Deploying large language models in production requires more than selecting weights from a leaderboard. Teams must balance latency requirements, data residency rules, and cost structures against the operational overhead of GPU infrastructure. Whether you are serving a fine-tuned Llama 3.3 70B behind a virtual private cloud or running a multi-step agent that calls external tools, the cloud deployment strategy directly impacts reliability and spend.
The Deployment Spectrum
Most production LLM deployments fall into three categories: fully self-managed instances, managed services from a cloud hyperscaler, and third-party inference APIs. Self-managed clusters offer complete control over model weights, custom kernels, and networking, but they require expertise in CUDA drivers, container orchestration, and autoscaling logic. Managed services abstract some of that complexity, yet they still demand capacity planning and reserved instance management. Inference APIs flip the model entirely: you send a request and receive a response, with no visibility into the underlying nodes.
For teams running standardized open-source models such as DeepSeek R1 671B MoE, Qwen 3, or Llama 3.3 70B, an inference API can eliminate weeks of infrastructure work. Oxlo.ai provides a developer-first platform with request-based pricing, meaning one flat cost per API call regardless of prompt length. Unlike token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, this structure does not scale with input length, which makes Oxlo.ai significantly cheaper for long-context and agentic workloads. For these workloads, the savings can be 10-100x compared to token-based alternatives.
Core Infrastructure Components
If you choose to self-host, your stack begins with the inference engine. vLLM remains the popular open-source choice for throughput-heavy workloads thanks to Paged
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