# GPUPerHour vs Replicate AI

GPUPerHour records 28,718 monthly visits with +25.3% month-over-month growth and Free pricing. Replicate AI records 904,038 monthly visits with -8.4% growth and Paid pricing.

| Metric | [GPUPerHour](https://openaitools.app/tool/gpuperhour) | [Replicate AI](https://openaitools.app/tool/replicate-ai) |
| :--- | :--- | :--- |
| Website URL | https://gpuperhour.com | https://replicate.com |
| Categories | AI Developer Tools, AI Tools Directory | AI API, AI Models, AI Developer Tools, Open Source AI Models |
| Pricing Model | Free | Paid |
| Monthly Visits | 28,718 | 904,038 |
| MoM Growth Rate | +25.3% | -8.4% |
| Primary Market | United States | United States |
| Direct Visits | 11,398 | 577,501 |
| Review Score | 4.4 | 4.6 |
| Saved Count | 4 | 5 |
| Supported Platforms | Website | Website |

## GPUPerHour

GPUPerHour solves a narrow but expensive problem: cloud GPU pricing is scattered across dozens of provider dashboards, in different units and with different availability. The site periodically scrapes the public pricing and inventory endpoints of 31 cloud GPU providers, normalises each listing to a price per GPU per hour (dividing a multi-GPU instance price by its GPU count) and tags it with its pricing type and security tier. The current-price summary deliberately uses only secure on-demand offers observed within roughly 15 minutes of the last refresh, excluding spot, reserved and peer-to-peer marketplace listings because those prices are not directly comparable.

The result is a single live table covering H100 SXM5 and PCIe, H200, B200, B300, A100 80GB and 40GB, L40S, RTX PRO 6000, RTX 5090, RTX 4090 and RTX A6000, alongside the provider, a 30-day historical catalog average and the number of providers with stock. A daily price index with CSV downloads lets you track how pricing moves over time, while buying guides and hardware explainers cover practical topics such as comparing like-for-like GPUs, choosing by VRAM for a workload, accounting for storage and egress, and renting by the hour. The site also points to a companion console, DeployGPU, for provisioning on supported providers from one place.

The key caveat is stated openly on the page: offer counts describe configurations, not the number of physical GPUs bookable, and availability can change before you complete a booking. Prices are per GPU-hour but providers bill for the whole instance, and storage, network transfer and commitment terms affect the final bill. GPUPerHour is a research and comparison layer, not a hosting provider itself.

**Pricing & Plans:** Free to use as a price-comparison and research resource; GPUPerHour does not sell GPU capacity itself. The prices shown are third-party provider rates for secure on-demand instances, per GPU-hour, and a companion service (DeployGPU) offers deployment on supported providers.

- **31-provider live price table:** Every tracked offer is normalised to a price per GPU-hour and shown with its provider, pricing type and security tier.
- **Cheapest-offer summary:** A market snapshot highlights the lowest current secure on-demand price for each popular GPU model, from H100 and H200 to RTX 4090.
- **Daily price index and CSV:** Historical catalog averages and downloadable daily data let you see how pricing for a given GPU has moved over the last 30 days.
- **Buying guides and explainers:** Practical articles cover comparing like-for-like GPUs, choosing by VRAM, estimating the full bill with storage and egress, and renting by the hour.
- **DeployGPU console:** A linked console lets you deploy on supported providers from a single place once you have picked an offer.

**Strengths:** Aggregates 31 providers into comparable per-GPU-hour prices; Clearly states its methodology and excludes non-comparable spot and P2P listings; Free to use, with daily historical data and CSV downloads; Buying guides cover the full bill, not just the headline rate

**Limitations:** Only covers providers whose pricing and inventory endpoints are publicly scrapable; The current-price view deliberately excludes spot and reserved capacity, which are often cheaper; It is a comparison resource, so you still create accounts and provision instances with the provider itself

[Profile (Markdown)](https://openaitools.app/tool/gpuperhour.md)

## Replicate AI

Replicate's core pitch is a uniform interface over thousands of models. Models range from image generators such as FLUX, GPT Image, Nano Banana, Seedream, and Recraft, to video models from MiniMax, ByteDance, xAI, and Alibaba, plus speech, music, captioning, image restoration, and large language models. Each model page exposes concrete inputs, versioned releases, and cost estimates, and you call them the same way regardless of the underlying framework. The catalog blends community-contributed open-source models with officially maintained proprietary ones, and the company positions every listed model as actually working with a production-ready API rather than a demo.

Beyond running hosted models, Replicate is a deployment and training layer. Cog, the project's open-source packaging tool, converts a model into a container with a generated HTTP API, so your own weights can be served from the same interface. Public models run on shared infrastructure and you pay only for processing time, while private models run on dedicated hardware and are billed for the full time instances are online — setup, idle, and active — with an exception for fast-booting fine-tunes that only bill when processing. Automatic scaling handles traffic spikes without manual capacity planning.

Pricing is strictly consumption-based. Public models are usually billed per second of runtime according to the hardware selected, with some billed per input and output instead; published examples include Flux 1.1 Pro at $0.04 per output image and DeepSeek R1 at $3.75 per million input tokens. Hardware rates are public too, from a T4 at roughly $0.81 per hour up to eight H100s at about $43.92 per hour. You can start free, and enterprise contracts unlock additional multi-GPU A100, H100, and H200 capacity.

