Scale with requests
Adjust computing resources with inference traffic to handle peaks while reducing idle capacity.
AI Model Service
Easily deploy AI models and enjoy inference services with strong concurrent processing capabilities, cost savings, elasticity, and pay-as-you-go billing during model calls
For enterprise customization, contact business:
Phone: 13311867760
Email: zhangjinhui@shengsuanyun.com
Why Choose Shengsuanyun's Serverless AI
Elastic Scaling
Dynamically adjust computing resources based on the usage of model inference services, quickly scale computing resources when usage surges, and reduce idle computing resources when usage decreases
Fast Deployment
The platform provides ssy tools, which can package and upload models to the cloud platform in just 1 hour, completing AI model deployment
Pay as You Go
When calling the model for inference, the platform charges for the used computing power based on usage, and users only pay for the actual computing power used
Strong Concurrent Processing
Uses advanced computing power scheduling system and backend architecture, supports high concurrent request processing, users no longer need to worry about computing power issues
Leading Core Technology
Realizes fast deployment, efficient inference and elastic services of large models on the cloud platform, and optimizes large model inference performance across the stack
TensorDeck
Intelligent Scheduler
Dynamically monitors and analyzes resource usage, automatically selects the best computing node based on model requirements, node load, etc.
TensorCabin
Intelligent Computing Development SDK
Developers only need to simply write model calling code according to examples to quickly generate inference images, and push the images to the cloud platform to complete model deployment
TensorOS
Self-developed Ultra-fast Container Technology
Container operating system specifically designed for AI inference tasks, specially optimized to meet the performance requirements of AI model inference, shortening cold start time and saving memory
Cold Start Time
Cold Start Time Acceleration
The team has optimized the container runtime and adopted a three-level storage architecture, significantly shortening the cold start time, making the startup speed 3 times faster than other solutions
Elastic model inference
Turn packaged AI models into on-demand services
Shengsuanyun Serverless AI helps teams deploy proprietary models and inference services with packaging tools, intelligent scheduling, and an optimized container runtime that scales computing resources with real requests.
Simplify model delivery
Use tools and examples to package a model, upload an image, and shorten the path from model to API.
Pay for actual compute
Meter resources used by inference instead of keeping long-running idle GPU capacity.
Model service launch workflow
Create a repeatable delivery path across models, images, and production inference traffic.
- 1
Prepare the model
Confirm files, dependencies, I/O formats, memory requirements, and expected concurrency.
- 2
Build and upload an image
Package the inference service with the provided workflow and upload a runnable image.
- 3
Publish and validate
Configure resources and scaling, then test latency, throughput, and cost with real requests.
Serverless AI frequently asked questions
- Which models can use Serverless AI?
- Models that can be packaged as inference services may be suitable. Compatibility depends on dependencies, image environment, memory, and deployment settings.
- Do I need to lease a GPU continuously?
- The service is designed around on-demand resources and meters the compute used by inference workloads.
- How is a model deployed?
- Prepare inference code, build and upload an image, configure resources, and publish the service from the platform.
- Is enterprise customization available?
- Yes. Contact the business team using the details displayed in the hero section to discuss deployment and resource requirements.