How do I ship an AI feature to production?
Model choice, APIs and gateways, serving, and the ops that keep it honest: evals, observability, guardrails.
The path.
- How to Choose a Model The honest answer is to shortlist from benchmarks and reputation, then run a small eval on your actual task.
- APIs, Inference Platforms & Gateways Four rough tiers, each with different trade-offs on model access, speed, price, and control.
- Inference, Serving & Optimization TTFT (Time To First Token). Latency until the first token appears. Dominated by the prefill (prompt-processing) phase and prompt length.
- LLMOps: Evals, Observability & Guardrails Shipping LLM features without evals and observability is flying blind: outputs are non-deterministic, quality is subjective, and regressions are silent.
- Pricing & Cost Reference Pricing is a Frontier Model Comparison snapshot. This section is the durable part: how to reason about cost so you're not surprised by a bill.
Check yourself.
One question per step. Take it cold to find where to start, or after reading to see what stuck. Nobody's grading you.
Best so far:
Question 1 of 5
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What's the most reliable way to choose a model for your use case?
Model rankings flip depending on the workload, so benchmarks and reputation are only good for a shortlist. A small eval on your actual task makes the final call.
Covered in step 1 · How to Choose a Model -
Your LLM feature just moved past prototype and has real traffic. What's the recommended next infrastructure step?
Most teams past prototype should run a gateway so no application code is welded to one vendor. It centralizes key management, fallback, rate limiting, and cost tracking.
Covered in step 2 · APIs, Inference Platforms & Gateways -
Users complain your chat feature feels slow to start responding. What most likely dominates time to first token?
TTFT is dominated by prefill, the compute-bound phase that processes the whole prompt, so long prompts delay the first token. Decode speed only affects how fast text streams afterward.
Covered in step 3 · Inference, Serving & Optimization -
What's the highest-ROI evaluation investment for an LLM feature in production?
A golden dataset is your regression suite: run it on every prompt or model change and diff the scores. Even 50 to 100 examples catch most regressions.
Covered in step 4 · LLMOps: Evals, Observability & Guardrails -
Your production agent resends the same big system prompt on every request. What's the single biggest cost lever?
Cached repeated prefixes are discounted ~90% on input (sometimes ~99% on hits). It is the single biggest win for agents and RAG that resend the same context, which is why the stable part of a prompt should come first.
Covered in step 5 · Pricing & Cost Reference