LLM integration in Austin.

The technical reality of LLM features in Austin products.

Questions from Austin buyers.

We run a multi-tenant B2B SaaS. How do we attribute LLM costs to individual customers?

Every model call passes through a metering layer that records tenant ID, feature name, token counts in both directions, and model used. Those events land in your existing billing or analytics store (Postgres, Segment, or a usage-billing platform), so finance can see cost per customer and product can see which accounts actually use the feature. The same layer enforces per-tenant rate limits, which protects you from one customer's batch job consuming the whole API quota. Metering is part of every multi-tenant integration we build because without it, AI features become an unattributable line item that finance eventually questions.

Can we add AI features to our product without a re-platform or a big refactor?

Almost always. An LLM integration is, structurally, a new service boundary: your existing app calls an internal endpoint we add, and that endpoint handles prompting, model calls, retries, caching, and logging. Your data model and frontend framework stay put. The cases that need real refactoring are rare and visible in discovery: usually a monolith with no async job infrastructure that wants long-running document processing. In that case we scope a small queue (typically one week of work) rather than a re-platform. Most Austin SaaS integrations ship behind a feature flag in two to four weeks without touching the core architecture.

Our test and yield data is sensitive fab IP. Can summarization run without data leaving our environment?

Yes. For semiconductor and hardware teams, we deploy against models running inside your cloud tenancy (Bedrock or Azure OpenAI private endpoints) or, where policy requires it, open-weight models like Llama or Qwen served on your own GPUs. Test logs, yield reports, and equipment data never reach a public API. The trade-off is honest: self-hosted open-weight models trail frontier models on reasoning-heavy tasks, so we benchmark on your actual logs before committing. Summarization and anomaly description over structured test output is exactly the kind of task where a well-prompted open-weight model usually clears the bar.

What does LLM-based catalog enrichment actually cost at e-commerce scale?

Per-SKU costs are driven by model size and prompt design more than anything else. Generating titles, attribute fills, and descriptions for a 50,000-SKU catalog on a small model with cached system prompts typically lands under $500 in inference, processed as a batch job over a weekend. The engineering cost is in quality control, not generation: brand-voice constraints in the prompt, attribute validation against your taxonomy, and a sampled human review queue for the long tail. We set acceptance thresholds with your merchandising team first, then tune until the sample passes. Regeneration after a prompt fix is cheap; re-reviewing 50,000 SKUs by hand is not.