Latency, Throughput, and Cost Trade-offs
Pick the pattern that fits your SLAs and budget.
Latency, Throughput, and Cost Trade-offs is a free MLOps Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Three Dials to Balance
Every serving choice juggles three things: latency, throughput, and cost. Push hard on one and you usually move the other two.
Latency Defined
Latency is the time for a single prediction to come back. Low latency feels snappy; high latency makes users and downstream systems wait.
Throughput Defined
Throughput is how many predictions you serve per second. A service can be fast per call yet still need high throughput under heavy load.
They Pull Apart
Grouping requests into a batch raises throughput but adds wait time, so each call sees higher latency. The two goals often fight.
Cost Joins the Fight
More machines cut latency and lift throughput, but the bill climbs. Cost is the third corner you cannot ignore when sizing a service.
Anchor to an SLA
An SLA sets your target, like 95% of requests under 100 ms. It turns vague goals into a number you design and measure against.
Batching Buys Throughput
Serving many inputs in one model call uses hardware better. This batching lifts throughput, ideal when a little extra latency is fine.
preds = model.predict(np.stack(batch))Scaling Out for Load
Add more replicas to share traffic. Horizontal scaling raises throughput and protects latency, at the price of more compute spend.
Watch the Tail
Averages hide pain. The slow p99 request is what users complain about, so you tune for the tail, not just the typical case.
Hardware Changes the Math
A GPU can crush throughput on big models but sits idle on light traffic. Match the hardware to your real load to avoid wasted cost.
Pick for Your Use Case
There is no universal best. You weigh latency, throughput, and cost against what your users truly need, then choose deliberately.
Quick Check
You enable request batching. What usually happens?
Recap
Latency, throughput, and cost form a triangle you cannot max all at once. Set an SLA, then use batching and scaling to hit the balance you need.
Frequently asked questions
Is the “Latency, Throughput, and Cost Trade-offs” lesson free?
Yes — the full text of “Latency, Throughput, and Cost Trade-offs” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Latency, Throughput, and Cost Trade-offs”?
Pick the pattern that fits your SLAs and budget. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Latency, Throughput, and Cost Trade-offs” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Batch Scoring on a Schedule
- Real-Time Online Inference
- Latency, Throughput, and Cost Trade-offs
- Precompute and Cache Predictions