# Groq vs Stakpak

Groq records 3,987,936 monthly visits with +10% month-over-month growth and Paid pricing. Stakpak records 633 monthly visits with -46.3% growth and Freemium pricing.

| Metric | [Groq](https://openaitools.app/tool/groq) | [Stakpak](https://openaitools.app/tool/stakpak-dev) |
| :--- | :--- | :--- |
| Website URL | https://groq.com | https://stakpak.dev |
| Categories | AI API, Large Language Models (LLMs), Open Source AI Models | AI Agent, AI Developer Tools, Open Source AI Models, AI Copilot |
| Pricing Model | Paid | Freemium |
| Monthly Visits | 3,987,936 | 633 |
| MoM Growth Rate | +10% | -46.3% |
| Primary Market | India | Egypt |
| Direct Visits | 1,702,515 | 253 |
| Review Score | 4.7 | 4.6 |
| Saved Count | 4 | 0 |
| Supported Platforms | Website | Website |

## Groq

Groq is an AI inference cloud that runs large language models and other AI models at exceptionally low latency. Instead of designing chips for training, Groq built custom silicon called the LPU, now joined by LPX, optimized specifically for inference, the stage where models actually serve users. The company positions itself as a 'neocloud' for fast inference and combines that hardware with a software stack and developer APIs.

Developers access Groq through an API that hosts popular open models such as Llama, Mixtral, Gemma, and Whisper speech models, along with text-to-speech capabilities. Because the platform is tuned for speed, it is often used for real-time chat, agents, and voice interfaces where response time matters. The API is broadly compatible with common OpenAI-style request formats, which lowers the cost of switching.

Groq has scaled aggressively, announcing a $350 million Series A and building hundreds of megawatts of inference capacity. It highlights reliability and cost alongside speed, arguing that fast and affordable no longer need to be a tradeoff for teams running AI workloads in production.

**Pricing & Plans:** Paid, usage-based inference API. Groq charges per token for hosted models, with free developer tiers and enterprise capacity options; check the official pricing page for current rates.

- **LPU and LPX inference hardware:** Custom accelerators designed specifically for inference rather than training, tuned for very low latency.
- **Open model catalog:** Hosted access to popular open models including Llama, Mixtral, Gemma, and Whisper speech models.
- **Fast developer API:** An API broadly compatible with common OpenAI-style request formats for quick integration.
- **Speech and audio models:** Automatic speech recognition with Whisper plus text-to-speech capabilities for voice applications.
- **Scales for production:** Capacity built for high-volume production workloads with an emphasis on reliability and cost.

**Strengths:** Very fast inference latency for real-time applications; Broad catalog of popular open-source models; OpenAI-compatible API reduces migration effort; Managed cloud removes the need to run your own GPUs

**Limitations:** Focus is inference only, not model training; Pricing is usage-based, so costs scale with traffic; Availability and model selection depend on the hosted catalog

[Profile (Markdown)](https://openaitools.app/tool/groq.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 Groq and Stakpak compare in monthly traffic?

Groq has 3,987,936 monthly visits, while Stakpak has 633. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Groq has a month-over-month growth rate of +10%; Stakpak has -46.3%.

### How does pricing compare?

Groq: Paid. Paid, usage-based inference API. Groq charges per token for hosted models, with free developer tiers and enterprise capacity options; check the official pricing page for current rates. 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 | [DeepSeek](https://openaitools.app/tool/deepseek) | 372,761,591 | +4.3% | Freemium | High-efficiency open-weight and hosted reasoning AI powered by DeepSeek-V4 and R1 at ultra-low token costs. | [.md](https://openaitools.app/tool/deepseek.md) |
| 2 | [OpenAI Platform](https://openaitools.app/tool/openai) | 234,678,639 | -9.6% | Paid | Proprietary frontier models with powerful reasoning and multimodal features, but lacking open-weight model hosting and custom inference speeds. | [.md](https://openaitools.app/tool/openai.md) |
| 3 | [Google AI Studio](https://openaitools.app/tool/google-ai-studio) | 112,620,020 | +2% | Freemium | Google's rapid prototyping IDE and developer API workbench for building multimodal applications with Gemini models. | [.md](https://openaitools.app/tool/google-ai-studio.md) |
| 4 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Unified API gateway aggregating hundreds of models across providers, but acting as a router rather than direct hardware host. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 5 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Robust cloud platform for open-source model inference, offering extensive fine-tuning support but slightly slower raw speeds. | [.md](https://openaitools.app/tool/together-ai.md) |
| 6 | [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) |
| 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 | [Cerebras](https://openaitools.app/tool/cerebras) | 701,013 | -12.6% | Contact for Pricing | Wafer-scale hardware cloud delivering ultra-fast token generation speeds comparable to Groq, but supporting a more specialized model catalog. | [.md](https://openaitools.app/tool/cerebras.md) |
| 9 | [Fireworks](https://openaitools.app/tool/fireworks-ai) | 659,687 | +6% | Contact for Pricing | High-throughput serverless inference for open models, featuring excellent speed and fine-tuning capabilities with broad API compatibility. | [.md](https://openaitools.app/tool/fireworks-ai.md) |
| 10 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Low-cost serverless inference cloud hosting hundreds of open-weight models, but lacking specialized custom hardware accelerators like LPUs. | [.md](https://openaitools.app/tool/deep-infra.md) |

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

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