Manifest vs SGLang

Manifest is an open-source LLM gateway for routing model requests, switching providers, repairing certain failures, and tracking inference spending. It provides one endpoint compatible with OpenAI and Anthropic models and subscriptions. When a provider goes down, fallback routing retries through another provider. Autofix repairs malformed requests and resends them when retries alone cannot resolve the error. Observability shows spending by harness, which models handled requests, and what failed. Manifest connects API keys, paid subscriptions, local models, and custom endpoints, including Ollama, llama.cpp, and LM Studio. The self-hosted gateway runs with Docker, works offline, requires no account, and supports amd64 and arm64. Self-hosted routing and request data remain on customer servers except documented telemetry and Autofix flows. The Cloud Free plan allows 10,000 routed requests per calendar month and cannot reach local models on the user's machine. Free includes unlimited self-hosted use; Pro costs $19 a month with unlimited Cloud requests and 365-day dashboard retention.

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.