DengCloud Platform
In-house inference engine × heterogeneous scheduling
Inference optimization lowers the cost per unit of compute; heterogeneous scheduling turns scattered accelerators into a standardized, programmable service.
One API above, any chip below — which is what we mean by an open platform for the compute era: developers should not need to care about hardware differences.
↓30%+
Lower inference cost
↑85%
Higher GPU utilization
99.9%+
Monthly availability
Technical pillars
Four components make up the technical foundation
Each layer maps directly to something a customer can feel: cost, stability, developer velocity, and credible billing.
In-house inference engine
Dynamic batching, KV cache optimization, and multi-GPU parallelism deliver high-throughput, low-latency inference — 30%+ lower inference cost and 85% higher GPU utilization than a conventional forwarding setup.
Heterogeneous scheduling platform
Unified scheduling across chip architectures, supporting domestic and mainstream multi-generation GPUs. Automatic failover and load balancing sustain 99.9%+ monthly availability.
Open platform architecture
An OpenAI-compatible API with a consistent account and billing model, so business teams integrate once instead of rebuilding per model vendor.
Metering and billing engine
Built in-house rather than wrapped around a vendor counter, so usage is attributable per project and every line on a bill can be independently verified.
Platform architecture
Four layers, bottom to top, hiding complexity as they go
The point of the platform is to keep complexity inside: what customers see is a stable interface and a bill they can check.
- L4
Access layer
OpenAI-compatible API, console, usage dashboard, and alerting
Business teams see one interface and one account — multiple models, chips, and regions are entirely transparent above this line.
- L3
Scheduling layer
Heterogeneous resource onboarding, intelligent routing, failover, and load balancing
Routing decisions follow measured operator capability and benchmarks, placing each job on the most suitable chip and failing over within 30 seconds.
- L2
Inference layer
In-house engine: dynamic batching, KV cache optimization, multi-GPU parallelism
Continuously raising the number of requests served per unit of compute is the direct reason we can offer low latency and low cost together.
- L1
Resource layer
Domestic and mainstream multi-generation GPU pools, distributed storage, high-speed interconnect
Compute is standardized and programmable, so customers are not locked to a single chip vendor.
Put the platform behind your business
From single-model validation to cross-architecture production deployment, Dengjia provides one interface, one metering system, and one operations model.
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