# Modal vs Stakpak

Modal records 1,196,021 monthly visits with +30.2% month-over-month growth and Freemium, Contact for Pricing pricing. Stakpak records 633 monthly visits with -46.3% growth and Freemium pricing.

| Metric | [Modal](https://openaitools.app/tool/modal-com) | [Stakpak](https://openaitools.app/tool/stakpak-dev) |
| :--- | :--- | :--- |
| Website URL | https://modal.com | https://stakpak.dev |
| Categories | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models | AI Agent, AI Developer Tools, Open Source AI Models, AI Copilot |
| Pricing Model | Freemium, Contact for Pricing | Freemium |
| Monthly Visits | 1,196,021 | 633 |
| MoM Growth Rate | +30.2% | -46.3% |
| Primary Market | United States | Egypt |
| Direct Visits | 845,636 | 253 |
| Review Score | 4.8 | 4.6 |
| Saved Count | 3 | 0 |
| Supported Platforms | Website | Website |

## Modal

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.

**Pricing & Plans:** Modal uses usage-based pricing billed by the second: a free monthly compute allowance gets you started, then you pay for the CPU, memory, and GPU resources your workloads consume. Enterprise plans and startup credits are available, and custom pricing can be discussed with sales.

- **Serverless Python functions:** Write plain Python, attach GPUs or containers, and deploy HTTPS endpoints that autoscale with traffic and scale to zero when idle.
- **Sandboxes for agents:** Launch isolated, secure environments for coding agents, RL rollouts, and untrusted code, with sub-second starts and support for over a million concurrent sandboxes.
- **Training and fine-tuning on demand:** Run fine-tunes, LoRA, full training, and reinforcement learning on elastic single or multi-node GPU capacity with no quota requests.
- **Model inference and endpoints:** Serve LLMs and image, video, or audio models through OpenAI-compatible endpoints, or bring your own inference stack and let Modal handle the infrastructure.
- **Fast storage and multi-cloud capacity:** 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.

**Strengths:** Python-native developer experience with almost no infrastructure boilerplate; Scales from zero to thousands of GPUs with per-second, usage-based billing; Sandboxes are well suited to agentic and untrusted-code workloads; Multi-cloud capacity spans many regions with out-of-the-box observability and enterprise compliance

**Limitations:** Billed by real usage, so heavy GPU workloads can get expensive quickly without cost controls; Some advanced inference and enterprise features require working with the team rather than self-serve signup; The serverless model can feel less familiar to teams used to managing their own VMs or Kubernetes clusters

[Profile (Markdown)](https://openaitools.app/tool/modal-com.md)

## Stakpak

Stakpak is an open-source AI agent for running production infrastructure. Rather than a chatbot that suggests commands, it is a long-running process that lives on your own machines, watches your applications, and acts when something goes wrong. You install it as a single Rust binary, run stakpak init to let it discover your stack and generate an APPS.md manifest, then stakpak up and it runs in the background — scheduling checks, taking action and messaging you only when a human decision is genuinely required.

The scenarios it targets are the ones that ruin sleep: a health check fails at 3am, the agent finds 312 idle database connections, restarts the service, confirms latency dropped from 340ms to 12ms and posts a summary to Slack. Over longer horizons it runs a cost watchdog that spots idle RDS instances and orphaned EBS volumes and recovers real monthly spend, and it defuses time bombs like expiring TLS certificates, stale secrets and end-of-life runtimes.

The trust model is the differentiator. A coding agent that gets a command wrong produces a bad commit; an agent with production access that gets it wrong causes downtime or a five-figure bill. So Stakpak routes every network request — curl, Python, or MCP servers — through a Cedar-policy transparent proxy called Warden, detects and substitutes over 210 types of secrets before anything reaches a model provider, and keeps a full session audit log that lets you replay or roll back any change. It is Apache 2.0 licensed and written in Rust, and it is model-agnostic: use the unified LLM Gateway for one-wallet billing across Claude, GPT, Gemini and Mistral, or bring your own keys and run models locally.

**Pricing & Plans:** Open Source is free forever (Apache 2.0, bring your own keys). Hacker is $15/mo for 3 apps, Builder $45/mo for 10 apps, Team $450/mo for 50 apps with unlimited users, and Enterprise is custom. Extra apps cost $5/app/month and each paid tier bundles LLM Gateway credits.

