AI Stack Selection & Architecture
We help you build on a model-agnostic foundation — right model for each workload, evaluation framework in place, cost and rate limit controls from day one.
Most early-stage AI products are built on a single model chosen by whoever wrote the first prompt. That works for a demo. It doesn't work when you're handling real traffic, getting rate-limited at 2am, or trying to explain your AI risk management to a Series A investor.
The numbers confirm what we see in practice. According to Datadog's 2026 State of AI Engineering report, drawn from production telemetry across more than 1,000 organizations, 70% of engineering teams now run three or more AI models — and most have no governance plan for them. Rate limit errors are the number-one production failure mode. And LLM technical debt compounds when teams add models without retiring old ones.
We design AI stacks that are deliberate and durable: the right model for each task, a routing and fallback strategy, prompt caching to control costs, observability so you can see what's actually happening, and an evaluation framework so you know when quality drifts. We've seen what happens when teams skip this — and we've also seen how fast it can be fixed when someone with the right experience steps in early.
What's Included
- Current stack audit: models, prompts, orchestration layer, and cost profile
- Model selection framework for each use case in your product
- Rate limit and fallback architecture design
- Observability setup recommendations (tool-agnostic)
- Prompt cost optimization review
- Written architecture decision record you own
How Engagements Work
| Engagement | What's Included | Fee |
|---|---|---|
| AI Stack Audit | Current-state audit of models, prompts, orchestration, and cost profile with written findings (1–2 weeks) | $6,000–$12,000 |
| Architecture Design Sprint | Model selection, routing and fallback strategy, observability recommendations, and architecture decision record (2–4 weeks) | $15,000–$35,000 |
| Ongoing Advisory | Monthly AI infrastructure review and architecture decision support as part of a Fractional CTO retainer | Included in retainer |
Nonprofits and mission-driven organizations receive a 10–15% discount on all engagements.
Who It's For
Funded founders with an AI product in production or near-production who want to build it right before scale forces the issue. If you're still in early prototype stage, the right starting point is often our AI Stack Readiness Assessment — it takes 3 minutes and tells you exactly where the gaps are.
Why Tristella
This work is anchored by John M., who has spent 30 years in technology leadership including roles as VP of Engineering and CTO, and who now advises growth-stage companies on the exact infrastructure decisions that determine whether an AI product survives first contact with real traffic. The judgment about which corner to cut and which corner never to cut comes from having made those calls in production, not in theory.