Deploying large language models in a cloud environment gives you control over data residency, network latency, and hardware configuration. It also forces you to solve GPU capacity planning, driver compatibility, container orchestration, and autoscaling before you generate your first completion. For many engineering teams, the infrastructure tax overwhelms the model logic. This guide walks through the core decisions for running LLMs on AWS, GCP, or Azure, and shows where an external inference platform like Oxlo.ai can remove the operational burden without sacrificing flexibility.
Provisioning GPU Infrastructure
Running a 70 billion parameter model or a 671 billion parameter mixture-of-experts model on a public cloud starts with securing the right GPU instances. On AWS, that means P4d, P5, or Trn1 instances. On GCP, A3 VMs with NVIDIA H100s. On Azure, NDv5 series nodes. These instances are often capacity constrained, require reserved capacity planning, and depend on specific CUDA drivers and high-bandwidth networking stacks such as EFA or NCCL.
Before you serve a single request, you must configure node pools, install GPU device plugins, and verify that your container runtime can access the underlying hardware. This step alone can consume days of engineering time, and any mismatch between the driver version and your serving framework will surface as silent performance degradation or outright failures.
Container Orchestration and Serving Frameworks
Most teams default to Kubernetes to manage inference workloads. You will need to build or adopt Helm charts for vLLM, Text Generation Inference (TGI), or TensorRT-LLM. Each framework exposes different knobs for continuous batching, KV cache management, and tensor parallelism across multiple GPUs.
Autoscaling GPU workloads is fundamentally slower than scaling CPU pods. A cold start on a large model can take several minutes as weights load into GPU memory. During that window, your queue backs up and user latency spikes. Oxlo.ai eliminates this problem entirely. Popular models on Oxlo.ai are always warm, so the first request returns at the same speed as the thousandth.
Understanding the Cost Model
Cloud GPU instances bill by the second. An idle H100 node still costs money. If your traffic is bursty, you either overprovision and waste budget, or underprovision and drop requests. Token-based API providers such as Together AI, Fireworks AI, OpenRouter, Replicate, and Anyscale shift the infrastructure burden away from you, but their pricing scales with input and output token count.
For long-context retrieval, agentic loops, or large document analysis, token-based costs grow linearly with prompt length. Oxlo.ai uses flat per-request pricing. One API call costs the same regardless of whether you send a one-line prompt or a 100,000 token context window. For long-context and agentic workloads, this model can be significantly cheaper than token-based alternatives. See the exact rates on the Oxlo.ai pricing page.
API Compatibility and Integration
Self-hosted inference endpoints require you to manage load balancing, failover logic, and request serialization. Oxlo.ai offers a fully OpenAI-compatible API, which means you can replace your self-hosted endpoint with a single configuration change. The Python SDK, Node.js client, and cURL examples all work without modification beyond the base URL and API key.
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="your_oxlo_api_key"
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain the tradeoffs between self-hosted LLMs and managed inference APIs."}
],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="")
Oxlo.ai hosts 45+ open-source and proprietary models across seven categories, including general-purpose LLMs such as Llama 3.3 70B and Qwen 3 32B, reasoning models such as DeepSeek R1 671B MoE and Kimi K2.6, code models such as Qwen 3 Coder 30B, and vision models such as Gemma 3 27B. If your cloud deployment currently relies on pulling weights from Hugging Face and building custom containers, switching to Oxlo.ai removes that pipeline entirely.
Architecture Patterns for Cloud Deployments
There are three common patterns for integrating LLMs into a cloud architecture.
- Fully self-hosted: You manage GPU nodes, serving frameworks, and model weights. This pattern makes sense for air-gapped environments, strict regulatory requirements, or heavily customized fine-tuned models.
- Hybrid: Your application runs in your cloud account, but inference traffic routes to Oxlo.ai. You retain control over application state, databases, and ingress while offloading GPU operations, scaling, and model updates.
- Fully managed API: Your compute layer handles orchestration and business logic, and every LLM call goes to Oxlo.ai. This is the fastest path to production and the easiest to budget, because you pay per request rather than per GPU hour.
Observability and Scaling
When you self-host, you must instrument GPU utilization, batch size, queue depth, and memory pressure with tools like Prometheus and Grafana. You are responsible for deciding when to scale out and when to trigger node termination.
With Oxlo.ai, the platform handles infrastructure scaling. Your responsibility shifts to application-level observability: tracking request latency, error rates, and token throughput from the client side. Because Oxlo.ai offers streaming responses, function calling, JSON mode, and vision endpoints, you can build complex agentic workflows without maintaining the underlying inference fleet.
Conclusion
Cloud LLM deployment exists on a spectrum. Self-hosting offers maximum control but demands deep expertise in GPU infrastructure, serving frameworks, and cost optimization. For most engineering teams, the operational overhead is not a competitive advantage.
Oxlo.ai provides a developer-first alternative: 45+ models, flat per-request pricing that favors long-context workloads, no cold starts, and full OpenAI SDK compatibility. Whether you run a fully cloud-native stack or a hybrid architecture, Oxlo.ai lets you focus on application logic instead of infrastructure. Start with the free tier to validate your workloads, then scale as needed.
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