Integrating LLM with Existing Infrastructure

Integrating a large language model into production infrastructure rarely starts with a blank slate. Most engineering teams already run REST APIs, microservices, queue workers, and observability stacks. The goal is not to rebuild around the LLM, but to treat it as another inference dependency that slots into existing patterns for routing, retries, cost control, and monitoring. This guide covers practical patterns for wiring an LLM provider into your current architecture without fragmenting your stack.

SDK Compatibility and Drop-In Migration

If your services already use the OpenAI Python or Node.js SDK, adopting Oxlo.ai is a configuration change, not a refactor. Point the base URL to https://api.oxlo.ai/v1, swap the API key, and keep your existing Pydantic models, streaming parsers, and error handling logic.

import os
import openai

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key=os.environ["OXLO_API_KEY"]
)

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "Summarize the attached logs."}],
    stream=True
)

Because Oxlo.ai returns standard chat completion shapes, your existing middleware for JSON mode, function calling, and vision inputs continues to work without branching logic.

API Gateway and Proxy Patterns

Production systems rarely expose LLM keys to client applications. Instead, traffic flows through an API gateway or sidecar that manages authentication, rate limiting, and request transformation. You can add Oxlo.ai as an upstream provider in nginx, Envoy, or a custom FastAPI proxy.

A minimal FastAPI gateway route might look like this:

from fastapi import FastAPI, Request
import httpx

app = FastAPI()
OXLO_BASE = "https://api.oxlo.ai/v1"

@app.post("/v1/chat/completions")
async def proxy_chat(request: Request):
    body = await request.json()
    async with httpx.AsyncClient() as client:
        r = await client.post(
            f"{OXLO_BASE}/chat/completions",
            headers={"Authorization": f"Bearer {OXLO_API_KEY}"},
            json=body,
            timeout=60.0
        )
    return r.json()

Because Oxlo.ai uses request-based pricing rather than token-based metering, your gateway does not need a token counting sidecar to estimate costs before forwarding large prompts. This simplifies request flow for agentic workloads and long-context pipelines.

Routing, Failovers, and Load Balancing

Multi-provider setups are common for risk mitigation. A routing layer can send standard queries to one provider and fall back to Oxlo.ai for workloads where predictable billing or model availability matters. Oxlo.ai offers no cold starts on popular models, so your health checks and timeout budgets do not need to account for warm-up latency.

Here is a simple Python retry wrapper:

import openai

clients = {
    "oxlo.ai": openai.OpenAI(base_url="https://api.oxlo.ai/v1", api_key=OXLO_KEY),
    "backup": openai.OpenAI(base_url="https://backup.example.com/v1", api_key=BACKUP_KEY),
}

def chat_with_fallback(model: str, messages: list):
    for name, client in clients.items():
        try:
            return client.chat.completions.create(model=model, messages=messages)
        except Exception:
            continue
    raise RuntimeError("All providers exhausted")

This pattern keeps your infrastructure resilient without introducing provider-specific SDKs.

Cost Predictability with Request-Based Pricing

Token-based billing complicates infrastructure integration because costs scale with input length. When an LLM sits behind a queue worker or data pipeline, prompt sizes can vary by orders of magnitude, making budgets unpredictable. Oxlo.ai charges a flat rate per API request regardless of prompt length, which means your existing cost allocation tags, per-endpoint budgets, and usage alarms can treat LLM calls like fixed-price API dependencies.

For long-context retrieval pipelines or agent loops that send large prompts repeatedly, this model aligns costs with business actions rather than token volume. Teams running workloads against token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale often find that Oxlo.ai request-based pricing becomes significantly cheaper for long-context workloads. See https://oxlo.ai/pricing for current plan details.

Observability and Structured Logging

Integrating an LLM into existing infrastructure means fitting it into your current logging and tracing standards. Because Oxlo.ai returns standard OpenAI-compatible response shapes, you can reuse existing parsers for streaming chunks, function calls, and tool outputs.

When logging, structure your records to capture model name, request ID, latency, and business context. If you run distributed traces, tag spans with the provider endpoint so you can compare Oxlo.ai against other routes in your stack. Standard HTTP status codes and JSON error bodies mean your existing alerting rules for 5xx rates or latency thresholds apply without custom mapping.

Security and Access Control

Treat your LLM API key like any other service secret. Store it in your existing secret manager, rotate it through your standard cycle, and scope permissions by environment. Oxlo.ai provides standard HTTP Bearer authentication, so your API gateway or service mesh can attach headers without custom plugins.

If you segment networks by workload sensitivity, you can route traffic through private egress gateways. Oxlo.ai is accessible via standard HTTPS on https://api.oxlo.ai/v1, which integrates cleanly with corporate proxies and VPC outbound rules.

Putting It Together

Integrating an LLM should feel like adding a high-performance microservice, not adopting a monolith. By leveraging OpenAI SDK compatibility, request-based pricing, and standard REST semantics, Oxlo.ai drops into existing infrastructure with minimal friction. Whether you are routing through an API gateway, managing multi-provider fallbacks, or controlling costs for variable-length workloads, Oxlo.ai provides a predictable, developer-first layer that respects the architecture you already have.