# cloud-migration Published articles for cloud-migration. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Top 15 Kubernetes Consulting Companies in 2026: Choosing the Right Partner for Cloud-Native Success DevFeed: [Top 15 Kubernetes Consulting Companies in 2026: Choosing the Right Partner for Cloud-Native Success]() Original publisher: [Read original article]() Author: Urolime Technologies Published: 2026-08-10T18:15:31Z Content type: comparison Language: en Sources: [Kubernetes Archives - Urolime Blogs]() Topics: [Kubernetes](), [Cloud Native Ecosystem](), [Architecture & Design](), [migration](), [Platform Engineering](), [Security]() Tags: [2026](), [architecture](), [cloud-migration](), [cloud-native](), [consulting](), [infrastructure-as-code](), [kubernetes](), [kubernetes-consulting](), [operational]() ### AI overview A guide to 15 Kubernetes consulting companies in 2026, describing why businesses use consulting services and the capabilities to assess when selecting a partner. It covers Kubernetes architecture, application migration, security, multicloud operations, platform engineering, GitOps, observability, governance, and cost optimization. ### Source excerpt The question isn't whether you should use Kubernetes or not, rather its on how to make use of Kubernetes to add value to your business. From AI-based applications and microservices to multicloud as well as edge computing, Kubernetes is at the center of digital transformation. But implementing and managing Kubernetes solutions requires a set of [...] ## A Practical Loop for Optimizing GPU Inference Throughput DevFeed: [A Practical Loop for Optimizing GPU Inference Throughput]() Original publisher: [Read original article]() Author: Andrei Potapkin Published: 2026-05-01T17:57:59Z Content type: article Language: en Sources: [Bumble Tech]() Topics: [Inference](), [Optimization](), [GPU](), [Benchmark](), [TensorRT](), [onnx](), [Cloud](), [migration]() Tags: [benchmark](), [cloud](), [cloud-migration](), [gpu](), [inference](), [nvidia-nsight-systems](), [onnx](), [optimization](), [python](), [tensorrt]() ### AI overview This practical article describes a production-focused loop for optimizing GPU inference: benchmark under representative load, identify the current throughput bottleneck, fix it, and verify the operational outcome. In the described ML moderation service, TensorRT, BF16, and an attention-subgraph rewrite increased throughput from roughly 150 to roughly 1100 requests per second in the same saturated benchmark. ### Source excerpt A practical loop for optimizing GPU inference without guessing It wasn't an incident. That made it interesting. A few days after a cloud migration, one of our highest-traffic ML services looked healthy. Traffic was flowing. Latency was fine. The rollout had gone the way you want these things to go: boringly. And yet a Slack thread started anyway. Not because anything was broken. Because something had become easier to see. On-prem, GPU efficiency tends to get folded into bigger conversations: capacity planning, hardware timelines, headroom for the next model. In the cloud, the same question sharpens. Throughput, utilization, and cost sit much closer together. A service can be perfectly healthy and still look expensive. That was our situation. We had a frontend text moderation model based on mT5, served through NVIDIA Triton as a two-component ensemble: a Python tokenizer and a static ONNX model. The service was stable, but throughput per GPU was much lower than our back-of-the-envelope estimates suggested Moments like that attract the usual optimization advice: Batch more aggressively. Tune Triton. Rewrite the Python stages. Enable TensorRT. Drop precision. None of that advice is silly. Some of it is very good. The problem is that it usually arrives before the diagnosis. GPU systems are awkward that way. By the time you go hunting for "the optimization", you are often already asking the wrong question. There usually isn't one magical fix. There is a bottleneck somewhere in the stack, and then another one behind it, and usually another one behind that. The loop that kept us honest was simple: TL;DR Benchmark the system under production-shaped load. Find the bottleneck limiting throughput right now. Fix that bottleneck. Check whether the operational outcome changed. In our case, that loop took us from roughly 150 requests per second to roughly 1100 in the same saturated benchmark: a bit over 7x baseline. TensorRT delivered the first big jump. BF16 moved the ceiling aga ## Breaking the Loop: How we migrated our backup catalog for 250+ MySQL clusters to AWS DevFeed: [Breaking the Loop: How we migrated our backup catalog for 250+ MySQL clusters to AWS]() Original publisher: [Read original article]() Author: Ioannis Androulidakis Published: 2026-02-19T14:19:52Z Content type: article Language: en Sources: [Booking.com Development - Medium]() Topics: [migration](), [Amazon RDS](), [MySQL](), [Amazon Web Services](), [backups](), [Cloud](), [on-prem]() Tags: [amazon-rds](), [aws](), [backup](), [cloud](), [cloud-migration](), [data-backup](), [database](), [databases](), [migration](), [mysql](), [on-prem](), [relational-databases](), [reliability-engineering]() ### AI overview Booking.com's Database Engineering team describes migrating the backup catalog orchestrator's backend for more than 250 production MySQL clusters from a self-managed on-premises MySQL database to managed Amazon RDS for MySQL in AWS. The article discusses the migration's challenges, implemented solutions, and lessons learned. ### Source excerpt This post was originally published internally on May 9, 2025 and has been reworked for a public audience. Authors: Ioannis Androulidakis, Mohammed Gaafar Intro The Database Engineering team at Booking.com is constantly looking for ways to improve database reliability and support scaling our business. In 2025 we completed a major milestone in our effort to modernize our infrastructure: we seamlessly migrated the backend of the orchestrator that schedules and manages the daily backups of 250+ production MySQL clusters. More specifically, we moved from a self-managed MySQL database running on premises to a managed Amazon RDS MySQL database running in the cloud. This blog post delves into the challenges we faced, the solutions we implemented, and some key lessons