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Overview

The core goal of R9S is to provide a stable, low-latency, observable, and guaranteed calling path to multiple regions, multiple models, and multiple backend suppliers on top of a unified access layer. Our value proposition is mainly reflected in the following three aspects:

  • Unified Access and Broad Compatibility: We provide an OpenAI-compatible API format, allowing you to access hundreds of models from different global suppliers (such as OpenAI, Anthropic, Google, etc.) through a single endpoint. This greatly reduces your integration and migration costs.

  • Distributed Guarantee and Excellent Performance: R9S relies on globally distributed edge nodes and region-aware routing technology to intelligently select the optimal path for your requests, ensuring nearby access and significantly reducing cross-border access latency. Our architecture is designed for high availability, with cross-regional failover and multi-supplier disaster recovery capabilities.

  • Enterprise-level Governance and Transparency: We provide end-to-end request observability, including detailed metrics, logs, and tracing. Through built-in App Attribution and transparent measurement, you can easily achieve multi-dimensional cost analysis and budget control. Our commitment to service quality is clear, and we provide production-level availability guarantees through a Service Level Agreement (SLA).

The following table summarizes the core value points of R9S and compares them with industry references:


DimensionR9S PropositionReader Benefit & Landing Point
Unified AccessUnified endpoint, unified message format, unified authenticationMinimal migration cost; reduced access complexity
Models & SuppliersMulti-model and multi-supplier aggregationFreedom of choice and cost optimization
Routing StrategyRegion-aware, multi-condition routing, explicit fallback chainLow latency and high reliability; controllable failover
Global NodesCross-regional nearby access and isolationReduced cross-border latency; meets data residency requirements
Streaming CapabilityStreaming/non-streaming compatibleGood interactive experience; compatible with existing ecosystem
ObservabilityEnd-to-end metrics, tracing, log correlationFast problem location; traceable governance
Attribution & TransparencyApp attribution and leaderboardsTransparent measurement and ecosystem exposure
SLA & GuaranteeClear SLO/error budget and compensationAlignment of procurement and operations; compliance and trustworthiness
Billing & QuotasFine-grained quotas and rate limitingAnti-abuse; multi-dimensional cost control
Security & ComplianceKey management, auditing, data residencyAlignment with enterprise-level auditing and residency requirements

  • Unified Endpoint: All AI model calls are made through the single endpoint https://api.r9s.ai/v1, which simplifies your code and configuration.
  • Model Identifier: We use the model-name format (e.g., gpt-5.2pro, DeepSeek-v3.2) to uniquely identify a model. For a complete list of models, please refer to the R9S console.
  • Region-Aware Routing: R9S automatically selects the best access node and backend service path based on your request’s origin and the health of global nodes to achieve the lowest latency.
  • Model Fallbacks: You can configure a model fallback strategy. If the primary model call fails or times out, the system automatically tries the other models in the order you specify, ensuring business continuity while keeping costs and latency low.
  • App Attribution: By passing HTTP-Referer and X-Title in the request header, your calls will be correctly attributed, facilitating subsequent cost analysis and ecosystem exposure.
  • Service Level Agreement (SLA): We make clear commitments to the availability and performance of the platform and provide corresponding compensation mechanisms. For details, please refer to the “Service Guarantee and SLA” chapter.

R9S has deployed edge nodes in multiple geographical locations around the world, including North America, Europe, and the Asia-Pacific region. When your request arrives at R9S:

  1. DNS Smart Resolution: Your request will be resolved to the nearest and healthiest edge node.
  2. Region-Aware Routing: The edge node determines which provider in which region to forward the request to based on preset routing policies (comprehensively considering latency, load, cost, and availability).
  3. Nearby Access: This architecture ensures the shortest data transmission path, significantly reducing network latency, especially for applications that require cross-border access to models.