- **24/7 auto-healing:** When a health check fails, the agent investigates — checking metrics, finding idle database connections, restarting the service, verifying the rollout — then posts a summary of what it did and how long it took.
- **Cloud cost watchdog:** Scheduled scans surface idle RDS instances and orphaned EBS volumes, quantify the waste in a report and can recover meaningful monthly spend before finance notices.
- **Expiry and drift defence:** Upcoming TLS expiries, stale secrets, deprecated APIs and end-of-life runtimes are caught and renewed ahead of time, with zero-downtime rollouts.
- **Single binary, runs on your machine:** A single Rust binary installs as a system service and persists across reboots, combining scheduling, messaging and a local API. It works with Claude, GPT, Gemini and Mistral, or your own model running locally.
- **Warden sandbox and audit logs:** Network requests go through a Cedar-policy sandbox, 210+ secret types are swapped for placeholders before reaching a model, and every command, edit and change is fully logged and replayable.
- **Slack, Telegram and Discord triggers:** Outcomes are reported to Slack, Telegram or Discord per agent, and only escalated to a human when the agent genuinely needs a decision.

**Strengths:** Genuinely open source (Apache 2.0) and runs entirely inside your own environment; Real production risk controls: Cedar network sandbox, secret substitution and replayable audit logs; One binary, no Kubernetes or control-plane sprawl to operate; Model-agnostic with a unified gateway or your own keys and local models

**Limitations:** Autopilot adds a long-running agent process with real production access, so misconfigured triggers carry real risk; Per-app subscription maths only makes sense once you manage several applications; Setup requires comfort with a terminal, system services and IaC concepts; The team is joining Vercel, so road-map and licensing direction may shift

[Profile (Markdown)](https://openaitools.app/tool/stakpak-dev.md)

## Frequently Asked Questions

### How do Modal and Stakpak compare in monthly traffic?

Modal has 1,196,021 monthly visits, while Stakpak has 633. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Modal has a month-over-month growth rate of +30.2%; Stakpak has -46.3%.

### How does pricing compare?

Modal: Freemium, Contact for Pricing. Modal uses usage-based pricing billed by the second: a free monthly compute allowance gets you started, then you pay for the CPU, memory, and GPU resources your workloads consume. Enterprise plans and startup credits are available, and custom pricing can be discussed with sales. Stakpak: Freemium. Open Source is free forever (Apache 2.0, bring your own keys). Hacker is $15/mo for 3 apps, Builder $45/mo for 10 apps, Team $450/mo for 50 apps with unlimited users, and Enterprise is custom. Extra apps cost $5/app/month and each paid tier bundles LLM Gateway credits.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [fal.ai](https://openaitools.app/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | Extremely fast serverless execution optimized specifically for generative media models rather than general Python code. | [.md](https://openaitools.app/tool/fal-ai.md) |
| 2 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | Provides flexible GPU infrastructure and serverless endpoints, though less focused on pure Python function decorators. | [.md](https://openaitools.app/tool/runpod.md) |
| 3 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Focuses heavily on managed open-source model inference APIs rather than arbitrary custom container execution. | [.md](https://openaitools.app/tool/together-ai.md) |
| 4 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Rent cloud GPUs by the second from a global marketplace | [.md](https://openaitools.app/tool/vast-ai.md) |
| 5 | [Browser Use](https://openaitools.app/tool/browser-use) | 1,206,376 | +41.1% | Freemium, Paid, Contact for Pricing | Browser agents and stealth cloud browsers for real web tasks | [.md](https://openaitools.app/tool/browser-use.md) |
| 6 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | Delivers robust enterprise GPU clusters and managed inference, requiring more infrastructure setup than Modal's serverless approach. | [.md](https://openaitools.app/tool/nebius.md) |
| 7 | [MyClaw](https://openaitools.app/tool/myclaw) | 990,387 | -23.8% | Paid | Your always-on AI teammate, hosted and managed for you | [.md](https://openaitools.app/tool/myclaw.md) |
| 8 | [Replicate AI](https://openaitools.app/tool/replicate-ai) | 904,038 | -8.4% | Paid | Easier model packaging via Cog, but offers less arbitrary serverless Python container execution than Modal. | [.md](https://openaitools.app/tool/replicate-ai.md) |
| 9 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Low-cost serverless inference APIs for 200+ open models | [.md](https://openaitools.app/tool/deep-infra.md) |
| 10 | [Zeabur](https://openaitools.app/tool/zeabur) | 275,642 | -15.9% | Free Trial, Paid | AI DevOps engineer that deploys and scales your apps | [.md](https://openaitools.app/tool/zeabur.md) |

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Source: https://openaitools.app/compare/modal-com-vs-stakpak-dev

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