# cost-savings Published articles for cost-savings. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost DevFeed: [Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost]() Original publisher: [Read original article]() Author: Anubhav Sharma Published: 2026-08-12T20:52:35Z Content type: article Language: en Sources: [Nirmata]() Topics: [Kubernetes](), [Cloud](), [Automation](), [data]() Tags: [agents](), [ai](), [automation](), [cloud](), [cloud-native](), [clusters](), [cncf](), [compute](), [cost-savings](), [deployment](), [devsecops](), [kubernetes](), [kyverno](), [policy-management](), [resource](), [verification]() ### AI overview Nirmata describes applying its Cost Analyzer and Resource Hygiene Cloud Agents across an enterprise customer's 40-cluster production Kubernetes fleet. The scans identified a roughly $107,000 monthly compute baseline and about 50% recoverable through right-sizing before stale-resource cleanup, while revealing recurring sources of waste and governance gaps. ### Source excerpt Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost Most Kubernetes Cost overruns don't come from one singularly bad decision. They come from dozens of reasonable ones -- made independently, by different teams, at different times -- that... The post Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost first appeared on Nirmata. ## Time is Money: How Pre-Validated UX Research Protects Your Resources & Budget DevFeed: [Time is Money: How Pre-Validated UX Research Protects Your Resources & Budget]() Original publisher: [Read original article]() Author: Christian Holst Published: 2026-06-30T11:45:00Z Content type: article Language: en Sources: [Baymard Institute]() Topics: [User Experience](), [Usability](), [Testing](), [Website]() Tags: [cost-savings](), [subscription](), [usability](), [user-research](), [ux]() ### AI overview The article presents a calculator intended to estimate initial savings from using Baymard's pre-validated UX research instead of conducting new in-house research. It states that a moderated usability study with 15 participants requires about 55 hours of labor and estimates the cost of producing one UX insight at $362 in the US and $262 in the UK and Europe. ### Source excerpt (Note: Unfortunately, e-mail and RSS don't support advanced layouts and features. If the graphics in this article look strange, you may want to read the article in your web browser.) When you try to prove the value of your design decisions, you often hit a wall. To back up your designs confidently, you need robust usability research. But conducting that research yourself for every minor UI change or UX decision is expensive and time-consuming. Baymard's pre-validated research gives teams like yours access to established UX best practices in an instant, which helps you ship designs faster, more confidently, and protects your costs by reducing the research overheads. To demonstrate that value, we've created a calculator that shows how a Baymard subscription helps you move faster and keep your UX research costs down. Note: This calculator demonstrates initial cost savings based on consuming Baymard's insights versus running new user research yourself. Baymard users typically see further value realizations from implementing those insights and improving website user experience and conversion rates. The Hidden Cost of Your In-House User Research It's easy to fall into the trap of thinking: "We can just replicate this research in-house for less cost." But when you try to build everything from scratch without access to pre-validated UX insights, your team quickly runs into three major roadblocks: You move slower: Your product teams lose momentum and your project timelines stretch out because you are forced to conduct research from scratch You risk harming conversions: When timelines get tight, you risk making critical UX decisions based on gut feelings, which can ultimately harm your conversion rates You spend more money: To keep up with research demands, you end up spending vastly more on external agency services or expanding your internal headcount just to conduct baseline testing What It Actually Costs You to Produce a UX Insight To protect your resources, you need to sh ## Major compute price reduction on Neon DevFeed: [Major compute price reduction on Neon]() Original publisher: [Read original article]() Author: Nikita