Products & Services

Three business lines, one account and one bill

A complete path from model access to compute rental — usable on their own, or combined as your business stage changes.

Most customers validate business value with the Token Factory first, then move core models into private deployment and run training and batch workloads on GPU hourly rental.

Compare the three lines Talk to Our Team

DengCloud Product Matrix

Four product lines covering the full path

Each line has its own capability boundary and delivery model. Open a card to see integration details and common questions.

<500msAverage latency

High-Quality Token Factory

One endpoint, the full model lineup on demand

OpenAI-compatible. One API key reaches every major model. Under 500 ms average latency, 99.9%+ monthly availability, 300+ billion tokens per day.

Full coverage: DeepSeek / GLM / Kimi / MiniMax / Qwen
Migrate by changing endpoint and API key — zero rework
Full operations dashboard with automatic failover

View model catalog

2weeksFastest delivery

Private Deployment & Post-Training

Data stays in your domain, models fit your business

On-premise deployment of open models, industry fine-tuning, and domestic-stack adaptation — training and inference data never leave your security boundary.

Domestic stack: compatible across domestic chips and operating systems
Post-training, LoRA adaptation, and knowledge distillation
Live in as little as 2 weeks, from environment setup to launch

See deployment options

from1hourMinimum rental

GPU Hourly Rental

Elastic compute, billed by the hour

H100, A100 and domestic accelerators on demand. Scale for a training peak and release when the project ends — no procurement cycle, no idle hardware.

Billed by the hour, starting from one hour
H100 / A100 / domestic accelerators available
High-speed storage and interconnect ready out of the box

Check availability

↓30%+Lower inference cost

DengCloud Platform

In-house inference engine × heterogeneous scheduling

An in-house inference engine lowers the cost per unit of compute, while heterogeneous scheduling turns scattered accelerators into a standardized, programmable service.

Dynamic batching / KV cache optimization / multi-GPU parallelism
Unified scheduling across domestic and mainstream multi-generation GPUs
One interface across the full model lineup, transparent to models and chips

Explore the architecture

How to choose

Start from your stage, not the product list

If you are not sure where to begin, match yourself to one of these three paths.

Validate: prove the business case first
Before the use case and model choice are settled, call any model on demand through the Token Factory. A few lines of code is enough, with no upfront commitment and no lock-in.
Production: bring core models inside your boundary
Once the scenario works and data sensitivity rises, move core models into your own or an isolated environment so data never leaves, and post-train on your industry data.
Scale: drive the compute cost down
Run training, batch, and simulation workloads on GPU hourly rental — scale elastically by the hour instead of buying for the peak and paying for idle hardware.

Explore the platform architecture

Not sure which product line fits?

Tell us your scenario and target, and our team will suggest a combination with a cost estimate.

Explore platform capabilitiesBusiness response, Mon–Fri 9:00–18:00 (CST)