Price-Performant Compute

Imagine you run a business and need different tools for different tasks. You wouldn't take a heavy-duty truck to the grocery store, or a sports car to haul construction materials. But when companies move data to the cloud, they often pick one main tool to store and process everything: one big warehouse, one big lake. That warehouse is expensive to use for every task, especially the small or low-value ones.

Most organizations overspend in the cloud because they default to a single, high-cost compute layer for all workloads, even though most tasks do not require it.

— Gartner, 2023

The problem is straightforward: most tasks are small and don't need that much power, which makes the one-warehouse approach costly and inefficient.

Up to 70% of cloud spend can be eliminated simply by aligning workload types to appropriate compute resources.

— McKinsey, 2023

Different types of data deserve different homes. AI workloads should sit on repositories built for AI. Business reports belong on systems tuned for analytics. Operational data belongs on transactional databases.

Fit-for-purpose data architectures: AI workloads perform best on specialized repositories, while BI tasks are far more cost-effective on analytic-optimized systems.

— Deloitte Insights, 2023

To make this work, you need a data control plane — a smart dispatcher that knows where every dataset lives and routes each task to the right place, just like sending a sports car to the grocery store and a truck to the building site.

The emerging unified control plane simplifies multi-repository complexity while improving operational performance.

— Forrester, 2024

I call this Price-Performant Compute (PPC). Back to the vehicle analogy: PPC is making sure you're using the right vehicle for the right job — to get the best value for your money.

In data terms, PPC means using the most cost-effective and efficient compute resource for each specific task. Simple tasks run on cheaper systems; complex tasks that genuinely need power use the more expensive ones. The company spends money wisely and gets the best performance available — without overspending on compute that isn't needed.

Now imagine running a business with several storage locations — a warehouse for large equipment, a unit for office supplies, a refrigerated unit for perishables. Each is ideal for its contents, but coordinating across all of them gets complicated fast: tracking what's where, keeping each one healthy, moving items between them.

The same is true of data. Storing different data types in different repositories makes sense, but managing them adds real overhead — knowing where everything lives, ensuring each one runs correctly, orchestrating processing across them.

Multi-repository ecosystems often introduce silos and operational overhead unless unified by a governance or control plane.

— IDC, 2024

At Data Tiles we built Latttice as a data mesh that operates as a control plane — central oversight without central storage.

Unified access point

A single, easy-to-use entry point to every repository — one platform to access and control all your data, regardless of where it lives.

Smart task routing

Build data products across multiple repositories with AI-powered, zero-code functionality. Each task lands on the repository best suited for it — AI on AI-optimized compute, BI on analytics-optimized compute — efficient and cost-effective by default.

Zero-code and AI-enabled data access is becoming a strategic requirement for business teams.

— Forrester, 2024

Simplified management

Latttice handles the coordination across repositories — no manual orchestration, no constant data movement. Everything works together with far less effort on your part.

Centralized control. Latttice is a control tower for your data — a comprehensive view and control of every repository from one place.

Optimized performance. By directing each task to the right repository, compute is used efficiently — the best performance without overspending on power that isn't needed.

Workload-aware routing is a core strategy for reducing cloud waste.

— Gartner

Ease of use. AI-powered, zero-code, and built so anyone can access and use data effectively without worrying about the underlying complexity.

A retail business needs to analyze sales data to make decisions. With Latttice, the team simply opens the platform — sales data is fetched from the right repository. When a complex AI model is needed to predict future sales trends, Latttice dispatches that task to a powerful AI-optimized repository. All of it happens seamlessly — no one needs to know where the data is stored or how to process it.

Separating repositories for reporting versus AI is one of the most effective ways to reduce cost and improve model performance.

— IDC, 2024

Price-Performant Compute addresses the inefficiency and high costs that come from running every task on expensive engines. By storing different data types in repositories tailored to specific workloads — AI, BI, operational — companies optimize spend without sacrificing performance.

Latttice simplifies the whole picture: a centralized control plane that unifies access to every repository and dispatches each task to the right system. Resources are used efficiently, costs come down, and management gets out of the way — so teams can focus on using data for better decisions, not maintaining capital-intensive infrastructure.

Use the right vehicle for the right job. Pay for the trip you actually need.

1. Gartner. How to Optimize Cloud Costs Without Compromising Performance. 2023.
2. McKinsey & Company. Cloud's Trillion-Dollar Prize Is Up for Grabs. 2023.
3. Forrester. The State of Data Strategy. 2024.
4. Deloitte Insights. AI and Data Infrastructure: Aligning the Right Tools with the Right Tasks. 2023.
5. IDC. Data Mesh and the Modern Enterprise. 2024.