# Fireworks vs GPUPerHour

Fireworks records 659,687 monthly visits with +6% month-over-month growth and Contact for Pricing pricing. GPUPerHour records 28,718 monthly visits with +25.3% growth and Free pricing.

| Metric | [Fireworks](https://openaitools.app/tool/fireworks-ai) | [GPUPerHour](https://openaitools.app/tool/gpuperhour) |
| :--- | :--- | :--- |
| Website URL | https://fireworks.ai | https://gpuperhour.com |
| Categories | AI API, Large Language Models (LLMs), Open Source AI Models, AI Image Generator | AI Developer Tools, AI Tools Directory |
| Pricing Model | Contact for Pricing | Free |
| Monthly Visits | 659,687 | 28,718 |
| MoM Growth Rate | +6% | +25.3% |
| Primary Market | United States | United States |
| Direct Visits | 313,827 | 11,398 |
| Review Score | 4.6 | 4.4 |
| Saved Count | 2 | 4 |
| Supported Platforms | Website | Website |

## Fireworks

Fireworks is a platform for teams that want to own their AI rather than rent it. Its core claim is a co-optimised stack: inference, model ensembling and training built to work together, from CUDA kernels and caching up through routing and fine-tuning. Inference is optimised for speed, quality and cost — the company reports up to 4x the throughput of providers wrapping vLLM, quality benchmarked against each model's reference implementation, and cache hit rates high enough that cached input costs half or less.

The platform is explicitly multi-model. Rather than betting on a single frontier API, teams start with state-of-the-art open models, then improve their economics by routing or ensembling across models on a per-turn basis, optimising accuracy and cost per finished task. Where a general model is not enough, training lets you specialise a model on the data that makes your business distinctive.

Deployment flexibility matters as much as performance. You can run serverless and reserve throughput as you grow, keep data local, and reach 30+ regions behind one endpoint with policy-aligned data residency. Fireworks publishes a model library, docs, cookbooks and cookbooks-style resources for developers, and holds SOC 2 Type 2, ISO 27001, ISO 27701 and ISO 42001 certifications — the last of which covers AI management systems, a signal aimed squarely at regulated enterprises.

**Pricing & Plans:** Usage-based pricing for inference, with per-million-token rates that vary by model, plus serverless options and reserved throughput for predictable loads. Training and ensembling carry their own charges. Enterprise and startup programs are available, and interested teams are directed to contact sales for platform, sovereign and custom deployments.

- **High-throughput inference:** Run open models and your own fine-tunes at up to 4x the throughput of providers that wrap vLLM, with lab-designed quality verified against reference implementations.
- **Cost-optimised caching:** Reach up to 99.6% cache hit rates, with cached input priced at half or less, so recurring workloads cost far less than emitting every token fresh.
- **Model ensembling and routing:** Route each turn to the model best suited to it, balancing accuracy against cost per completed task rather than locking in one model.
- **Training and specialisation:** Fine-tune open models on proprietary data to build domain-specific intelligence that competitors cannot simply rent from a frontier lab.
- **Global deployment with data residency:** One endpoint spanning 30+ regions, your cloud or Fireworks', with policy-aligned residency plus SOC 2 Type 2, ISO 27001 and ISO 42001 compliance.

**Strengths:** Co-optimised inference, ensembling and training in one stack; Open-model focus avoids being locked into one vendor's frontier pricing; Strong throughput, quality benchmarking and cache economics for high-volume workloads; Deployment flexibility including your cloud, your data locality and 30+ regions

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

## 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)

## Frequently Asked Questions

### How do Fireworks and GPUPerHour compare in monthly traffic?

Fireworks has 659,687 monthly visits, while GPUPerHour has 28,718. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Fireworks has a month-over-month growth rate of +6%; GPUPerHour has +25.3%.

### How does pricing compare?

Fireworks: Contact for Pricing. Usage-based pricing for inference, with per-million-token rates that vary by model, plus serverless options and reserved throughput for predictable loads. Training and ensembling carry their own charges. Enterprise and startup programs are available, and interested teams are directed to contact sales for platform, sovereign and custom deployments. 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.

## 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 | Acts as a unified API gateway across multiple providers, but lacks dedicated fine-tuning and custom model deployment infrastructure. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 2 | [Groq](https://openaitools.app/tool/groq) | 3,987,936 | +10% | Paid | Delivers ultra-fast token generation speeds using custom LPU hardware, but supports a smaller catalog of open models. | [.md](https://openaitools.app/tool/groq.md) |
| 3 | [fal.ai](https://openaitools.app/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | Specializes in lightning-fast serverless inference for generative media models, but focuses less on general text LLMs. | [.md](https://openaitools.app/tool/fal-ai.md) |
| 4 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Provides robust serverless inference and model fine-tuning similar to Fireworks, but with a broader developer cloud ecosystem. | [.md](https://openaitools.app/tool/together-ai.md) |
| 5 | [Replicate AI](https://openaitools.app/tool/replicate-ai) | 904,038 | -8.4% | Paid | Features seamless deployment and fine-tuning for machine learning models, but can have higher latency for text inference. | [.md](https://openaitools.app/tool/replicate-ai.md) |
| 6 | [Unsloth AI](https://openaitools.app/tool/unsloth-ai) | 887,457 | -17.6% | Free | Open-source, memory-efficient fine-tuning and local model running | [.md](https://openaitools.app/tool/unsloth-ai.md) |
| 7 | [Atlas Cloud](https://openaitools.app/tool/atlas-cloud) | 777,031 | -16.4% | Paid | One OpenAI-compatible API for 500+ image, video, audio, and LLM models | [.md](https://openaitools.app/tool/atlas-cloud.md) |
| 8 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Offers exceptionally low-cost serverless inference for open-weight models, but provides fewer advanced fine-tuning orchestration tools. | [.md](https://openaitools.app/tool/deep-infra.md) |
| 9 | [Runware](https://openaitools.app/tool/runware) | 402,862 | +27.1% | Paid | One API for 400K+ AI models, plus serverless and reserved GPUs on modular data centers | [.md](https://openaitools.app/tool/runware.md) |
| 10 | [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) |

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

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