Shamgunov Published: 2025-11-03T19:23:07Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [Databases](), [autoscaling](), [databricks](), [cloud-infrastructure](), [Supabase]() Tags: [autoscaling](), [company](), [compute](), [cost](), [cost-savings](), [databases](), [databricks](), [durability](), [infrastructure](), [load](), [serverless](), [snapshots]() ### AI overview Neon announces compute price reductions of up to 25% across its plans, including lower rates for the Launch and Scale Plans. The article also reviews earlier reductions to storage, minimum spend, enterprise features, and Free Plan compute, then compares costs with Aurora Serverless v2 and Supabase in selected usage scenarios. ### Source excerpt Databases are often one of the biggest infrastructure expenses for any company. From day one, Neon's mission has been to make databases radically more efficient through separation of storage and compute, allowing instant autoscaling and better unit economics. Now, with Neon runni... ## How Internet Exchanges (IX) Help Enterprises Save Bandwidth & Costs DevFeed: [How Internet Exchanges (IX) Help Enterprises Save Bandwidth & Costs]() Original publisher: [Read original article]() Author: Shivam Anand Published: 2025-08-30T13:09:53Z Content type: article Language: en Sources: [Shivam Anand - DevOps & Cloud Engineering Blog]() Topics: [Internet](), [networking](), [BGP](), [Routing (disambiguation)](), [Latency](), [Scalability](), [Cloud]() Tags: [bandwidth-savings](), [bgp](), [cloud-connectivity](), [cost-savings](), [data-center-interconnect](), [enterprise-networking](), [firewall](), [internet](), [internet-exchange](), [isp-costs](), [ix](), [ixp](), [latency](), [mpls](), [paloalto-firewall](), [peering](), [remote-peering](), [scalability](), [university-connectivity](), [vlan]() ### AI overview This article explains how enterprises can use Internet Exchanges (IX) and VLAN-based multi-campus links to exchange traffic directly with networks, cloud providers, and content platforms. It describes potential bandwidth and transit-cost savings, lower latency, improved performance, and more flexible scaling, with university and network-architecture examples. ### Source excerpt Discover how connecting your enterprise infrastructure to Internet Exchanges (IX) can reduce bandwidth costs, improve performance, and scale efficiently. Includes cost-saving examples, diagrams, and real-world scenarios. ## Join me if you can: ClickHouse vs. Databricks vs. Snowflake - Part 2 DevFeed: [Join me if you can: ClickHouse vs. Databricks vs. Snowflake - Part 2]() Original publisher: [Read original article]() Author: Al Brown; Tom Schreiber Published: 2025-06-25T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Benchmark](), [SQL](), [databricks](), [datasets](), [Apache Iceberg]() Tags: [benchmark](), [clickhouse](), [cost-savings](), [memory](), [parquet](), [performance](), [s3](), [sql]() ### AI overview This article explains how replacing joins with in-memory dictionaries in ClickHouse Cloud improved query performance by up to 6.6x and reduced costs by over 60% in benchmarks querying 1.4 billion rows. It describes using dictionaries for fast lookups against small dimension tables and reports that the changes required only minor SQL edits, without reloading data or rewriting schemas. ### Source excerpt We took the same JOIN-heavy benchmark from Part 1 and made ClickHouse even faster. By replacing JOINs with in-memory dictionaries, we saw up to 6.6x faster queries and over 60% cost savings, with no data reloading or schema rewrites required. ## Achieving $2.25 million in savings: ROI analysis of migrating to Temporal Cloud DevFeed: [Achieving $2.25 million in savings: ROI analysis of migrating to Temporal Cloud]() Original publisher: [Read original article]() Author: Mason Egger Published: 2024-12-12T10:22:00Z Content type: comparison Language: en Sources: [Temporal Blog]() Topics: [maintenance](), [Scalability](), [reliability](), [Confluent Cloud](), [Cloud]() Tags: [cloud](), [confluent](), [confluent-cloud](), [cost](), [cost-savings](), [how-to](), [maintenance](), [reliability](), [scalability]() ### AI overview This ROI report compares a client's homegrown Inventory Management System with Temporal Cloud. Based on a single client and estimates for a typical large customer, it projects $2.25 million in annual savings across