Enterprise MaaS
One-stop enterprise
LLM service platform.
An end-to-end loop from heterogeneous compute orchestration through model training, inference and scenario delivery — for faster, cheaper, more reliable LLM rollout at scale.
Product overview
One platform — closing the loop on the full LLM lifecycle.
Unified heterogeneous compute orchestration
Onboard and intelligently schedule multi-architecture, multi-vendor compute — with deep optimization for domestic accelerators — for centralized, efficient resource management.
Model best practices & performance tuning
Curated reference implementations of leading open-source LLMs, with training/inference tuning that accelerates time to production.
Low-friction calls, broad scenario fit
Visual configuration UI plus OpenAI-compatible APIs — call models across scenarios without deep ML expertise.
Full-lifecycle, resource-efficient management
Unified monitoring, optimization, and reclamation across compute, models and apps — for sustainable long-term operation.
Capability gallery
See the platform in action
Enterprise-grade compute base
Onboard multi-DC, multi-vendor and multi-generation hardware into one elastic compute base.
Training & observability
Visual training monitors, performance tuning and full-stack metrics make every iteration data-driven.
Deep domestic heterogeneous tuning
Operator-level tuning for Ascend, MetaX, Moore Threads, Hygon and more domestic accelerators.
Platform architecture
Six-layer stack — compute to applications, fully integrated
- OpenAI-compatible SDK — zero migration cost
- 30+ ready-to-use business templates
- Visual orchestration — ship an app in 3 minutes
- Text / multimodal / embeddings / rerank — full coverage
- Tagged registry for precise scenario matching
- Built-in evaluation across 20+ dimensions
- Data ingestion → cleaning → governance → evaluation loop
- One-click multi-node, multi-GPU training
- End-to-end visualization for training & fine-tuning jobs
- Continuous batching
- PagedAttention VRAM optimization
- Lossless dynamic quantization, 60–80% fewer ops
- Load- and SLA-aware dynamic routing
- Multi-tenant isolation and quota management
- Self-healing failover and cross-cluster HA
- Deep support for Ascend, MetaX, Moore Threads, Hygon, Cambricon
- Operator-level tuning for peak per-card performance
- Unified monitoring, reclamation and power-saving policies
Why choose us
Six core advantages.
Fast onboarding · agile go-live
- Out-of-the-box: 100+ leading LLMs pre-integrated
- Dynamic image updates — new models supported on day one
- End-to-end toolchain: training, inference, fine-tuning, deployment
High performance · production-grade
- Optimized inference: latency −70%, throughput 3–5× higher
- Smart load balancing across compute and model services
- Second-level autoscaling — balancing performance and cost
Right model · precise capability matching
- Tagged model registry for rapid best-fit selection
- Built-in evaluation suite with 20+ performance dimensions
Low cost · maximize business value
- Optimal compute and VRAM management — large unit-cost reduction
- Lossless dynamic quantization — 60–80% fewer compute ops
Easy to use · empower every team
- Unified compute pool with automated deployment
- Visual interface — get productive in under 3 minutes
- 30+ ready-to-use templates
Strong security · defense in depth
- End-to-end data security & compliance — 99% lower leakage risk
- Real-time attack defense; content-safety accuracy >99%
Use cases
Industries we serve
Customers
Trusted by enterprise teams
"The platform's unique inference acceleration, dynamic routing and VRAM optimization significantly raised our GPU cluster utilization and lowered customer inference costs."
"Unified APIs, flexible fine-tuning and a full toolchain accelerated our AI delivery across finance, government and education."
"We deployed a 100B-parameter industry-specific model; heterogeneous compute management and large/small-model collaboration sharply improved diagnostics and decision-making."
FAQ
What enterprises ask
When should an enterprise deploy a private MaaS platform?+
When you handle sensitive data, need at-scale AI rollout, run mixed domestic/heterogeneous compute, or lack an in-house team to continuously adapt models.
How well does the platform support domestic accelerators?+
Deep native support for Ascend, MetaX, Moore Threads, Hygon, Cambricon and others, with operator-level optimization.
Can non-technical staff use it quickly?+
Visual UI plus 30+ templates let business users deploy and call models in under 3 minutes.
Typical deployment timeline?+
Standard delivery in 2–6 weeks across PoC → pilot → scale, with on-site experts and 24×7 support.
Ready to roll out LLMs at scale?
Our solution architects will design the rollout with you.