Platform migration avoided for AT&T by rationalizing 4,380 Tableau workbooks
Typical cloud, warehouse, and AI compute spend recovered in an optimization sprint
We find where the money leaks: idle warehouses, oversized capacity, runaway auto-suspend settings, duplicate pipelines, and queries that scan everything. You get a ranked list of fixes with dollar values attached.
We instrument and control AI cost: token usage, model selection, caching, and batch versus real-time tradeoffs, so your agentic and GenAI workloads scale without the bill scaling with them.
We retire the duplicate dashboards, overlapping tools, and unused licenses that quietly compound. The AT&T engagement retired 4,380 workbooks and avoided a multi-million-dollar migration.
We set up the ongoing practice: cost allocation by team, budgets and alerts, showback or chargeback, and a review cadence so spend stays controlled after we leave.
Spend climbs every quarter and nobody can say which workload, team, or query is driving it, because cost is never allocated back to the thing that caused it.
The GenAI pilot looked cheap in the demo. In production, token and compute cost scales with every user and nobody set a ceiling, so finance gets surprised.
Three BI tools, overlapping warehouses, and seats nobody uses. Each renewal is a rubber stamp because no one has mapped what is actually used.
Cloud and AI cost optimization, often called FinOps, is the practice of bringing financial accountability to variable cloud, data-warehouse, and AI spend. It combines a technical audit (finding waste in compute, storage, pipelines, queries, and AI token usage) with an operating model (cost allocation, budgets, alerts, and a review cadence) so spend maps to value and stops surprising finance.
In a typical optimization sprint we recover 30 to 45 percent of cloud, warehouse, and AI compute spend, with the bulk coming from idle or oversized capacity, inefficient queries, duplicate pipelines, and unused licenses. The exact figure depends on how much governance already exists. Environments that have never been optimized usually see the largest first-pass savings.
Turning things off is a one-time cleanup that drifts back within a quarter. FinOps is the operating model that keeps spend controlled: cost allocated to the team or workload that caused it, budgets and alerts in place, and a regular review so new waste gets caught early. The audit finds the savings; the operating model keeps them.
Yes. AI spend is now one of the fastest-growing and least-governed line items. We instrument token and compute usage, right-size model selection, add caching and batching where real-time is not required, and set ceilings so agentic and GenAI workloads scale in capability without the cost scaling one-to-one with usage.
We work across the major cloud and data platforms our clients run, including Snowflake, Databricks, BigQuery, Microsoft Fabric, AWS, Azure, and the BI tools on top (Tableau, Power BI). We are platform-agnostic: the goal is the lowest defensible cost for the workload, not a migration to whatever we resell.
A focused cost audit for a single platform or domain runs the equivalent of a 3 to 5 week senior-led engagement and is usually self-funding from the savings it surfaces. A full FinOps operating-model rollout across teams typically lands in the 2 to 4 month range. We price by deliverable, not by hours bucket.
Cloud and AI cost optimization services: warehouse and pipeline cost audits, AI and LLM spend control, BI tool rationalization, and a FinOps operating model. Senior-led, self-funding.
Cloud and AI cost optimization, often called FinOps, is the practice of bringing financial accountability to variable cloud, data-warehouse, and AI spend. It pairs a technical audit that finds waste with an operating model that keeps spend mapped to value.
BI and tooling rationalization: retire duplicate dashboards and unused licenses.
A one-time cleanup. Turning things off drifts back; the operating model is what holds.
A migration pitch. We target the lowest defensible cost, not a move to what we resell.
A finance-only exercise. The savings live in the engineering, so engineering owns the fixes.
A spend dashboard shows you the bill. Lowering it takes architecture and workload changes. Here is the difference.
Architecture and workload decisions that lower the bill, not just visibility.
Forecasting and guardrails for inference, training, and model APIs.
98% of organizations now manage AI-related spend, up from 31% two years ago, so the AI line is where most of the new opportunity sits. These are the factors that move the effort.
The size and spread of your bill across compute, storage, and AI services sets the opportunity and the work.
Inference, training, and GPU or model-API spend are harder to forecast and govern than steady compute.
Multi-account, multi-cloud, and hybrid setups take more to instrument and tag.
Putting controls in before spend happens is more than a one-time cleanup of idle resources.
AI inference and training costs are unpredictable and ungoverned.
You want engineers who implement the fix, not just a spend dashboard.
Your core issue is consolidating overlapping systems: see System Consolidation.
You need the data foundation built first: see Data Foundation.
You want governance of AI usage and policy: see AI Governance & Managed Operations.
Rationalize fragmented systems and overlapping tools that quietly compound cost.
Warehouse design and tuning that lowers compute cost while improving performance.
Catch the broken pipeline that is silently burning compute on bad data.
Cloud and AI cost optimization, or FinOps, brings financial accountability to variable warehouse, pipeline, and AI spend. We audit where the money leaks, recover 30 to 45 percent in a typical sprint, and set up the operating model that keeps it controlled.
Cloud and AI cost optimization, or FinOps, brings financial accountability to variable warehouse, pipeline, and AI spend. Thinklytics pairs a technical audit that finds where the money leaks, recovering 30 to 45 percent in a typical sprint, with an operating model that keeps spend mapped to value after the engagement ends.
Start with a cost audit. We map your cloud, warehouse, AI, and BI spend and hand you a ranked list of fixes with dollar values. It usually pays for itself.