#FinOps
FinOps brings financial accountability to cloud infrastructure by connecting engineering decisions to their cost impact. These posts cover BigQuery reservation planning, storage cost tiering, multi-cloud egress optimisation, and the dashboards and automation that keep cloud spend visible and actionable.
23 posts tagged with finops. ← All posts
The AI bill isn't only your production inference — it's also the AI coding tools your engineers use all day, the gateways that route your traffic, and the accelerator and data-platform costs underneath. This closing post covers the spend beyond core inference and pulls the whole playbook into what generalizes across every provider.
The AI bill isn't only production inference — it's the coding tools your engineers use all day, the gateways that route your traffic, and the accelerator and data-platform costs underneath. This closes the playbook with what generalizes across every provider.
Every lever in this playbook shares one precondition: you have to measure. The vendor percentages are directional signposts, not promises — the only number that governs your bill is the one from your own workload. Measurement, and the right unit of measurement, is what turns a list of tactics into an actual reduction.
Every lever shares one precondition: you have to measure. The vendor percentages are directional signposts, not promises — the only number that governs your bill is the one from your own workload, measured as cost per unit of value.
The last inference lever is the classic cloud play in AI clothing: commit to capacity for the steady, predictable part of your load and pay less per unit. And underneath all the AI-specific tactics sits the ordinary cloud bill, where most of a mature estate's money actually lives — governed by the same rate, usage, and waste principles for decades.
The last inference lever is the classic cloud play in AI clothing: commit to capacity for the steady part of your load and pay less per unit. And underneath all the AI-specific tactics sits the ordinary cloud bill, where most of a mature estate's money lives.
This is the classic fixed-versus-marginal decision, and it has a clean answer: managed APIs win until your volume is high and steady enough to keep expensive GPUs busy — which is a much higher bar than most teams assume.
The classic fixed-vs-marginal decision with a clean answer: managed APIs win until your volume is high and steady enough to keep expensive GPUs busy — a much higher bar than most teams assume.
A system can be reliable, safe, and responsible and still fail — by being uneconomical — and the final move is turning one production system into a repeatable capability many teams can build safely.
A system can be reliable, safe, and responsible and still fail by being uneconomical. Phases 10-11: known unit economics and spend guardrails, then a paved road that lets many teams ship governed AI safely.
Two more levers of control: how hard the model thinks, and how you bound what it spends. Both have measured sweet spots — and both have a trap that quietly wastes money. The sharpest is a cap that most people reach for first and that saves nothing at all: max_tokens.
Two levers of control: how hard the model thinks, and how you bound what it spends. Both have measured sweet spots — and both have a trap that quietly wastes money. The sharpest is a cap most people reach for first that saves nothing: max_tokens.
You cannot manage what you cannot see, and the difference between a team that controls its AI spend and one that is surprised by it is almost always whether they measure cost per feature, per user, and per call.
You cannot manage what you cannot see. The difference between a team that controls its AI spend and one surprised by it is whether they measure cost per feature, per user, and per call.
Model selection is the most commonly botched cost decision, because the intuitive answer — pick the cheaper model — is measured by the wrong number. The right number is cost per completed task, and by that measure the more capable model often wins. Paired with it is the least-known lever of all: auditing prompts written for an older model against your current one.
Model selection is the most commonly botched cost decision, because the intuitive answer — pick the cheaper model — is measured by the wrong number. The right number is cost per completed task, priced on the tail not the median.
Some of the biggest cost decisions are architectural — whether to feed knowledge through retrieval or bake it into a model, whether to prompt a big model or fine-tune a small one, and whether to rent tokens or run your own — and each trade turns on your volume and your task.
Some of the biggest cost decisions are architectural — retrieval versus long context, prompting a big model versus fine-tuning a small one, and renting tokens versus self-hosting — each turning on your volume.
The second-largest cost lever is almost embarrassingly simple: if no one is waiting on a response, run it in a batch for half off — and the discount is identical across all three major clouds, which makes it a safe architectural assumption. Then comes token hygiene, where the wins are real but one popular technique quietly costs more than it saves.