**Pricing & Plans:** Paid, usage-based — you only pay for what you use. Most public models are billed by runtime at per-second hardware rates; some models are billed per input and output (for example, $0.04 per output image on Flux 1.1 Pro, $3.75 per million input tokens on DeepSeek R1). Published GPU hardware ranges from about $0.81/hr for a T4 up to roughly $43.92/hr for 8x H100. Private deployments bill for setup, idle, and active time on dedicated hardware, and you can start for free. Enterprise and volume discounts available.

- **Thousands of models behind one API:** Run image, video, audio, speech, and language models through a uniform API surface, with official models for FLUX, Nano Banana, Seedream, Grok Imagine, MiniMax, Wan, Seedance, and more.
- **One-line-of-code inference:** Set up a model in minutes and make an API call in Python, Node, or plain HTTP after authenticating with an API token.
- **Cog packaging and custom deployments:** Package your own model with Cog, an open-source tool for packaging machine learning models, and deploy it on dedicated hardware that scales up and down with demand.
- **Model fine-tuning:** Fine-tune models on your own data, including fast-booting fine-tunes that are billed only while actively processing requests rather than for idle time.
- **Published hardware and inference rates:** Transparent per-second hardware rates across CPU, T4, L40S, A100, H100, and H200 configurations, with committed-spend options for multi-GPU capacity.

**Strengths:** Very broad model catalog spanning image, video, audio, speech, and LLMs behind one consistent API; Usage-based billing with openly published per-second hardware and per-output model rates; Cog makes it straightforward to package and deploy your own private models; Automatic scaling handles traffic spikes without manual capacity planning

**Limitations:** Costs are consumption-based and can rise quickly with heavy traffic or always-on private deployments; Private models bill for idle and setup time, not just request processing, unless they are fast-booting fine-tunes; Long-running GPU models can queue on shared public capacity during peak periods; It is an infrastructure layer, so you still need your own prompts, pipelines, and application code

[Profile (Markdown)](https://openaitools.app/tool/replicate-ai.md)

## Frequently Asked Questions

### How do GPUPerHour and Replicate AI compare in monthly traffic?

GPUPerHour has 28,718 monthly visits, while Replicate AI has 904,038. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

GPUPerHour has a month-over-month growth rate of +25.3%; Replicate AI has -8.4%.

### How does pricing compare?

GPUPerHour: Free. Free to use as a price-comparison and research resource; GPUPerHour does not sell GPU capacity itself. The prices shown are third-party provider rates for secure on-demand instances, per GPU-hour, and a companion service (DeployGPU) offers deployment on supported providers. Replicate AI: Paid. Paid, usage-based — you only pay for what you use. Most public models are billed by runtime at per-second hardware rates; some models are billed per input and output (for example, $0.04 per output image on Flux 1.1 Pro, $3.75 per million input tokens on DeepSeek R1). Published GPU hardware ranges from about $0.81/hr for a T4 up to roughly $43.92/hr for 8x H100. Private deployments bill for setup, idle, and active time on dedicated hardware, and you can start for free. Enterprise and volume discounts available.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Excels at routing text and multimodal prompts across various providers, but lacks Replicate's dedicated custom ML deployment. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 2 | [fal.ai](https://openaitools.app/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | Faster serverless execution for image and video models, but less generalized custom model deployment than Replicate. | [.md](https://openaitools.app/tool/fal-ai.md) |
| 3 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Stronger focus on LLM inference and fine-tuning, but lacks Replicate's diverse community-curated media model catalog. | [.md](https://openaitools.app/tool/together-ai.md) |
| 4 | [Unsloth AI](https://openaitools.app/tool/unsloth-ai) | 887,457 | -17.6% | Free | Specializes specifically in ultra-efficient model fine-tuning, rather than providing a general-purpose model hosting API. | [.md](https://openaitools.app/tool/unsloth-ai.md) |
| 5 | [Atlas Cloud](https://openaitools.app/tool/atlas-cloud) | 777,031 | -16.4% | Paid | Offers a broad mix of generative media models via API, but lacks Replicate's massive community deployment ecosystem. | [.md](https://openaitools.app/tool/atlas-cloud.md) |
| 6 | [Fireworks](https://openaitools.app/tool/fireworks-ai) | 659,687 | +6% | Contact for Pricing | Delivers exceptionally fast inference speeds for open models, but has a narrower model selection than Replicate. | [.md](https://openaitools.app/tool/fireworks-ai.md) |
| 7 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Significantly cheaper pricing for running open-weight models, but offers fewer advanced custom training and deployment features. | [.md](https://openaitools.app/tool/deep-infra.md) |
| 8 | [Pollinations](https://openaitools.app/tool/pollinations) | 316,875 | +22.8% | Free | Open infrastructure and one API for generative AI builders | [.md](https://openaitools.app/tool/pollinations.md) |
| 9 | [mcp.so](https://openaitools.app/tool/mcp-server) | 286,704 | -3% | Free | Discover and share Model Context Protocol servers for AI agents and coding tools | [.md](https://openaitools.app/tool/mcp-server.md) |
| 10 | [Novita AI](https://openaitools.app/tool/novita-ai) | 271,371 | -10.4% | Paid | Provides affordable serverless GPU execution and diverse model APIs, but with a less polished developer ecosystem. | [.md](https://openaitools.app/tool/novita-ai.md) |

---
Source: https://openaitools.app/compare/gpuperhour-vs-replicate-ai

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