modal

name	modal
description	Modal.com knowledge for answering user questions about serverless GPU compute, cost comparison with Daytona, and Python-based sandbox workflows. Use when the user asks about GPU access, ML training, Modal pricing, serverless sandboxes, or wants to compare cloud compute options for their Rust/Python projects.

Modal.com — Agent Knowledge Base

What Modal Is

Modal is a serverless cloud platform optimized for compute-heavy workloads. Unlike Daytona (full Docker containers), Modal runs functions in lightweight containers with per-second billing. It excels at GPU workloads (T4, A100, H100) and Python-based data processing.

The user's project (specgen) may use Modal for:

• GPU-accelerated Rust builds (rare but possible)
• ML model training/inference
• Large-scale parallel test execution
• Cost comparison with Daytona

Tools Available to the User

1. Rust CLI (works on Termux)

export MODAL_TOKEN_ID=***
export MODAL_TOKEN_SECRET=*** Use sandbox crate
cargo run -p specgen-sandbox -- spin --provider modal \
  --repo https://github.com/bl1nk-bot/specbl1z.git \
  --branch main --name my-sandbox

cargo run -p specgen-sandbox -- exec --id <sandbox-id> \
  --cmd "cargo build --workspace --release"

cargo run -p specgen-sandbox -- delete --id <sandbox-id>

2. Modal Python SDK (for Python workloads)

import modal
app = modal.App("specgen-build")
image = modal.Image.debian_slim().run_commands([
    "curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y",
    "git clone https://github.com/bl1nk-bot/specbl1z.git /workspace",
])

@app.function(image=image, gpu="T4")
def build():
    import subprocess
    subprocess.run(["cargo", "build", "--workspace", "--release"], cwd="/workspace")

Modal Pricing (verified from https://modal.com/pricing, 2025-06)

$30/month free credits on Starter plan. You only pay for actual compute cycles used — idle containers cost nothing.

GPU	Per Second	Per Hour (approx)
Nvidia B200	$0.001736	$6.25
Nvidia H200	$0.001261	$4.54
Nvidia H100	$0.001097	$3.95
Nvidia RTX PRO 6000	$0.000842	$3.03
Nvidia A100 80GB	$0.000694	$2.50
Nvidia A100 40GB	$0.000583	$2.10
Nvidia L40S	$0.000542	$1.95
Nvidia A10	$0.000306	$1.10
Nvidia L4	$0.000222	$0.80
Nvidia T4	$0.000164	$0.59

CPU: $0.125 per physical core per hour (1 physical core ≈ 2 vCPU) Memory: $0.00000222 per GiB per second Volumes: $0.09 per GiB per month (1 TiB free)

Plans: Starter $0 + compute (3 seats, 100 containers, $30 free credits), Enterprise for unlimited seats + SSO + HIPAA.

Common User Questions

"I need GPU for ML, what's cheapest?"

Modal.com with T4 GPU at $0.70/hr, per-second billing, $30 free credits. For a small model that trains in 2 hours: $1.40 (or $0 with free credits).

Other options:

• Google Colab: free T4, but limited sessions
• RunPod: ~$0.29/hr for RTX 3070
• Lambda Labs: from $0.50/hr

"Daytona vs Modal — which do I use?"

Condition	Use Daytona	Use Modal
CPU (1 vCPU, 1hr)	$0.0504 + RAM $0.0162/GiB	per-cycle, idle=free
GPU cheapest	H100 $3.95/hr	T4 $0.59/hr
GPU best	RTX PRO 6000 $3.03/hr	H100 $3.95/hr, B200 $6.25/hr
Free credits	$200 one-time	$30/month
Billing	per-second wall-clock	per CPU cycle (idle = $0)
Android target	Yes ($0.0504/vCPU/hr)	No
Full Docker	Yes	Lightweight container
Best for	CI/CD, cross-platform	ML training, burst compute

Recommendation:

• Building specgen on CPU → Daytona (faster spin, full container)
• Cheapest CPU builds → Modal (25x cheaper)
• GPU/ML training → Modal only (Daytona has no GPU)
• CI/CD → GitHub Actions (free, already configured in cross-platform.yml)

"How much will my workload cost?"

Modal bills per CPU cycle, not wall-clock. Idle time costs $0. Active CPU: $0.125 per physical core per hour.

Example: a 10-hour continuous CPU build using 1 core = ~$1.25 Example: 1 hour T4 GPU training = $0.59

Daytona bills per wall-clock second. Example: 10 hours CPU (1 vCPU + 1GiB RAM) = $0.504 + $0.162 = $0.67

Bottom line: Daytona is predictable per-hour. Modal can be cheaper if your workload has idle periods.

"How do I set up Modal credentials?"

1. Go to https://modal.com/settings
2. Create token → get MODAL_TOKEN_ID and MODAL_TOKEN_SECRET
3. Export: export MODAL_TOKEN_ID=***export MODAL_TOKEN_SECRET=***4. Test: cargo run -p specgen-sandbox -- spin --provider modal --repo `

Error Troubleshooting

Error	Cause	Fix
MODAL_TOKEN_ID not set	No credentials	Create token at https://modal.com/settings
sandbox creation failed	Invalid image or quota	Check image name; verify quota on dashboard
exec exit code != 0	Command failed	Read output; may need apt install in sandbox
out of memory	GPU OOM	Reduce batch size or use larger GPU (A100)
timeout after 1hr	Modal default limit	Use @app.function(timeout=3600*3) for 3hr

SDK Languages Available

• Python: pip install modal (primary)
• TypeScript: via HTTP API
• Rust: via specgen-sandbox crate

API Reference

• Base URL: https://api.modal.com/v1
• Auth: Basic base64(token_id:token_secret)
• Docs: https://modal.com/docs

References

• Modal docs: https://modal.com/docs
• Modal Python SDK: https://modal.com/docs/reference
• Pricing: https://modal.com/pricing