Training gets the headlines. A company announces a new model, quotes the eye-watering GPU bill it took to build, and everyone treats that number as the cost of doing AI. It isn’t. That number is a one-time spike. The real cost arrives afterwards, quietly, every time the model answers a question in production — and it never stops. At L&T Vyoma, we design AI infrastructure with the complete cost journey in mind - because while training is a major upfront investment, continuous inference at scale can become a significant and recurring component of an enterprise’s AI budget.
The Numbers Behind the Claim
This is not a hunch; it is where the industry has landed. Analyses of production AI systems consistently put inference at 80 to 90% of a model’s total lifetime compute cost (industry analyses). Gartner expects inference to absorb the majority of AI-optimised cloud infrastructure spending — around 55% in 2026, rising toward 65% by 2029 (Gartner). And the market reflects it: one industry forecast values AI inference at USD 106 billion in 2025, climbing to USD 255 billion by 2030 (industry forecast). Training built the model once. Inference pays to run it, for every user, every day, for years.
The Trap: Cheaper Per Token, Bigger Bill
Here is the part that catches finance teams off guard. The cost of a single inference has collapsed. Stanford’s AI Index reports that the price of running a GPT-3.5-level query fell more than 280-fold in roughly eighteen months, from about USD 20 per million tokens to USD 0.07 (Stanford HAI). So why are the bills going up? Because cheaper inference invites far more of it. When each answer costs a fraction of a cent, product teams put AI into everything — search, support, every screen — and volume explodes faster than unit cost falls. Lower cost per token, far more tokens, a bigger bill overall. Anyone budgeting AI on the training number alone is reading the wrong line of the invoice.
Why Inference Is a Different Infrastructure Problem
Training and inference are not the same workload wearing different hats. Training is a sprint: thousands of GPUs, flat out, for a fixed run, then done. Inference is a utility: a steady, around-the-clock hum whose economics live and die on one number — how many tokens a GPU can serve per second, per rupee. That difference changes what good hardware looks like. Training wants raw compute and interconnect. Inference wants memory to hold the model and the context, and it wants to be sliced, so a single card can serve several models or several tenants at once instead of sitting mostly idle. Buy for the training headline and you overpay for inference; size for inference and the whole economic picture shifts.
How We Size for the Work That Actually Runs
This is the reasoning behind our AI Factory. We do not hand every workload the same GPU. For the largest models and long-context serving, the Blackwell Ultra B300 carries 288 GB of HBM3e, so a big model and its context stay on a single card instead of being split across many (NVIDIA). For high-throughput serving, Hopper H200s run the mature inference stack on 141 GB of HBM3e. And the RTX PRO 6000 Blackwell Server Edition uses MIG to split one card into up to four isolated instances — the single most direct lever on inference economics, because it turns a GPU that a small model barely touches into four GPUs’ worth of useful work. Utilisation climbs, cost per token falls, and nobody queues. Our Cloud Calculator prices a serving cluster before you commit, so the inference bill is a decision, not a surprise.
The sovereignty point matters more for inference than for training, too. A training run touches your data once; an inference endpoint touches it every day, in production, in plain sight. For a bank, a hospital or a government service, that steady stream of prompts and outputs is exactly the data that must stay in India. Because our compute runs on L&T-operated data centers on Indian soil, it does — which is why AI infrastructure services built for inference have to be sovereign by design. Speed alone is not enough.
Training vs Inference, Side by Side
|
Dimension |
Training |
Inference (production) |
|
When it runs |
Once, or occasionally |
Every request, around the clock |
|
Cost shape |
Big, one-off spike |
Relentless, compounding with usage |
|
What decides the bill |
Time to convergence |
Tokens served per second per rupee |
|
Right-sized GPU |
Frontier training silicon |
Memory-rich, MIG-partitioned inference GPUs |
|
Scaling pattern |
Burst, then release |
Steady base plus demand peaks |
|
Where the waste hides |
Idle clusters between runs |
Whole GPUs serving a fraction of a model |
Budget for the Mortgage, Not the Down Payment
The model you train is a one-time event. The model you serve is a running cost that grows with every user you win — and that is the good problem, the sign your AI is working. The mistake is planning for the spike and being ambushed by the stream. Size the infrastructure for inference, keep utilisation high, and keep the data at home. Explore the AI Factory, price a serving cluster on the Cloud Calculator, or talk to our team about what production will actually cost.