AI Infrastructure Modal

Modal Patterns

Quick Guide: Modal is a serverless GPU compute platform where you define Python functions with decorators and Modal handles containers, scaling, and GPU provisioning. TypeScript apps interact with Modal via HTTP endpoints (calling @modal.fastapi_endpoint or @modal.asgi_app functions) or the modal npm SDK (calling functions directly via gRPC). Define container images, secrets, and volumes as code -- no YAML config files. Use modal deploy for production, modal serve for dev.

CRITICAL: Before Using This Skill

All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)

(You MUST define Modal functions in Python -- the TypeScript SDK can call functions and manage resources but cannot define them)

(You MUST use @modal.fastapi_endpoint (not the old @modal.web_endpoint) for simple web endpoints -- renamed in Modal 1.0)

(You MUST use modal.Volume for model weight caching -- @modal.build is deprecated in Modal 1.0)

(You MUST never hardcode secrets in Modal code -- use modal.Secret.from_name() and access via os.environ)

(You MUST bind to 0.0.0.0 (not 127.0.0.1) when using @modal.web_server)

Auto-detection: Modal, modal, modal.App, modal.Image, modal.Volume, modal.Secret, modal.gpu, modal.fastapi_endpoint, modal.asgi_app, modal.web_server, modal.Cron, modal.Period, modal deploy, modal serve, MODAL_TOKEN_ID, MODAL_TOKEN_SECRET, ModalClient

When to use:

• Deploying ML models (vLLM, Hugging Face, custom PyTorch) on serverless GPUs
• Creating HTTP API endpoints backed by GPU compute for TypeScript apps to consume
• Running scheduled GPU jobs (fine-tuning, batch inference, data processing)
• Calling Modal functions from TypeScript using the modal npm SDK
• Building AI inference pipelines with auto-scaling and pay-per-second billing

Key patterns covered:

• Web endpoints (@modal.fastapi_endpoint, @modal.asgi_app, @modal.web_server) for HTTP access
• TypeScript client patterns (fetch-based and modal npm SDK)
• Container images, secrets, volumes, and GPU configuration
• Model serving with vLLM and custom inference
• Scheduled functions and deployment

When NOT to use:

• Pure Python ML workloads with no TypeScript consumer -- this skill focuses on the TypeScript interaction surface
• Simple CPU-only tasks where a regular server or cloud function suffices
• When you need persistent long-running servers (Modal scales to zero by default)
• You need sub-100ms cold starts (Modal cold starts are 2-4 seconds)
• You need persistent WebSocket connections beyond request/response

Examples Index

• Core: Web Endpoints & TypeScript Client -- Defining endpoints, calling from TypeScript, authentication, GPU functions, images, secrets, volumes
• Quick API Reference -- CLI commands, decorator parameters, URL patterns, GPU types

Philosophy

Modal eliminates infrastructure management for GPU workloads. Everything is code -- container images, GPU allocation, secrets, volumes, scaling rules. There are no YAML configs, Dockerfiles, or Kubernetes manifests.

Core principles:

1. Infrastructure as Python code -- Container images, GPU types, secrets, and volumes are all declared as Python decorators and objects. No separate config files.
2. Serverless GPU scaling -- Functions scale from zero to hundreds of GPUs automatically. You pay per second of compute, not for idle capacity.
3. Two interaction models for TypeScript -- Call Modal via HTTP endpoints (most common) or via the modal npm SDK for direct function invocation without HTTP overhead.
4. Immutable deployments -- modal deploy creates a named, persistent deployment with stable URLs. modal serve creates ephemeral dev endpoints.

Core Patterns

Pattern 1: Web Endpoint (TypeScript Consumption)

The most common pattern: define a Python endpoint on Modal, call it from TypeScript via fetch.

Python Side

# inference.py
import modal

app = modal.App("my-inference-api")

image = modal.Image.debian_slim().uv_pip_install(["fastapi[standard]", "transformers", "torch"])

@app.function(image=image, gpu="A10G")
@modal.fastapi_endpoint(method="POST")
def predict(payload: dict):
    # GPU-accelerated inference
    text = payload["text"]
    result = run_model(text)
    return {"prediction": result}

TypeScript Side

const response = await fetch(MODAL_ENDPOINT, {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify(input),
  signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS), // Essential for cold starts
});

Key requirements: Named constant for URL (not hardcoded at call sites), Content-Type: application/json header (FastAPI rejects without it), AbortSignal.timeout() to handle cold start delays, typed request/response interfaces.

See examples/core.md for a complete TypeScript client with error handling and typed interfaces.

Pattern 2: Authenticated Endpoints

Modal supports proxy auth tokens that protect endpoints without spinning up containers for unauthorized requests.

