Cloud-native applications are increasingly built around large language models as core inference services rather than peripheral features. Whether you are building autonomous agents, retrieval pipelines, or multimodal content generators, the inference layer determines your latency budget, cost structure, and scaling behavior. Oxlo.ai provides a developer-first inference platform designed for these exact workloads, offering request-based pricing and full OpenAI SDK compatibility so you can integrate LLMs into cloud architectures without rewriting client code or predicting token burn.
LLMs as Cloud Infrastructure
Modern cloud applications treat LLMs like any other microservice. They run behind API gateways, scale horizontally behind load balancers, and return results via streaming endpoints. The difference is computational intensity. A single request to a reasoning model such as DeepSeek R1 671B MoE or GLM 5 can trigger billions of floating-point operations, which makes provider choice a first-class architectural decision.
Oxlo.ai approaches this by hosting 45+ open-source and proprietary models across seven categories, from chat and reasoning to vision, audio, and image generation. Because the platform exposes a fully OpenAI-compatible API, you can point an existing client at https://api.oxlo.ai/v1 and begin testing models immediately. There are no cold starts on popular models, which means your cloud functions and containerized workers do not pay a latency penalty on first invocation.
Architectural Patterns
Most production cloud workloads fall into three patterns. Retrieval-Augmented Generation uses embedding models to ground LLM outputs in private data. Agentic workflows chain multiple tool
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