The second-largest cost lever is almost embarrassingly simple: if no one is waiting on a response, run it in a batch for half off — identical across all three major clouds. Then comes token hygiene, where one popular technique quietly costs more than it saves.
Not all AI work needs an answer this second, and for the work that can wait, batching and asynchronous processing buy meaningfully cheaper tokens in exchange for latency you were not using anyway.
Not all AI work needs an answer this second, and for the work that can wait, batching and asynchronous processing buy meaningfully cheaper tokens in exchange for latency you were not using anyway.
Of every AI cost lever, one dominates the rest, and all the major vendors agree on it: prompt caching. On long prompts and agentic workloads the reported savings are the largest anywhere — because caching attacks the specific way agent costs explode. If you optimize one thing, optimize this.
Of every AI cost lever, one dominates and all the major vendors agree on it: prompt caching. On agentic workloads the reported savings are the largest anywhere — because caching attacks the specific way agent costs explode. If you optimize one thing, optimize this.
A large share of what an AI system processes is repeated — the same system prompt, the same documents, the same questions — and caching is how you stop paying full price for work you have already done.
A large share of what an AI system processes is repeated — the same system prompt, the same documents, the same questions. Caching is how you stop paying full price for work you have already done.
Before any tactic, know the map. Four cloud and industry frameworks now govern AI cost — AWS, Azure, Google, and the FinOps Foundation — and they converge more than they differ. Every specific lever in this playbook sits underneath one of them, so starting with the constitutional documents is what turns a bag of cost tricks into a coherent discipline.
Before any tactic, know the map. Four cloud and industry frameworks now govern AI cost — AWS, Azure, Google, and the FinOps Foundation — and they converge more than they differ. Every specific lever sits underneath one of them.
Since you pay for every input token, the fastest way to cut the cost of a call without changing the model is to send fewer tokens — and most production prompts are carrying far more than they need.
Since you pay for every input token, the fastest way to cut the cost of a call without changing the model is to send fewer tokens — and most production prompts carry far more than they need.
The single biggest cost lever in most AI systems is not clever prompting — it is not using an expensive model for work a cheap one would do just as well.
The single biggest cost lever in most AI systems is not clever prompting — it is not using an expensive model for work a cheap one would do just as well. Right-sizing and routing models to tasks.
You cannot optimize what you cannot price, and pricing an AI system starts with understanding the token — what counts as one, why input and output cost differently, and how to compute the true cost of a request.
You cannot optimize what you cannot price. Pricing an AI system starts with the token — what counts as one, why input and output cost differently, and how to compute the true cost of a request.
An LLM demo costs almost nothing, so teams ship without a cost model — and then production traffic turns a rounding error into the biggest line item on the bill.
An LLM demo costs almost nothing, so teams ship without a cost model — then production traffic turns a rounding error into the biggest line item on the bill. Why AI costs explode, and how to see it coming.
What looked like an idiomatic BigQuery MERGE was scanning the full target table on every batch. The fix was syntactic, not architectural — and it was the single biggest contributor to a 57% data-warehouse cost reduction across the Tata Group engagement.
Architecture decisions that delivered multi-million dollar savings at petabyte scale.
Architecture decisions that delivered 57% cost reduction on Tata Group's BigQuery data warehouse. The MERGE anti-pattern fix, partition strategy, capacity model, and the trust gap I'd handle differently next time.
We built a small Go + Python service that parses a project's INFORMATION_SCHEMA, asks Gemini to classify each top-spending query against a catalog of anti-patterns, and recommends a rewrite. It is not a magic box; it is a pipeline that cuts the human review time per query from 20 minutes to 90 seconds.
Capacity-based slot reservation is the biggest single FinOps lever for predictable batch workloads, but the transition is harder than the math. Notes from sizing reservations across enterprise GCP customers.
Storage was the second-biggest line on a large-enterprise BigQuery bill. Physical-vs-logical billing and column-level retention delivered significant savings.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.