Python Side

@app.function(image=image, gpu="A10G")
@modal.fastapi_endpoint(method="POST", requires_proxy_auth=True)
def predict_secure(payload: dict):
    return {"prediction": run_model(payload["text"])}

TypeScript Side

headers: {
  "Content-Type": "application/json",
  "Modal-Key": process.env.MODAL_PROXY_KEY,     // Proxy auth token
  "Modal-Secret": process.env.MODAL_PROXY_SECRET,
},

Why good: Auth handled at Modal's proxy layer (no container spin-up for bad requests), env vars for credentials. Add explicit 401 handling in your error logic.

See examples/core.md for a complete authenticated TypeScript client with error handling.

Pattern 3: Modal npm SDK (Direct Function Calls)

For TypeScript apps that need to call Modal functions without HTTP overhead. Requires Node 22+.

import { ModalClient } from "modal";
const modal = new ModalClient(); // Create once, reuse
const fn = await modal.functions.fromName("my-inference-api", "predict");
const result = await fn.remote([text]); // sync call
const call = await fn.spawn([text]); // async (fire-and-forget)
const later = await call.get(); // retrieve result later

Why good: No HTTP serialization overhead, typed SDK, supports async spawn for long-running jobs

See examples/core.md for complete TypeScript SDK patterns including error handling and fire-and-forget job IDs.

When to use: Backend-to-Modal calls where you control the Node.js runtime (Node 22+). Not for browser or edge runtimes.

Pattern 4: GPU Functions and Container Images

Modal functions define their compute environment inline.

import modal

app = modal.App("gpu-inference")

# Container image with ML dependencies
inference_image = (
    modal.Image.debian_slim(python_version="3.11")
    .uv_pip_install(["torch==2.5.0", "transformers==4.47.0", "accelerate"])
    .apt_install(["libgl1"])
)

MODEL_ID = "meta-llama/Llama-3.1-8B-Instruct"

@app.function(
    image=inference_image,
    gpu="A100",                   # GPU type: "T4", "A10G", "A100", "H100", etc.
    secrets=[modal.Secret.from_name("huggingface-secret")],
    volumes={"/models": modal.Volume.from_name("model-cache", create_if_missing=True)},
    min_containers=1,             # Keep warm to avoid cold starts
    scaledown_window=300,         # Seconds before scaling to zero
)
def generate(prompt: str) -> str:
    from transformers import AutoModelForCausalLM, AutoTokenizer
    # Load from volume cache
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, cache_dir="/models")
    model = AutoModelForCausalLM.from_pretrained(MODEL_ID, cache_dir="/models")
    # ... generate and return

Why good: Pinned dependency versions, volume-based model caching (avoids re-download), min_containers for warm starts, secrets for HF token

# BAD: No version pinning, no volume cache, model re-downloads every cold start
@app.function(gpu="A100")
def generate(prompt: str):
    from transformers import pipeline
    pipe = pipeline("text-generation", model="meta-llama/Llama-3.1-8B-Instruct")
    return pipe(prompt)[0]["generated_text"]

Why bad: Unpinned deps break reproducibility, no volume means multi-GB model download on every cold start (30-60s+ delay), no secret for gated models

Pattern 5: Full ASGI App (FastAPI)

For endpoints that need routing, middleware, or multiple routes.

import modal
from fastapi import FastAPI

app = modal.App("my-api")
web_app = FastAPI()

@web_app.post("/predict")
async def predict(payload: dict):
    return {"result": "prediction"}

@web_app.get("/health")
async def health():
    return {"status": "ok"}

@app.function(image=modal.Image.debian_slim().uv_pip_install(["fastapi"]))
@modal.asgi_app()
def serve():
    return web_app

Why good: Full FastAPI capabilities (routing, middleware, validation), multiple endpoints under one function

Pattern 6: Secrets and Environment Variables

# Creating secrets via CLI
# modal secret create my-api-keys API_KEY=sk-xxx DB_URL=postgres://...

@app.function(
    secrets=[
        modal.Secret.from_name("my-api-keys"),
        modal.Secret.from_name("huggingface-secret"),
    ]
)
def my_function():
    import os
    api_key = os.environ["API_KEY"]  # Injected by Modal
    hf_token = os.environ["HF_TOKEN"]

Why good: Secrets created via dashboard or CLI, referenced by name in code, accessed as standard env vars, multiple secrets composable

Pattern 7: Scheduled Functions

@app.function(
    schedule=modal.Cron("0 2 * * *"),  # 2 AM daily
    image=inference_image,
    gpu="A10G",
    volumes={"/data": modal.Volume.from_name("training-data")},
)
def nightly_batch_inference():
    # Process accumulated data
    # Write results to volume
    pass

@app.function(schedule=modal.Period(hours=6))
def periodic_health_check():
    # Check model freshness, data quality, etc.
    pass

Why good: modal.Cron for precise scheduling, modal.Period for intervals. Scheduled functions cannot accept arguments -- use volumes or secrets for input data.

