Deploying LLM Models on Cloud Infrastructure

Deploying large language models on cloud infrastructure has become a standard requirement for engineering teams that need data sovereignty, custom fine-tuning, or specialized reasoning pipelines. The path from a downloaded model weights file to a production API, however, is rarely straightforward. You must provision GPU instances, configure inference engines, manage autoscaling groups, and optimize batching strategies, all while keeping latency predictable and costs under control.

For many teams, the operational burden of self-hosting distracts from product development. Oxlo.ai offers a developer-first alternative: a fully managed inference platform with request-based pricing, OpenAI SDK compatibility, and no cold starts. Before you commit months to building internal infrastructure, it is worth understanding what production self-hosting actually requires and where a managed platform fits.

The Hidden Costs of Self-Hosted Inference

Cloud GPUs are expensive, and inference workloads are spiky. A standard deployment using NVIDIA A100 or H100 instances on AWS, GCP, or Azure requires you to pay for idle capacity or accept cold-start latency during scale-up events. You also need engineering time to maintain serving stacks such as vLLM, TensorRT-LLM, or Text Generation Inference (TGI).

Beyond hardware, you must handle model parallelism, quantization, continuous batching, and KV-cache management. Each optimization adds complexity. For long-context workloads, memory pressure increases non-linearly with sequence length, which can crash under-provisioned pods or degrade latency without careful tuning.

Architecture Patterns for Cloud Deployment

If you still need to self-host, a typical production stack looks like this:

• Inference engine: vLLM or TGI for high-throughput serving.
• Orchestration: Kubernetes with GPU operator and node auto-provisioning.
• Routing: A reverse proxy or custom scheduler to distribute requests across replicas.
• Observability: Prometheus metrics for queue depth, time-to-first-token, and inter-token latency.

Here is a minimal Kubernetes deployment snippet for vLLM:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: llm-inference
spec:
  replicas: 2
  selector:
    matchLabels:
      app: llm
  template:
    metadata:
      labels:
        app: llm
    spec:
      nodeSelector:
        cloud.google.com/gke-accelerator: nvidia-l4
      containers:
      - name: vllm
        image: vllm/vllm-openai:latest
        args:
        - --model
        - meta-llama/Llama-3.3-70B-Instruct
        - --tensor-parallel-size
        - "2"
        resources:
          limits:
            nvidia.com/gpu: "2"
            memory: "80Gi"
        ports:
        - containerPort: 8000

This configuration assumes tensor parallelism across two GPUs. You must also configure a Horizontal Pod Autoscaler that responds to custom GPU metrics, not just CPU, which adds further complexity to your cluster.

When to Build and When to Use Oxlo.ai

Self-hosting makes sense when you have strict regulatory requirements that mandate on-premises data, or when you run highly specialized models that no provider hosts. For everything else, managed inference platforms remove operational risk.

Oxlo.ai provides access to 45+ open-source and proprietary models, including Llama 3.3 70B, DeepSeek R1 671B MoE, and Qwen 3 32B, through a single endpoint. Because Oxlo.ai charges per request rather than per token, costs remain predictable even for long-context prompts and multi-step agentic workflows. Token-based providers scale costs with input length, which can make agent loops and RAG pipelines prohibitively expensive. Oxlo.ai flips this model: one flat cost per API call.

You can compare plans and explore the details at https://oxlo.ai/pricing.</p