Latency, Cost, and Sanity: Lessons from AI Inference in Production

Building AI demos is fun.
Running AI inference at scale is… humbling.

You start with a model that works beautifully in your notebook.
Then you deploy it, get real users, and suddenly you’re debugging latency spikes, cost overruns, and rate limits at 3 a.m.

This post is about what I learned from that transition — scaling multiple inference-heavy projects on AWS and GCP using limited credits, tight budgets, and a lot of patience.

1. Latency Is the Real UX

When people think about model performance, they obsess over accuracy.
But in production, latency is the only metric users actually feel.

Even a small delay creates the illusion that the system is “thinking” — and not in a good way.

I learned this fast while building my video interview analysis app.
Every millisecond between “submit” and “response” broke user flow.

Lessons

• Cache aggressively. Everything. Transcripts, embeddings, even prompts.
• Batch small requests — one large call is faster and cheaper than ten small ones.
• Use async pipelines with user-facing feedback (“Analyzing your response…”).
• If it takes longer than 2 seconds, acknowledge the wait. Perception management is half the battle.

Takeaway: Performance is empathy expressed through code.

2. The Cost Spiral Is Real

Inference costs sneak up on you like compound interest.

You think: “It’s just a few cents per request.”
Then you add retries, parallel calls, background jobs — and you’re staring at a $200 bill from one weekend of tests.

How I Survived

• Use credits strategically. AWS Activate + GCP for redundancy and model diversity.
• Build a cost dashboard early. Track spend per feature, not per month.
• Offload when possible. Whisper → local or serverless GPU when feasible.
• Token discipline. Truncate, compress, summarize — every token counts.

I learned to treat prompt size as a system resource, not a creative indulgence.

3. Reliability Is a Cultural Problem

Tech issues are solvable. Reliability issues come from chaos culture.

When you’re solo or small-team, discipline matters more than tooling.
You need clear rituals for recovery, rollback, and rest.

Practical Patterns

• Always have a “safe fallback” — even if it’s a dumb static response.
• Log everything: model name, latency, input size, output tokens, errors.
• Create test prompts for regression. Models update silently; your system shouldn’t break silently.
• Use queues (e.g., Pub/Sub, SQS) to protect against spikes.

Reliability is built in the boring moments — not during outages.

4. GCP vs. AWS: The Tradeoffs

After months on both, here’s my no-fluff comparison:

Verdict: GCP for prototypes and fast iteration. AWS for scale, if you can afford the overhead.

5. Sanity: The Hidden Constraint

AI infrastructure can feel like wrestling fog.
Logs don’t match, GPUs go idle, APIs timeout, and you start to question your life choices.

Sanity isn’t about controlling everything — it’s about controlling enough.

What helped me most:

• Automate deploys. If pushing code feels heavy, you’ll delay improvements.
• Use staging pipelines. One bad model update can nuke your confidence.
• Timebox experiments. Don’t spend a week on marginal gains.
• Monitor yourself. You’re part of the system too.

No stack is worth burnout.

6. What I’d Do Differently

If I were starting again:

1. Pick one cloud. Avoid multi-cloud until you absolutely need it.
2. Set latency targets early (e.g., <1.5s P95). Build around them.
3. Budget at 10× your estimate — because you will underestimate.
4. Prioritize explainability logs (inputs + outputs). They save lives.
5. Automate cost alerts the same way you’d monitor uptime.

Closing Thoughts

AI inference isn’t a technical challenge — it’s a design challenge.
You’re designing a system that users can trust, afford, and enjoy waiting for.

Every optimization is a small act of empathy: faster load, clearer feedback, fewer surprises.

I used to think scaling was about infrastructure.
Now I think it’s about rhythm — building systems that respond as naturally as a conversation.

Further Reading

• Google Cloud — Designing for Latency
• AWS Well-Architected Framework (AI/ML Lens)
• Best Practices for Cost Optimization in AI Inference

Music for Focus

🎧 “Division” by Lane 8 — calm precision, one loop at a time.

This post is part of my “AI in Motion” series — field notes from scaling small AI systems that stay fast, affordable, and human.