infrastructure, maintenance and incident management, and human capital costs. It also describes potential improvements in system stability, scalability, feature development, and data accuracy. ### Source excerpt Discover how Temporal Cloud saves $2.25M annually by cutting infrastructure, maintenance, and labor costs while boosting scalability and reliability. ## Free the data: Why US federal agencies should standardize on OpenTelemetry DevFeed: [Free the data: Why US federal agencies should standardize on OpenTelemetry]() Original publisher: [Read original article]() Author: Bill Wright Published: 2024-06-26T00:00:00Z Content type: opinion Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [OpenTelemetry](), [telemetry](), [interoperability](), [vendor lock-in](), [Open Source](), [data-processing](), [Monitoring](), [Cloud Native Ecosystem]() Tags: [collection](), [cost-savings](), [government](), [interoperability](), [migrations](), [observability](), [open](), [opentelemetry](), [opentelemetry-data-ingestion-open-source-standards-government](), [public-sector-government](), [standard](), [standards](), [systems](), [telemetry](), [universal-profiling-logs-metrics](), [us](), [vendor-lock-in]() ### AI overview The article argues that US federal agencies should standardize on OpenTelemetry, an open-source observability framework and CNCF project. It presents unified collection of traces, metrics, and logs as a way to reduce vendor lock-in and costs, improve interoperability and collaboration, and provide adaptable monitoring across government applications and infrastructure. ### Source excerpt Adopting OpenTelemetry standards can enhance the government's ability to manage and analyze telemetry data across diverse systems - ensuring consistent and reliable data collection and leading to better decision-making and improved service delivery. ## Chuck, Acme, and Remediation Avoidance DevFeed: [Chuck, Acme, and Remediation Avoidance]() Original publisher: [Read original article]() Author: Adam Published: 2023-07-27T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Security](), [Automation]() Tags: [automation](), [cost-savings](), [integration](), [reporting](), [security]() ### AI overview The article discusses how automated threat modeling may reduce remediation costs and improve efficiency, using findings from a Forrester report and a hypothetical company, Acme, to illustrate the challenges of discovering security issues late through penetration testing. ### Source excerpt Threat modeling really CAN save you money, just ask Chuck! ## Tutorial: Low Usage Alerting On Slack for Google Cloud Platform (GCP) DevFeed: [Tutorial: Low Usage Alerting On Slack for Google Cloud Platform (GCP)]() Original publisher: [Read original article]() Author: Mauricio Martinez Published: 2023-04-24T15:57:09Z Content type: tutorial Language: en Sources: [Engineering at Premise - Medium]() Topics: [Google Cloud Platform (GCP)](), [Cloud Run](), [Python](), [Monitoring](), [Slack]() Tags: [cloud-computing](), [cloud-run](), [cloudrun](), [cost-savings](), [deploy](), [gcp](), [github](), [google-cloud](), [metrics](), [monitoring](), [python](), [slack]() ### AI overview This tutorial explains how to deploy a Python-based Cloud Function that monitors weekly CPU and memory usage for Google Cloud services, including Cloud Run, and sends Slack notifications to service owners when a service may be safely downscaled to reduce costs. It covers creating MQL queries, retrieving monitoring metrics, checking minimum thresholds, and deploying the scheduled function. ### Source excerpt Google Cloud Platform (GCP) is a powerful cloud computing platform that allows businesses to run their applications and workloads with ease. However, as the number of services and applications increases, it becomes challenging to keep track of the usage of each service and ensure they are cost optimized. To address this issue, we can deploy a Python-based Cloud Function that will monitor the low usage of GCP services and notify service owners weekly via Slack that their service can be safely downscaled to minimize costs. This tutorial will walk you through setting up this entire flow in a few quick and easy steps, while allowing you to easily customize the service thresholds, Slack message ui, and the cron schedule. The code for this tutorial can be found on our gcp-tutorials GitHub