Orq.ai vs SGLang

Orq.ai is an AI gateway and platform for routing, governing and observing requests to models and tools. Through an OpenAI-compatible API, it lists more than 500 models from over 30 providers. Smart Router can select models by cost or quality strategy, with fallbacks, retries and load balancing. Controls include budgets, spend limits, guardrails and PII redaction. Observability covers request traces, cost and token tracking, dashboards and OpenTelemetry ingestion. Integrations include OpenAI and Anthropic APIs, Python and Node SDKs, remote MCP servers, Azure AI Foundry, Vertex AI and compatible endpoints. It supports Claude Code, Cursor, Codex and more than 25 agent frameworks. Deployment options are EU SaaS, VPC and on-premises. Orq.ai says prompts, outputs and traces are not used to train models, and lists EU residency and retention controls. Pay-as-you-go is free, usage-based, with provider-rate model charges and stated allowances.

SGLang is a free, open-source inference framework for serving large language, vision-language and diffusion models. It targets agentic workloads, reinforcement-learning rollouts and large-scale serving, from one GPU to distributed clusters. Runtime features include RadixAttention, prefix caching and multi-GPU parallelism. Listed optimizations include disaggregated prefill and decode, speculative decoding, a zero-overhead scheduler and optimized GPU kernels. Supported models include DeepSeek, Qwen, GPT-OSS, Llama, Mistral and GLM. SGLang works with Hugging Face and OpenAI APIs and exposes OpenAI-compatible endpoints. It installs with pip or Docker and supports self-hosted deployment. Hardware support spans NVIDIA and AMD GPUs, Google TPUs, Intel GPUs and CPUs, Apple Silicon, Huawei Ascend NPUs and Moore Threads GPUs. SGLang Diffusion adds image and video generation. The Apache-2.0 license permits open-source use.