# Groq vs Thunder Compute

Groq records 3,987,936 monthly visits with +10% month-over-month growth and Paid pricing. Thunder Compute records 102,147 monthly visits with -29% growth and Paid (per-minute instances, per-second sandboxes) pricing.

| Metric | [Groq](https://openaitools.app/tool/groq) | [Thunder Compute](https://openaitools.app/tool/thunder-compute) |
| :--- | :--- | :--- |
| Website URL | https://groq.com | https://thundercompute.com |
| Categories | AI API, Large Language Models (LLMs), Open Source AI Models | AI Developer Tools, AI Models, Large Language Models (LLMs) |
| Pricing Model | Paid | Paid (per-minute instances, per-second sandboxes) |
| Monthly Visits | 3,987,936 | 102,147 |
| MoM Growth Rate | +10% | -29% |
| Primary Market | India | United States |
| Direct Visits | 1,702,515 | 40,743 |
| Review Score | 4.7 | 4.4 |
| 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)

## Thunder Compute

Thunder Compute is a cloud GPU provider that markets itself as the GPU sandbox company. Rather than a single generic VM offering, it splits its product into two shapes. Instances are long-lived GPU machines for hands-on development and training, available with RTX A6000, L40, A100 80GB, or H100 SXM GPUs, configurable across GPU count, vCPU, RAM, and disk, and billable per minute only while running. Sandboxes are isolated, short-lived Ubuntu microVMs designed for agent workloads: reinforcement-learning environments, evaluation sweeps, and CI jobs that need a GPU for a few minutes and can then disappear.

The company leans on price and developer ergonomics. Its published rates list H100 SXM instances at $3.20 per GPU-hour, A100 80GB at $1.09, L40 at $0.79, and RTX A6000 at $0.35, with per-second sandbox pricing of $2.95 per GPU-hour for H100 and $1.95 for A100. Storage is metered per GB-month with monthly snapshot pricing, and there are no data egress fees. Thunder Compute says it keeps costs down by scheduling workloads efficiently and reducing idle time, and passes those savings on.

The workflow is designed to be scriptable rather than click-heavy. A CLI handles instance creation and connection, a VS Code extension supports in-editor iteration, and an MCP server plus REST API let agents and internal platforms provision GPUs programmatically. Snapshots preserve disk state between sessions, and neither instances nor sandboxes can be preempted. The company raised a $13 million Series A in 2026 to expand the platform, and documents the stack through guides, quickstarts, and API references.

**Pricing & Plans:** Thunder Compute bills instances per minute while running and sandboxes per second. Published instance rates: RTX A6000 $0.35/GPU-hour, L40 $0.79, A100 80GB $1.09, H100 SXM $3.20, with extra vCPUs at $0.04/vCPU-hour. Sandbox rates are $2.95/GPU-hour for H100 SXM and $1.95/GPU-hour for A100 80GB plus vCPU, memory, and storage components. Instance disk storage is $0.219/GB per month (first 100 GB per GPU included), snapshots are $0.05/GB per month, and data egress is free. Grilled specifically: rates change over time, so verify on the pricing page.

- **Persistent GPU instances:** Launch RTX A6000, L40, A100 80GB, or H100 SXM machines, often ready to SSH into within minutes, and pay only while they run.
- **Per-second GPU sandboxes:** Spin up isolated Ubuntu VMs with or without a GPU for RL rollouts, evals, and CI, billed per second and scaled to zero between runs.
- **Developer-first tooling:** A CLI, VS Code extension, MCP server, and REST API let you drive infrastructure from a terminal, an editor, an AI agent, or your own services.
- **Snapshots and persistent storage:** Save an instance disk as a snapshot and restore it later, so you can pause experiments without leaving machines running.
- **No preemption, no egress fees:** Instances and sandboxes are never preempted, and outbound data transfer is not billed.

**Strengths:** Aggressive pricing versus hyperscalers: published H100 rates around $3.20/GPU-hour against $6+ at major clouds.; Two billing models that match two real workloads: per-minute persistent instances and per-second ephemeral sandboxes.; No egress fees and no preemption, which removes two common sources of surprise cost and interruption.; Real developer surface area: CLI, VS Code extension, MCP server, REST API, and templates.

**Limitations:** GPU availability can fluctuate, so a particular type may be sold out when you try to launch it.; Sandbox access is requested through sales rather than being fully self-serve at sign-up.; The platform's GPU catalog is narrower than the largest hyperscalers, which offer a wider range of accelerator families.

[Profile (Markdown)](https://openaitools.app/tool/thunder-compute.md)

## Frequently Asked Questions

### How do Groq and Thunder Compute compare in monthly traffic?

Groq has 3,987,936 monthly visits, while Thunder Compute has 102,147. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Groq has a month-over-month growth rate of +10%; Thunder Compute has -29%.

### 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. Thunder Compute: Paid (per-minute instances, per-second sandboxes). Thunder Compute bills instances per minute while running and sandboxes per second. Published instance rates: RTX A6000 $0.35/GPU-hour, L40 $0.79, A100 80GB $1.09, H100 SXM $3.20, with extra vCPUs at $0.04/vCPU-hour. Sandbox rates are $2.95/GPU-hour for H100 SXM and $1.95/GPU-hour for A100 80GB plus vCPU, memory, and storage components. Instance disk storage is $0.219/GB per month (first 100 GB per GPU included), snapshots are $0.05/GB per month, and data egress is free. Grilled specifically: rates change over time, so verify on the pricing page.

## 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 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | The AI developer cloud for GPU pods, serverless inference and clustering | [.md](https://openaitools.app/tool/runpod.md) |
| 6 | [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) |
| 7 | [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) |
| 8 | [Modal](https://openaitools.app/tool/modal-com) | 1,196,021 | +30.2% | Freemium, Contact for Pricing | Serverless cloud infrastructure for AI and data workloads | [.md](https://openaitools.app/tool/modal-com.md) |
| 9 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | An AI-native cloud built from silicon to API for training, tuning, and serving models | [.md](https://openaitools.app/tool/nebius.md) |
| 10 | [Salad](https://openaitools.app/tool/salad-gpu-cloud) | 713,519 | +10.4% | Free Trial, Paid, Contact for Pricing | Distributed GPU cloud that orchestrates the world's idle consumer GPUs | [.md](https://openaitools.app/tool/salad-gpu-cloud.md) |

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Source: https://openaitools.app/compare/groq-vs-thunder-compute

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)