repository. ArchitectureGet CPU/Memory Usage Metrics We can use the Metrics Explorer UI to build a MQL query to retrieve the desired data from the monitoring metrics API. In this tutorial we will monitor Cloud Run services, but you can monitor other resources by using a different MQL query. In our example we selected the CPU and Memory Cloud Run metrics: We group by service name and location and select the alignment for the 99th percentage to get the max usage over the 1 week duration. Then click on CODE EDITOR to generate a sample MQL query: fetch cloud_run_revision | metric 'run.googleapis.com/container/cpu/utilizations' | group_by 1w, [value_utilizations_percentile: percentile(value.utilizations, 99)] | every 1w | group_by [resource.service_name, resource.location], [value_utilizations_percentile_max: max(value_utilizations_percentile)]fetch cloud_run_revision | metric 'run.googleapis.com/container/memory/utilizations' | group_by 1w, [value_utilizations_percentile: percentile(value.utilizations, 99)] | every 1w | group_by [resource.service_name, resource.location], [value_utilizations_percentile_max: max(value_utilizations_percentile)]Code Once we have our MQL queries we will use the ## Cinco de Trino recap: Learn how to build an efficient data lake DevFeed: [Cinco de Trino recap: Learn how to build an efficient data lake]() Original publisher: [Read original article]() Author: Brian Olsen, Brian Zhan Published: 2022-05-17T00:00:00Z Content type: article Language: en Sources: [Trino Blog]() Topics: [data](), [data-engineering](), [big-data](), [Query (disambiguation)](), [datasets](), [Apache Iceberg](), [Stack Overflow](), [X (Twitter)]() Tags: [analytics](), [architecture](), [big-data](), [build](), [clusters](), [community](), [conference](), [cost-savings](), [data](), [data-engineering](), [data-lake](), [etl](), [learn](), [pipelines](), [recap](), [recovery](), [scale](), [tools]() ### AI overview A recap of the Cinco de Trino conference covering Trino's role in data lakehouse architectures, interactive and federated querying, adaptive query planning, and fault-tolerant execution. It highlights Project Tardigrade for autoscaling, spot-instance use, cost savings, and failure recovery, plus a Starburst Galaxy lab for ingesting, cleaning, and analyzing Twitter and Stack Overflow data. ### Source excerpt When Trino (formerly PrestoSQL) arrived on the scene almost 10 years ago, it immediately became known as the much faster alternative to the data warehouse of big data, Apache Hive. The use cases that you, as the community, have built had far exceeded anything we had imagined in complexity. Together we've made Trino not only the fastest way to interactively query large data sets, but also a convenient way to run federated queries across data sources to make moving all the data optional. At Cinco de Trino, we came full circle back to the next iteration of analytics architecture with the data lake. This conference offers advice from industry thought leaders about how to use best lakehouse tools with Trino to manage that data complexity. Hear from industry thought leaders like Martin Traverso (Trino), Dain Sundstrom (Trino), James Campbell (Great Expectations), Jeremy Cohen (DBT Labs), Ryan Blue (Iceberg), Denny Lee (Delta Lake), Vinoth Chandar (Hudi). You can watch the talks on-demand on the Cinco de Trino playlist. In this post, I'd like to cover the key items from each talk you won't want to miss. ## Firestore Adds Data Bundles for Faster or Lower-Cost Mobile and Web Applications DevFeed: [Firestore Adds Data Bundles for Faster or Lower-Cost Mobile and Web Applications]() Original publisher: [Read original article]() Author: Todd Kerpelman Published: 2021-04-15T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firestore](), [Firebase](), [web applications]() Tags: [app](), [applications](), [cache](), [cost-savings](), [database](), [firebase](), [firestore](), [launch](), [mobile](), [web-applications]() ### AI overview Firebase announces Firestore data bundles, which let developers package retrieved documents, store the bundles on a CDN or object storage service, and load them into mobile and web applications. Reading common queries from cached bundles can reduce Firestore database calls and may improve speed, especially for applications with many users. ### Source excerpt News, tutorials, and updates from the Firebase team.