Accessing a diverse library of commercial and open-source AI models via Cloudsway’s Model services. Empowering customers with high-performance AI capabilities through simple API calls, enabling rapid integration and utilization of advanced technologies into enterprises’ existing workflows.
Run leading proprietary and open-source models cost-effectively. Instant integration and inference popular models with optimized latency, throughput and context length. Access via APIs and explore our models tailored for context, image and video
Blazing-Fast inference for 50+ models Optimized speed without compromising accuracy
The platform is compatible with a variety of commercial models, including OpenAI GPT, Anthropic Claude, Google Gemini, etc. Users can choose the most suitable models according to specific needs.
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Supporting various open-source models including Llama, Stable Diffusion, Whisper, etc. offering flexible model selection options.
Excels in performance and through end-to-end optimization across hardware and software, to achieve faster tokens per second, increased throughput, and reduced time to first token.
Easy to use, run leading models using a simple API and deploy at scale, free from infrastructure or hardware management.
Implement fast scaling infrastructure to maintain low latency and scale as needed. Designed to support large-scale concurrent requests.
Leverages global distributed network to deliver low-latency user experience and rapid responses, through multi-node redundancy and failover mechanisms.
Simple and transparent pricing to minimize expenses, no up-front investment allowing for accurate budget suitable for individuals and large enterprises.
Stable system with 99.9% uptime providing comprehensive health monitoring and automatic repairs.
Fine-tuning models for superior performance, unlock the power of your own dataset to achieve unmatched accuracy for specific tasks
Select a model that aligns with your task and domain.
Organize your dataset, following the prompt template of the model you’re fine-tuning.
Ensure your dataset is formatted correctly and upload it to the platform.
Use a deep learning framework to initiate fine-tuning with a single command.
Track progress and results or deploy checkpoints.
Integrate your fine-tuned model into your application or system, optimize your training jobs over any number of GPUs.
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