we learned along the way. We are moving to the cloud (gradually) Cloud adoption is a journey, not a switch. Over the past few years the adoption of AWS solutions at Booking.com has been growing rapidly across different business units, allowing teams to be more autonomous, run their databases in the cloud and solve known issues with their old on-premise setup. Like customer teams, we are running our own databases to power the core services that we offer and manage databases efficiently at scale. To name a few: automatic failover of writable primaries, auto-scaling of read-only replicas, service discovery, capacity planning, online schema changes, user access management, volume backups, etc. Traditionally, the Database Engineering team has been running databases on-premises. In the emerging era of cloud databases, we wanted to compare our in-house offerings with existing cloud databases and bridge the gap between the two. Knowing that we lacked deep, hands-on experience with the operational realities of running critical infrastructure on AWS, we saw this as a unique opportunity to learn more about cloud databases while also improving the reliability of our systems. Long story short, we decided to prioritize the m ## How ZoomInfo used Temporal Cloud to scale dynamic audience management DevFeed: [How ZoomInfo used Temporal Cloud to scale dynamic audience management]() Original publisher: [Read original article]() Author: Tim Imkin Published: 2024-12-03T08:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Developer experience](), [Orchestration](), [real-time](), [data-processing](), [migration](), [configuration](), [observability](), [AWS Step Functions]() Tags: [aws-step-functions](), [cloud](), [cloud-migration](), [configuration](), [data-processing](), [developer-experience](), [development](), [observability](), [orchestration](), [real-time](), [temporal](), [temporal-concepts]() ### AI overview ZoomInfo adopted Temporal Cloud to manage continuously changing marketing audiences and address throughput and batch-processing challenges in a homegrown system. The article describes Temporal's workflow-as-code approach, cloud-agnostic deployment, and built-in observability. ### Source excerpt Learn how ZoomInfo used Temporal Cloud to scale dynamic audience management, speed up development, and process 200M+ monthly actions. ## Introducing Konfig: GitLab and Google Cloud preconfigured for startups and enterprises DevFeed: [Introducing Konfig: GitLab and Google Cloud preconfigured for startups and enterprises]() Original publisher: [Read original article]() Published: 2024-04-04T20:51:23Z Content type: release Language: en Sources: [Brave New Geek]() Topics: [GitLab](), [Google Cloud Platform (GCP)](), [internal developer platform](), [Terraform](), [migration](), [Development]() Tags: [cloud-migration](), [developer-platform](), [enterprise](), [gcp](), [gitlab](), [google-cloud](), [idp](), [internal-developer-platform](), [konfigurate](), [modernization](), [platform-engineering](), [product-development](), [real-kinetic](), [terraform]() ### AI overview Real Kinetic introduces Konfig, an opinionated configuration or distribution built around GitLab and Google Cloud for startups and enterprises. The article argues that assembling flexible, unopinionated cloud products into scalable, secure delivery platforms requires substantial engineering effort. ### Source excerpt Real Kinetic helps businesses transform how they build and deliver software in the cloud. This encompasses legacy migrations, app modernization, and greenfield development. We work with companies ranging from startups to Fortune 500s and everything in between. Most recently, we finished helping Panera Bread migrate their e-commerce platform to Google Cloud from on-prem and led their transition to GitLab. In doing this type of work over the years, we've noticed a problem organizations consistently hit that causes them to stumble with these cloud transformations. Products like GCP, GitLab, and Terraform are quite flexible and capable, but they are sort of like the piles of Legos below. ## Expanding Secrets Infrastructure to AWS Lambda DevFeed: [Expanding Secrets Infrastructure to AWS Lambda]() Original publisher: [Read original article]() Author: Michael Weissbacher Published: 2021-08-04T19:00:00Z Content type: article Language: en Sources: [Square Corner Blog](), [Square Corner Blog RSS Feed]() Topics: [AWS Lambda](), [migration](), [SPIFFE](), [Terraform](), [Cloud](), [Serverless]() Tags: [availability](), [aws-lambda](), [cloud](), [cloud-migration](), [engineering](), [security](), [serverless](), [spiffe](), [terraform]() ### AI overview The article describes how Square extended its data-center secrets infrastructure to AWS Lambda. It explains the addition of SPIFFE compatibility and a Lambda secrets syncer deployed through a Terraform module, while keeping secrets in Keywhiz and caching them in AWS Secrets Manager for cloud-native services. ### Source excerpt Extending our data center to the cloud ## Codename One Build App Enters Public Beta on Android DevFeed: [Codename One Build App Enters Public Beta on Android]() Original publisher: [Read original article]() Author: Shai Almog Published: 2018-10-09T00:00:00Z Content type: release Language: en Sources: [CodeName One]() Topics: [App](), [Android](), [migration](), [API](), [Back end](), [iOS](), [Universal Windows Platform]() Tags: [android](), [api](), [app](), [backend](), [cloud](), [cloud-migration](), [core](), [experimental](), [github](), [ios](), [notifications](), [production](), [updates]() ### AI overview Codename One announces the public beta of its new Codename One Build app for Android. The app uses a new backend and server APIs, supports build push notifications and in-app subscriptions, and is currently version 0.13 rather than production-grade. iOS and possibly UWP versions may follow. ### Source excerpt One of the big "behind the scenes" motivations for our big build cloud migration was new server API's. We now have a completely new backend and this made it easier to build a completely new Codename One App christened as "Codename One Build". This app is currently in public beta on Android which you can opt-into here. Once we feel good with the Android version we'll push out versions for iOS and maybe UWP if there's demand for that.