Capabilities
Agentic MaaS offers broad model support and standardized access, abstracting away underlying differences so developers can focus on business innovation.
Comprehensive multimodal coverage：Supports 100+ models across large language models, image understanding, image generation, video generation, and embeddings, enabling a wide range of application scenarios.
Deep optimization for leading open-source models：Provides high-performance service optimizations for leading open-source models such as DeepSeek, GLM, Kimi, MiniMax, and Qwen.
Developer-friendly API compatibility：Fully compatible with OpenAI- and Anthropic-style APIs, meeting diverse needs with zero migration cost.
Comprehensive support for advanced complex features：Support advanced capabilities such as function calling, structured output, and streaming responses, enabling direct use in agent development.
An enterprise-grade high-availability architecture that provides a unified entry point for model services and intelligent traffic management, ensuring stable service delivery and smooth upgrades and releases across diverse scenarios.
Request feature awareness：The unified gateway automatically identifies request characteristics such as type, length, and complexity.
Intelligent routing and distribution：Requests are intelligently routed to the optimal instance based on request feature analysis, with support for cache-aware routing.
Multi‑strategy adaptation：Supports policy‑based routing across multiple dimensions, including region, user, priority, and resource pools.
Traffic governance and service health protection：Supports sub-second traffic switching, one-click rollback to stable versions, and automatic isolation of problematic instances.
Through system-level engineering optimizations, we maximize inference performance and resource utilization efficiency, while ensuring accuracy and user experience.
High-accuracy inference assurance：Supports precise inference for ultra-large-scale models. Tool calling, structured output, and related scenarios remain fully consistent with the original vendor.
Extreme performance optimization：Achieves 3× or greater improvements in time-to-first-token and token throughput, with dedicated sequence-parallel optimizations for 128K+ context lengths.
Observability and system assurance：Supports real-time performance monitoring and request traceability, automatically identifies abnormal requests, locates performance bottlenecks, and enables early issue detection.
Intelligent resource management and orchestration capabilities enable sub-second startup, minute-level elastic scaling, rapid deployment, and high service availability.
Business-aware elastic scaling：Automatically adjust instance counts based on actual business workloads, with rapid scale-out during peak periods to meet demand.
Ultra-fast instance startup：Model caching and container image caching significantly reduce cold‑start times.
Multi-layer high-availability protection：Service continuity is ensured through live workload migration, cross-AZ high-availability architecture, and automatic restarts of abnormal instances.
A range of billing models and subscription options is provided to support customers of different sizes and usage scenarios,keeping costs flexible and predictable.
Multiple billing options：Supports per-token billing, monthly plans based on concurrency, and Coding Plan subscriptions—addressing diverse traffic patterns and business needs.
Real-time usage visibility：Usage is tracked at the individual-model and per-token level, delivering full cost transparency with real-time views of remaining quotas and usage trends.
Agent-powered operation system with self-healing and self-optimizing capabilities ensures reliable 24/7 operation of model services.
Comprehensive health monitoring：Continuous analysis across services, instances, requests, and business workloads provides full operational visibility.
Automatic anomaly detection：Logs, metrics, and request traces are analyzed automatically to quickly identify root causes.
Intelligent capacity management：Historical workload patterns are used to anticipate traffic peaks, maintain optimal capacity levels, and generate scaling recommendations
Human-in-the-loop Change Management: Faulty instances are automatically removed, while service upgrades and change plans are determined through human-machine collaboration, with fully traceable audit records.
Full-stack capabilities: model access, traffic governance, inference optimization and resource scheduling
Agent-enabled LM inference system for peak engineering optimization
realizing industrialized AI products
Advantages
Use Case
A Leading Large Model Unicorn Company
The company delivers AI services to millions of users through consumer web applications, mobile apps, and enterprise‑grade B2B APIs. It processes tens of millions of inference requests daily, operates 24/7, and must consistently balance model accuracy, response speed, and service stability.Before Using the Service:
Deployment depends on multiple departments: Every model update required traffic forecasting and cross-team resource coordination. Significant ongoing effort was spent optimizing inference performance, leaving limited capacity to focus on improving model quality.
Accuracy loss from quantization acceleration: With conventional providers, accuracy degradation introduced by quantization was difficult to identify promptly. This led to lower tool-calling accuracy, negative user feedback, and an increase in enterprise customer complaints.
Smoothly handling tens of thousands of QPS of sudden traffic spikes：Zero downtime during marketing campaigns,15% increase in user retention, and issue resolution time reduced from hours to minutes.
Significant gains in team efficiency: Business scale increased by an order of magnitude, allowing teams to focus on model performance iteration.
Quantifiable and evaluable accuracy：A standardized accuracy evaluation framework for model services was established, helping build strong, measurable credibility for model capabilities.
A Multimodal AIGC Platform
Client provides text-and-image generation services, running a mix of synchronous and asynchronous inference tasks. Traffic fluctuates sharply in response to trending topics and market dynamics.
Scaling unable to keep pace with traffic surges: Sudden trends caused request volumes to increase by up to 100×. Slow instance scaling led to a sharp rise in production failure rates and a degraded user experience.
Unclear scheduling for mixed workloads: Synchronous and asynchronous inference tasks shared resources without clear prioritization, making targeted performance optimization difficult. Image generation latency was high, resulting in frequent user drop-off.
Complex version management: Multiple versions of models and runtime environments had to be maintained in parallel. Frequent business updates made it hard to coordinate version control with traffic routing.
Minute-level rapid scaling: All surge traffic was routed through the platform, with scaling completed within one minute, preventing the loss of millions of user requests.
Dramatic inference performance improvements：Inference task speed increased by over 80%,conversational text throughput more than doubled, and user churn decreased by 71%.
Optimization in iteration efficiency：Different model APIs could be flexibly integrated based on business needs, without building custom queueing or dispatch mechanisms. Iteration cycles were shortened from weeks to days.
Infinite Computing, Accelerating the Future of AGI
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