SiliconFlow

You're building an AI app and need to run inference without spinning up your own GPU servers

17 views ↑ 5.4% MoM · 302.2K users/Feb 2026

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You're building an AI app and need to run inference without spinning up your own GPU servers. SiliconFlow gives you instant access to dozens of open source and commercial models through a single API endpoint.

You make one API call and your text, image, video, or multimodal model starts running. No containers to configure, no servers to provision. SiliconFlow handles everything serverless, so you only pay when tokens actually process. If you're prototyping a RAG system or coding assistant, you pick a model from their library and start sending requests. The unified API means you don't rewrite code when switching between models.

SiliconFlow runs a self-developed inference engine with end-to-end optimization. Works fast. When you need to fine-tune a model on your own data, you can upload datasets, process them, train the model, and deploy with one click. The fine-tuned version becomes available through the same API structure you're already using.

For production workloads where you can't risk throttling, you reserve GPUs. This guarantees capacity and keeps performance stable when traffic spikes. The service supports NVIDIA H100 and H200, AMD MI300, and RTX 4090 hardware. If your needs fluctuate, elastic GPUs scale up and down automatically while maintaining reliability.

The AI Gateway sits between your application and the models, handling smart routing so requests hit the right endpoint every time. You set rate limits to prevent runaway is priced at and monitor usage in real time. This matters when you're running customer-facing features that could rack up bills overnight if something breaks.

Where things get tricky is choosing between dozens of models without clear guidance on which performs best for your specific use case. The context windows vary wildly—some models handle 164K tokens total, others push to 262K. Output limits range from 66K to 262K tokens depending on which model you pick. You'll need to test multiple options to find what works.

Token pricing varies considerably by model. Step-3.5-Flash costs $0.1 per million input tokens and $0.3 per million output tokens. Kimi-K2.5 runs $0.23 input and $3.0 output. DeepSeek-V3 sits at $0.25 input and $1.0 output. GLM-5 charges $0.3 input but $2.55 output. You can start for free to test different models before committing to paid usage.

SiliconFlow targets developers building agents, content generation tools, AI assistants, and search systems where you need reliable inference without infrastructure headaches.

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Estimated website traffic data for SiliconFlow

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302.2K

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~300.0K avg/3mo