers of the stack. Teams that use both are typically solving two separate problems: custom ML infrastructure on one side and agent capability access on the other.
Can I use Modal to power AnyCap capabilities?
Technically yes, you could deploy a custom model on Modal and invoke it from an agent workflow. But AnyCap is not a raw compute layer; it is a curated runtime that exposes production-ready models through one CLI. If your goal is simply giving agents access to best-in-class image or video models, AnyCap provides that without needing to manage Modal infrastructure.
When should I choose Modal over AnyCap?
Choose Modal when you need to run custom Python workloads on GPU, fine-tune your own models, or deploy proprietary containers at scale. Modal is the right choice when your team owns the compute pipeline and needs serverless infrastructure that adapts to ML-specific workloads. AnyCap does not solve custom compute problems, it packages pre-integrated capability access for agents.
Does AnyCap use Modal under the hood?
AnyCap's capability infrastructure is independent of Modal. AnyCap routes agent requests to the appropriate model providers through its own backend. The specific infrastructure powering each capability is an implementation detail — agents interact only with the CLI surface.
What is the simplest rule of thumb?
If your team needs to run custom Python on serverless GPU, use Modal. If your agents need to call image generation, video generation, or vision without building GPU infrastructure, use AnyCap. The categories rarely overlap in practice.