InfoQ Homepage Cost Optimization Content on InfoQ
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The Hard-Stop Rule: from 3 HCM Monoliths to 120 Domain Microservices
A payroll and HR software team rebuilt three monoliths into over 120 smaller services over five years, with no dedicated migration budget. Every new feature was built as its own service instead of changing the old ones. The article covers the pull-based migration, the tools that made this possible, how costs were kept down, and the problems the team ran into along the way.
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Local-First AI Inference: a Cloud Architecture Pattern for Cost-Effective Document Processing
The Local-First AI Inference pattern routes 70–80% of documents to deterministic local extraction at zero API cost, reserving Azure OpenAI calls for edge cases and flagging low-confidence results for human review. Deployed on 4,700 engineering drawing PDFs, it cut API costs by 75% and processing time by 55%, while bounding errors through a human review tier.
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Sandbox as a Service: Building an Automated AWS Sandbox Framework
This article outlines an automated AWS Sandbox Framework to provide secure, cost-controlled environments for innovation. It leverages AWS services like Control Tower and open-source tools to automate provisioning, enforce security policies, manage resource lifecycles, and optimize costs through automated cleanup and governance.
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Backend FinOps: Engineering Cost-Efficient Microservices in the Cloud
Backend FinOps integrates financial discipline into microservices, crucial for cutting cloud costs. Challenges such as resource fragmentation and cold starts underscore the need for intelligent design, effective language choice, robust tagging, and automation. Implementing FinOps via IaC, CI/CD checks, and dynamic autoscaling (e.g., Karpenter) ensures sustained efficiency.
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Engineering Principles for Building a Successful Cloud-Prem Solution
Discover how Cloud-Prem solutions combine cloud efficiency with on-premise control, meeting data sovereignty and compliance demands while optimizing operational costs and enhancing customer security.
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Cloud Waste Management: How to Optimize Your Cloud Resources
The 2024 "State of FinOps" survey results of the FinOps Foundation mentioned that organizations' top priorities have shifted to reducing cloud waste or unused resources. This article delves into understanding how to manage cloud waste.
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Scaling Challenges: Productivity, Cost Efficiency, and Microservice Management
The main objective of this article is to delve into the technical complexities and strategic adjustments undertaken by Trainline. By examining challenges such as managing peak transaction volumes and orchestrating microservice architectures, we aim to uncover the valuable lessons learned and insights gained from Trainline's journey through the dynamic landscape of digital transportation platforms.
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Platform as a Runtime - the Next Step in Platform Engineering
As systems become larger and more complex we need to take the concepts of platform engineering to a higher level – to the code level – by creating platforms and abstractions that will reduce cognitive load, help simplify and accelerate software development, and allow for easy maintenance and upgrades to the platform. Let’s move from “platform” to “Platform as a Runtime”.
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Million Dollar Lines of Code - an Engineering Perspective on Cloud Cost Optimization
A single line of code can shape an organization's financial future. Erik Peterson, the CTO and founder at CloudZero, presented an engineering perspective on cloud cost optimization at QCon San Francisco.
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Multi-Cloud Observability Using Fluent Bit
Explore the benefits and challenges of observability in multi-cloud deployments. See how Fluent Bit, a lightweight log collection and distribution tool, can enhance multi-cloud observability by improving cloud neutrality, cutting egress costs, and tackling compliance challenges.
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Optimizing Resource Utilization: the Benefits and Challenges of Bin Packing in Kubernetes
Optimizing Kubernetes usage is an important part of a responsible cloud strategy. Bin packing is an effective strategy for maximizing the usage of each node.
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Debugging Go Code: Using pprof and trace to Diagnose and Fix Performance Issues
In this article, we will look at how to identify and fix performance issues in Go programs using the pprof and trace packages. We will begin by covering the fundamentals of the tools, then delving into practical examples of how to use them. By the end of this article, you will have a solid understanding of how to use these powerful tools to improve the performance of your Go applications.