#AI Cost

Articles about AI Cost — exploring patterns, best practices, and real-world implementations in production systems.

10 posts tagged with ai cost. ← All posts

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Pratik Dhanave · ·7 min read

AI Developer Tooling and the Provider Landscape

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.

#AI Cost#FinOps#AI Engineering#MLOps
Pratik Dhanave · ·7 min read

Measurement and Unit Economics

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.

#AI Cost#FinOps#Observability#AI Engineering#Reliability
Pratik Dhanave · ·6 min read

Latency, Throughput, and Cost

There is no single "make it fast" for LLM serving — latency and throughput pull against each other, and both trade against cost. The job isn't to maximize one number; it's to hit your latency targets at the lowest cost per token, which means knowing exactly which knob moves which metric and in which direction.

There is no single 'make it fast' for LLM serving — latency and throughput pull against each other, and both trade against cost. The job is to hit your latency targets at the lowest cost per token, knowing which knob moves which metric.

#LLM Inference#AI Engineering#AI Cost#MLOps#Performance#LLMs
Pratik Dhanave · ·6 min read

Capacity Commitments and Cloud Fundamentals

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.

#AI Cost#FinOps#Cloud#AI Engineering
Pratik Dhanave · ·7 min read

Effort Tuning, Budgets, and the max_tokens Trap

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.

#AI Cost#AI Engineering#LLMs#AI Agents#FinOps
Pratik Dhanave · ·7 min read

Model Selection and Prompt Audits

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.

#AI Cost#AI Engineering#LLMs#AI Architecture#FinOps#Prompt Engineering
Pratik Dhanave · ·6 min read

Batching and Token Hygiene

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.

#AI Cost#AI Engineering#LLMs#Context Engineering#FinOps
Pratik Dhanave · ·6 min read

Quantization

Quantization shrinks a model by storing its numbers in fewer bits — and because LLM decode is bottlenecked on moving those numbers from memory, making them smaller makes inference both cheaper to host and faster to run. It's the rare optimization that improves memory, cost, and speed at once, if you respect its limits on quality.

Quantization stores a model's numbers in fewer bits — and because decode is bottlenecked on moving those numbers from memory, making them smaller makes inference cheaper to host and faster to run, if you respect the limits on quality.

#LLM Inference#AI Engineering#Performance#AI Cost#LLMs
Pratik Dhanave · ·7 min read

Prompt Caching: The Single Largest Lever

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.

#AI Cost#AI Engineering#LLMs#Performance#FinOps#Prompt Engineering
Pratik Dhanave · ·6 min read

The Four Governing Frameworks

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.

#AI Cost#FinOps#AI Engineering#Cloud

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.