Decision Framework

How to Expose Modal to TypeScript

Does your TypeScript app need to call Modal?
+-- Via HTTP (most common)
|   +-- Single endpoint? -> @modal.fastapi_endpoint
|   +-- Multiple routes? -> @modal.asgi_app with FastAPI
|   +-- Non-Python server (vLLM, TGI)? -> @modal.web_server(port=8000)
|   +-- Need auth? -> Add requires_proxy_auth=True
+-- Via SDK (direct gRPC)
|   +-- Node 22+ backend? -> npm install modal, use ModalClient
|   +-- Browser/edge? -> Use HTTP endpoints instead
+-- Async job?
    +-- Fire-and-forget? -> SDK spawn() + later get()
    +-- Webhook callback? -> Modal calls your endpoint on completion

When to Use Each Endpoint Type

What are you serving?
+-- Simple function -> @modal.fastapi_endpoint (auto-wraps in FastAPI)
+-- Full web app -> @modal.asgi_app (FastAPI, Starlette, FastHTML)
+-- Legacy sync app -> @modal.wsgi_app (Flask, Django)
+-- Custom server binary -> @modal.web_server(port=8000) (vLLM, TGI, Ollama)

HTTP vs SDK

How should TypeScript call Modal?
+-- Browser/edge runtime? -> HTTP (fetch)
+-- Server-side Node 22+? -> Either works
|   +-- Need simplicity? -> HTTP
|   +-- Need speed (no serialization overhead)? -> SDK
|   +-- Need async spawn? -> SDK
+-- Multiple providers? -> HTTP (vendor-agnostic)

RED FLAGS

High Priority Issues:

• Using deprecated @modal.web_endpoint instead of @modal.fastapi_endpoint (renamed in Modal 1.0)
• Using @modal.build for downloading model weights (deprecated -- use modal.Volume instead)
• Using .lookup() for object references (deprecated -- use .from_name())
• Hardcoding secrets in Python source (use modal.Secret.from_name() + os.environ)
• Binding @modal.web_server to 127.0.0.1 instead of 0.0.0.0 (endpoint unreachable)

Medium Priority Issues:

• Not pinning dependency versions in uv_pip_install() (breaks reproducibility)
• No min_containers for latency-sensitive endpoints (2-4s cold starts)
• Missing signal: AbortSignal.timeout() on TypeScript fetch calls (hangs on cold starts)
• Not using volumes for model weight caching (re-downloads multi-GB models on cold starts)
• Using modal.Period when you need exact times (use modal.Cron -- Period resets on redeploy)

Common Mistakes:

• Confusing modal serve (ephemeral dev) with modal deploy (persistent production)
• Using messages parameter with @modal.fastapi_endpoint (it is not OpenAI -- it is a plain HTTP endpoint)
• Forgetting that scheduled functions cannot accept arguments -- use volumes, secrets, or global variables for input
• Not setting Content-Type: application/json header from TypeScript (FastAPI endpoints may reject the request)
• Using the modal npm SDK in browser or edge runtimes (requires Node 22+, native modules)

Gotchas & Edge Cases:

• Web endpoint max request timeout is 150 seconds (enforced by Modal's proxy)
• Rate limit: 200 requests/second default with 5-second burst multiplier
• Request bodies up to 4 GiB, response bodies unlimited
• modal serve URLs get a -dev suffix to avoid production conflicts
• URL pattern: https://<workspace>--<app-name>-<function-name>.modal.run
• Labels exceeding 63 characters are truncated with a SHA-256 hash suffix
• Volumes v1 limited to 500,000 files; v2 has no limit (use version=2)
• modal.Cron maintains schedule across redeploys; modal.Period resets
• Container class parameters are limited to primitives: str, int, bool, bytes
• Local Python files require Image.add_local_python_source() (automounting removed in 1.0)

CRITICAL REMINDERS

All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)

(You MUST define Modal functions in Python -- the TypeScript SDK can call functions and manage resources but cannot define them)

(You MUST use @modal.fastapi_endpoint (not the old @modal.web_endpoint) for simple web endpoints -- renamed in Modal 1.0)

(You MUST use modal.Volume for model weight caching -- @modal.build is deprecated in Modal 1.0)

(You MUST never hardcode secrets in Modal code -- use modal.Secret.from_name() and access via os.environ)

(You MUST bind to 0.0.0.0 (not 127.0.0.1) when using @modal.web_server)

Failure to follow these rules will produce broken deployments, security vulnerabilities, or unreachable endpoints.