Aussie AI Blog

Scaling Your AI Wrapper Architecture

March 12th, 2026

by David Spuler, Ph.D.

Basic Wrapper Problems

Everyone has built a "wrapper" architecture using one of the major token APIs. The idea is that you add your LLM to your "special sauce" in regard to:

Prompting
UX capabilities

It ain't so special. Anybody can copy that in a weekend without AI, or a few hours using AI coding.

The other problem is that it won't scale. This method works as a demo, but not in production.

Scalability Problems

A simple LLM APi wrapper architecture will fall over with only a few users. Your wrapper needs to take care of other things when you get more users:

Rate limits (on queries and on tokens)
Request routing and queueing — multiplex lots of user queries to lots of model endpoints and back again (e.g., using Kafka or RabbitMQ)
Load balancing of LLM queries — often part of message routing solutions, but also somewhat a distinct issue.
Graceful API error handling (e.g., timeouts, rate limits, retries, etc.)

Some other considerations:

Per-session conversational history storage and reuse (most API endpoints are "stateless")
Multi-server session handling (e.g., database interface or "sticky sessions" at network load balancer level).
Logging (with consideration of privacy restrictions)
Token count statistics (to compare against your bills later)
Advanced rate limiting (e.g., retries with exponential backoff)
Per-user token budgets (e.g., do you block heavy users, degrade service, make them pay more for "credits", or eat the loss?)

If you're running queries at a loss, well, you can always make it up on volume (that's a joke, not, you know, business advice!).

Security of AI Wrappers

Your AI server could have some security issues:

Protecting your LLM input points
Avoiding bots sending queries and other misuse
Auth problems
Guardrails, safety and refusal modules (some LLM APIs are good at this being handled in the model's response, but for some others, it's up to you to filter or block prompts and/or LLM output).
Abuse detection (of users, bots, hackers, and other miscreants)
Malicious prompt injection attacks (and the good old SQL injection attacks, too)
Basic website security (e.g., Linux server patching)

Some of the issues include:

Can't leave your API key anywhere (e.g., not in a form field)
Proxy token server is required (because you can't just put your API key in a client-side JS file)
No way to piggyback onto users who are paying for a "Pro" version of an LLM.

Web Server Optimizations

Don't forget basic website optimizations, which is not specific to AI applications:

DNS TTL
Etags configurations
Apache vs Nginx
Static files on a subdomain
Linux server optimizations (e.g. "noatime" in /etc/fstab)
JavaScript optimizations
Merge and minify CSS and JS
Critical CSS earlier in page
Full CDN versions
... and many more

There's plenty of advice on the web about this, and even some books written specifically about it.

Optimizing an AI Wrapper

Some of the other considerations for having a more optimal wrapper architecture include:

App-specific system prompt — your basic prompt prefix that is added to all your queries.
Cached tokens — using API support of a cache for prefix tokens to get cheaper token bills (this is based on "prefix KV caching").
Batched tokens — for non-interative applications like SEO article generation, run it overnight.
Multi-model routing — use different models from your main provider, or some cheaper API providers, or various multi-model API aggregator startups (e.g., OpenRouter, Baseten), or your own self-hosted open-source LLMs (where it makes sense).
Monitoring response times — instrumentation for observability of requests going to different API endpoints (with text notifications sent to you at 3am when everything fails).
Stateful APIs — recently available from some providers, takes away the need for your own conversational history manager.
Budget tracking — stopping or degrading capabilities when your cost budget is being exceeded.
Geo-aware load balancing and request routing
Load testing (without sending too many tokens to expensive model APIs).
Streaming of longer model responses
Prioritization of paid user queries
Testing capabilities — send a test query and check availability and performance status.
KPI statistics calculations — totals, averages, etc., for queries, tokens, performance, etc.

And finally, after all that infrastructure, you can get back to doing whatever was special about your "AI app" which is: