# Scalability Published articles for Scalability. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic DevFeed: [How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic]() Original publisher: [Read original article]() Author: Khan Academy Published: 2020-05-09T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Scalability](), [Architecture & Design](), [Serverless](), [Caching](), [Google Cloud Platform (GCP)](), [Load Balancing]() Tags: [architecture](), [caching](), [cdn](), [engineering](), [gcp](), [google-cloud](), [load-balancing](), [news](), [scalability](), [serverless](), [web]() ### AI overview Marta Kosarchyn explains how Khan Academy handled site usage reaching 2.5 times the previous year's level during the coronavirus pandemic. The article attributes the scalability to Google Cloud services, serverless infrastructure, Datastore, Memcache, Fastly CDN caching, and advance preparation. ### Source excerpt By Marta Kosarchyn Talk about rapid scaling... A few months ago I posted some thoughts on scaling and ... Read more ## ML based ranking using Nrtsearch DevFeed: [ML based ranking using Nrtsearch]() Original publisher: [Read original article]() Author: Mohammad Mohtasham (Software Engineer); Tao Yu (Software Engineer) Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [Inference](), [Scalability](), [Latency](), [recommendation systems](), [Open Source]() Tags: [bridge](), [inference](), [machine-learning](), [overhead](), [performance](), [pipeline](), [scalability](), [service]() ### AI overview Yelp's Nrtsearch Inference Plugin embeds machine-learning ranking directly in the search layer. The article explains the scoring workflow, including model configuration, feature extraction, candidate ranking, and application-specific business logic. It describes how co-locating feature storage and inference reduces network transfer, serialization overhead, and latency compared with a standalone inference service. ### Source excerpt We've extended Nrtsearch with the Inference Plugin, which embeds ML-based ranking directly in the search layer -- eliminating the need for a standalone scoring service. We use Nrtsearch (read more information on the blog post), a Lucene-based open-source search engine built by Yelp, to power a variety of applications such as business search, reviews search, ad delivery and photo search. In this blog post, we give a high-level overview of the Machine Learning (ML) based scoring workflow in Nrtsearch. We'll show how ML models are configured and loaded, and how different applications use custom business logic to develop, test, and... ## Performance improvements in Percona Server 8.4.11-11 DevFeed: [Performance improvements in Percona Server 8.4.11-11]() Original publisher: [Read original article]() Author: Bogdan Degtyariov Published: 2026-09-15T11:52:54Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [Percona Server for MySQL](), [Percona](), [MySQL](), [Cache](), [Scalability](), [Database]() Tags: [benchmark](), [benchmarks](), [cache](), [io](), [mysql](), [percona](), [percona-server-for-mysql](), [performance](), [scalability]() ### AI overview This article describes performance and scalability improvements in Percona Server for MySQL 8.4.11-11, focusing on changes to the InnoDB buffer pool and page flushing. It explains how narrowing mutex coverage and using finer-grained latching allows physical reads to proceed more in parallel, particularly for read-heavy, I/O-bound workloads. ### Source excerpt Focusing on Percona Server 8.4.11-11 My previous post (Performance Progression of Percona Server for MySQL 8.4) did a brief review of the performance changes in Percona Server for MySQL 8.4 released in 2026. I recommend reading it first to better understand the material in this post. Version 8.4.11-11 includes patches that deliver significant improvements in ... Continued The post Performance improvements in Percona Server 8.4.11-11 appeared first on Percona. ## Evolving Pinterest's Embedding Retrieval Platform DevFeed: [Evolving Pinterest's Embedding Retrieval Platform]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-09-11T15:01:03Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [Architecture & Design](), [Algorithms](), [Scalability](), [Latency](), [IO]() Tags: [ann](), [cost](), [embedding](), [embeddings](), [engineering](), [infrastructure](), [latency](), [memory](), [models](), [pinterest](), [platform](), [quantization](), [real-time](), [retrieval](), [scalability](), [search]() ### AI overview Pinterest describes evolving its Manas embedding-retrieval platform to address the cost, scale, and flexibility challenges of serving billions of embeddings. The excerpt covers ANN search, vector quantization, and SSD-based serving. ### Source excerpt Authors: Bowen Zhou | Staff Software Engineer; Shan Gao | Senior Software Engineer; Jingwen Hu | Software Engineer II; Wenjiang Chu | Staff Software Engineer The Billion-Embedding Challenge At Pinterest, the "signal" is our lifeblood. Whether it's a home decor enthusiast finding the perfect rug or a fashion seeker discovering a new aesthetic, our discovery engine relies on understanding deep semantic relationships to help our users find inspirations. Over the last few years, the explosive growth of embedding-based retrieval has fundamentally transformed how we surface these signals -- and at the heart of that transformation is Manas, Pinterest's in-house distributed search platform. Embedding Retrieval is one of the core capabilities of Manas, supporting multiple approximate nearest neighbor search algorithms, hybrid queries with both token and embedding clauses, as well as real-time updates to ensure fresh contents become searchable within seconds. Deployed on over 80 clusters and serving billions of embeddings, Manas embedding retrieval powers all major product surfaces at Pinterest including Home Feed, Search, Related Pins, Ads, and Notifications. However, as our corpus scales toward tens of billions of embeddings and our models capture increasingly complex interactions, we face mounting challenges around cost efficiency, scalability, and flexibility. On the infrastructure side, traditional ANN algorithms like HNSW are notoriously memory-hungry -- they require the entire index to reside in RAM to maintain low query latency, making cost grow linearly with corpus size. On the modeling side, the classic two-tower retrieval paradigm is too restrictive: it reduces each candidate to a single embedding and scores relevance through a simple dot product, leaving little room to express richer, context-dependent notions of similarity. To tackle these challenges, our team has been evolving Manas's embedding retrieval stack across three fronts: Quantization. We reduce the memor ## Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows DevFeed: [Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-11T14:17:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Orchestration](), [Amazon S3](), [Kafka](), [Apache Iceberg]() Tags: [amazon-s3](), [apache-iceberg](), [apache-kafka](), [architecture-design](), [asynchronous-architecture](), [cassandra](), [cloud-architecture](), [concurrency](), [development](), [devops](), [distributed-systems](), [elasticsearch](), [java-operator-sdk](), [kafka](), [latency](), [microservices](), [netflix](), [netflix-conductor-4-workflow](), [news](), [orchestration](), [s3](), [scalability](), [windows-workflow-foundation](), [workflow](), [workflow-bpm](), [workflow-foundation]() ### AI overview Netflix reworked Conductor 4.0 to scale workflow orchestration to roughly 200,000 definitions and 420 million monthly executions. The redesign raises supported workflow size to 30,000 tasks and reports a roughly 40% reduction in p99 evaluation latency by loading only task data needed for each decision. ### Source excerpt Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls. By Leela Kumili ## Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock DevFeed: [Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock]() Original publisher: [Read original article]() Author: Tanvi Girinath Published: 2026-09-08T22:06:58Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [AI, ML & Data Engineering](), [AI-assisted coding](), [AI Chat]() Tags: [agents](), [amazon-bedrock](), [announcements](), [api](), [aws](), [cache](), [chatgpt](), [codex](), [gpt](), [intermediate-200](), [openai](), [scalability](), [security]() ### AI overview GPT-6 Astra is generally available on Amazon Bedrock. The article describes its reasoning, codebase work, browser use, prompt caching, and enterprise controls for production workloads. ### Source excerpt GPT-6 Astra from OpenAI is now generally available on Amazon Bedrock. It brings deeper reasoning and sharper judgment to your most demanding tasks, running on the Amazon Bedrock inference engine built for high performance, security, and scale. ## Internal Developer Portals: Why Native CI/CD Drives Scale DevFeed: [Internal Developer Portals: Why Native CI/CD Drives Scale]() Original publisher: [Read original article]() Author: Rashmi Hegde Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Platform Engineering](), [CI/CD](), [developer velocity](), [Automation](), [Scalability]() Tags: [automation](), [ci-cd](), [developer-velocity](), [idp](), [scalability](), [self-service](), [velocity]() ### AI overview This article argues that internal developer portals need native CI/CD integration to move beyond service catalogs and documentation. It explains that integrated pipelines can reduce context switching, support self-service workflows, help enforce standards, and improve developer velocity and scalability. ### Source excerpt Internal developer portals need native CI/CD integration to scale effectively. Learn how integrated pipelines improve velocity. Explore Harness IDP. | Blog ## Data Engineering Weekly #286 DevFeed: [Data Engineering Weekly #286]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-07T00:18:06Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [agent observability](), [Large Language Model](), [data-modeling](), [Monitoring](), [Scalability](), [semantic-layer](), [parquet]() Tags: [ai](), [ai-agents](), [data-engineering](), [data-modeling](), [llm](), [monitoring](), [newsletter](), [observability](), [parquet](), [scalability](), [semantic-layer](), [weekly]() ### AI overview Data Engineering Weekly #286 is a curated newsletter covering data platform fundamentals, mathematics for machine learning, agentic machine learning at Instacart, Netflix's lifecycle for LLM-as-a-Judge systems, semantic layers and data modeling for AI analytics, and Apache Pinot scalability. ### Source excerpt The Weekly Data Engineering Newsletter ## Open by design: How Elastic supports MOSA, the DoD's Modular Open Systems Approach DevFeed: [Open by design: How Elastic supports MOSA, the DoD's Modular Open Systems Approach]() Original publisher: [Read original article]() Author: Woody Walton Published: 2026-09-04T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Architecture & Design](), [data](), [Instrumentation](), [telemetry]() Tags: [acquisition](), [analytics](), [data](), [defense-government-open-source-standards](), [design](), [elasticsearch-platform](), [government-public-sector](), [instrumentation](), [integration](), [open](), [opentelemetry](), [scalability](), [systems]() ### AI overview Elastic explains how its data and analytics platform can support the US Department of Defense Modular Open Systems Approach through modularity, open standards, and component removability. ### Source excerpt Major US defense acquisition programs are required to be designed around a Modular Open Systems Approach (MOSA). Here's what program offices need to know about how Elastic can support MOSA at the data and analytics layer. ## How Every Team Can Build in Port at Scale, Without the Bottleneck DevFeed: [How Every Team Can Build in Port at Scale, Without the Bottleneck]() Original publisher: [Read original article]() Author: Guy Berman Published: 2026-09-03T14:39:07Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Authorization](), [Scalability](), [sdlc](), [pull-requests](), [Security]() Tags: [agent](), [agentic](), [compliance](), [dashboards](), [development](), [production](), [pull-requests](), [scalability](), [scale](), [sdlc](), [security](), [workflow]() ### AI overview The article explains how every team can build and own a scoped part of Port at scale. It presents fine-grained permissions, role-based access controls, and controlled promotion across development, staging, and production environments as ways to reduce platform-team bottlenecks while limiting risk. ### Source excerpt How every team can build in Port at scale: scoped permissions, dynamic policies, and safe rollouts across environments. ## MCP Went Stateless: What Now? DevFeed: [MCP Went Stateless: What Now?]() Original publisher: [Read original article]() Author: Janet Wagner Published: 2026-09-03T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [Model Context Protocol (MCP)](), [Scalability](), [MCP Server](), [Architecture & Design](), [Security](), [Tool]() Tags: [ai](), [ai-agents](), [api-gateway](), [api-security](), [api-standards](), [artificial-intelligence](), [blog](), [cloud-native](), [developers](), [http](), [llm](), [mcp](), [mcp-server](), [model-context-protocol](), [scalability](), [security](), [strategy]() ### AI overview The article examines the shift to stateless MCP and its implications for scalability, state management, security, gateways, and MCP server architecture. ### Source excerpt The latest release of the Model Context Protocol (MCP) specification has created a lot of buzz in tech circles. Why? Primarily because this AI communication protocol has now gone stateless! This shift from stateful to stateless impacts how developers of MCP servers and AI tools use the protocol moving forward. In addition, the change impacts ... ## How AgentFlo built AI sales agents with Amazon Bedrock AgentCore - Part 1 DevFeed: [How AgentFlo built AI sales agents with Amazon Bedrock AgentCore - Part 1]() Original publisher: [Read original article]() Author: Muhammad Musab Iqbal Published: 2026-08-20T00:32:52Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [AI Chat]() Tags: [ai-agents](), [amazon-bedrock-agentcore](), [customer-solutions](), [sales](), [scalability](), [strands-agents](), [whatsapp]() ### AI overview AgentFlo describes building always-on AI sales agents with Amazon Bedrock AgentCore and the Strands Agents SDK. Part 1 focuses on velocity, standardization, and scalability for commerce conversations. ### Source excerpt Learn how AgentFlo built always-on AI sales agents on Amazon Bedrock AgentCore and the Strands Agents SDK. Part 1 covers three pillars of production-grade agents--velocity, standardization, and scalability--including recipe-based deployment, tool routing through AgentCore Gateway, and elastic, stateful commerce conversations. ## What is a data topology? DevFeed: [What is a data topology?]() Original publisher: [Read original article]() Author: Ahmed Darwich Published: 2026-08-17T00:00:00Z Content type: article Language: en Sources: [Blog -- PlanetScale]() Topics: [data](), [vitess]() Tags: [architecture](), [data](), [database](), [engineering](), [internals](), [json](), [mapping](), [neki](), [performance](), [postgres](), [postgresql](), [routing](), [scalability](), [transactions](), [vitess]() ### AI overview The article explains Neki data topologies: JSON configurations that map logical PostgreSQL tables to physical shard groups so routers can place data and route queries. ### Source excerpt A data topology describes the sharding scheme a Neki router uses to map logical PostgreSQL tables to physical shards and route queries. ## Software Delivery Platform: Key Features & How to Evaluate DevFeed: [Software Delivery Platform: Key Features & How to Evaluate]() Original publisher: [Read original article]() Author: Eric Minick Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Software](), [CI/CD](), [Continuous Delivery (CD)](), [Continuous integration]() Tags: [audit](), [automation](), [blog](), [ci-cd](), [ci-cd-pipeline](), [governance](), [how-to](), [progressive-delivery](), [rollback](), [scalability](), [security](), [software-delivery](), [visibility]() ### AI overview This article explains how a software delivery platform differs from a standalone CI/CD tool. It describes a platform as an integrated system covering the full after-code lifecycle, including continuous integration, continuous delivery, security, governance, insights, coordination, and rollback, then outlines criteria for evaluating vendors. ### Source excerpt A software delivery platform isn't just a CI/CD tool. Get the must-have feature checklist and the demo questions to use when evaluating vendors. | Blog ## Terraform Scalability: When IaC Outgrows Your Setup DevFeed: [Terraform Scalability: When IaC Outgrows Your Setup]() Original publisher: [Read original article]() Author: Mrinalini Sugosh Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Terraform](), [Infrastructure as code](), [Scalability](), [iac-security]() Tags: [dynamodb](), [harness](), [iac](), [s3](), [scalability](), [security](), [terraform]() ### AI overview This article examines how Terraform scalability challenges emerge as infrastructure code and teams grow. It discusses bottlenecks involving state management, collaboration, governance, large state files, locking, plan times, module sprawl, and version drift, and introduces Harness IaCM as a management solution. ### Source excerpt Terraform scalability issues slow teams down. Learn how to overcome IaC bottlenecks with better management. See how Harness helps. | Blog ## CTO.new Coordinates Tens of Thousands of AI Agents with a Database per Project on Turso DevFeed: [CTO.new Coordinates Tens of Thousands of AI Agents with a Database per Project on Turso]() Original publisher: [Read original article]() Author: Glauber Costa Published: 2026-08-06T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [AI Bots](), [AI-assisted coding](), [AI, ML & Data Engineering]() Tags: [ai-agents](), [autonomous-agents](), [coding](), [database](), [infrastructure](), [scalability](), [security](), [software-architecture](), [turso](), [vibe-coding]() ### AI overview CTO.new uses a dedicated Turso database for each AI-agent project to coordinate workloads at scale. The article describes lower database costs compared with its earlier Supabase setup and the platform's return to Turso for agent infrastructure management. ### Source excerpt CTO.new gives every team of AI agents its own Turso database as a coordination layer, running tens of thousands of databases on a $500 per month plan. ## CockroachDB Kubernetes Operator launches with full lifecycle automation DevFeed: [CockroachDB Kubernetes Operator launches with full lifecycle automation]() Original publisher: [Read original article]() Author: Ethan Shmargad,Pritesh Lahoti Published: 2026-08-05T00:00:00Z Content type: release Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Kubernetes](), [Deployment](), [Automation](), [migration](), [Scalability]() Tags: [automation](), [cockroach-labs](), [cockroachdb](), [database](), [deployment](), [kubernetes](), [migration](), [production](), [scalability]() ### AI overview Cockroach Labs announces the CockroachDB Kubernetes Operator, designed to connect CockroachDB-aware operations with Kubernetes. It provides lifecycle automation for deployment, upgrades, scaling, certificate rotation, and storage management, and supports multi-region deployments and workflows including Helm, GitOps, private registries, and air-gapped environments. ### Source excerpt The production-proven K8s operator is now available with full lifecycle automation, zero-downtime migration, as well as the flexibility and scalability to fit how your team actually works. ## DDIA 2nd Edition Excerpt: On Scalability DevFeed: [DDIA 2nd Edition Excerpt: On Scalability]() Original publisher: [Read original article]() Author: ScyllaDB Team Published: 2026-08-04T12:59:50Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [Scalability](), [Architecture & Design](), [App](), [data](), [Database]() Tags: [applications](), [architecture](), [community](), [data](), [engineering](), [optimization](), [scalability](), [scale](), [systems]() ### AI overview An excerpt from the second edition of Designing Data-Intensive Applications explains scalability as a system's ability to cope with increased load. It discusses growth in users and data, trade-offs for early-stage products, architectural limits, resource expansion, and performance bottlenecks. ### Source excerpt Martin Kleppmann and Chris Riccomini's scalability considerations for designing data-intensive applications -- from the second edition of the Designing Data-Intensive Applications book ## Install Terraform: Secure & Scalable IaC Setup Guide DevFeed: [Install Terraform: Secure & Scalable IaC Setup Guide]() Original publisher: [Read original article]() Author: Mrinalini Sugosh Published: 2026-07-24T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [Terraform](), [Infrastructure as code](), [iac-security](), [Scalability](), [Automation](), [Command-line interface]() Tags: [cli](), [guide](), [iac](), [infrastructure-as-code](), [install](), [scalability](), [security](), [terraform]() ### AI overview A guide to installing Terraform across platforms with security hardening and scalability in mind. It covers installation, configuration, state management, secret handling, version control, team workflows, and production-oriented infrastructure automation. ### Source excerpt Learn to install Terraform with security and scalability built in. Master IaC automation setup from start to finish. See how Harness helps. | Blog ## Week Ending July 19, 2026 DevFeed: [Week Ending July 19, 2026]() Original publisher: [Read original article]() Published: 2026-07-23T22:00:00Z Content type: news Language: en Sources: [Last Week in Kubernetes Development]() Topics: [Kubernetes](), [releases](), [Scalability](), [Streaming](), [x86]() Tags: [developer](), [kubernetes](), [news](), [releases](), [review](), [scalability](), [streaming]() ### AI overview This developer news roundup covers Kubernetes community and release updates, including Feature Discovery v0.19.0, Kubernetes v1.37 entering code freeze, patch releases, EtcdRangeStream becoming enabled by default, and ClusterTrustBundles reaching general availability. It also reports a watch cache initialization improvement on 5,000-node clusters and upcoming maintainer and steering committee activities. ### Source excerpt Developer News ## Alpha features in Kubernetes 1.36 DevFeed: [Alpha features in Kubernetes 1.36]() Original publisher: [Read original article]() Author: Nigel Douglas Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Kubernetes](), [AI Platform](), [Hardware](), [Scalability](), [Security](), [API](), [cpu](), [real-time]() Tags: [ai-ml](), [api](), [cpu](), [hardware](), [kubernetes](), [real-time](), [scalability](), [security]() ### AI overview An overview of selected Alpha-stage features planned for Kubernetes 1.36, focusing on five Dynamic Resource Allocation enhancements for AI/ML workloads. The article explains improved hardware discovery, real-time resource availability, list-typed device attributes, group-aware resource claims, and native resource requests. ### Source excerpt Explore the top 10 new Alpha features in Kubernetes 1.36. Learn about 5 DRA advancements for AI/ML workloads, workload-aware scheduling, new security policies, and major scalability gains ## The hyperscale blueprint: Why cloud giants prioritize VMs for Kubernetes over bare metal DevFeed: [The hyperscale blueprint: Why cloud giants prioritize VMs for Kubernetes over bare metal]() Original publisher: [Read original article]() Author: Jack Wallen Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [virtualization](), [Kubernetes](), [Containers](), [Scalability](), [Security](), [Amazon Web Services](), [Nitro System](), [Multi-tenancy]() Tags: [aws](), [containers](), [kubernetes](), [multi-tenancy](), [nitro-system](), [scalability](), [security](), [virtual-machines](), [virtualization]() ### AI overview Cloud providers generally run Kubernetes and other containerized workloads on virtual machines rather than bare metal because virtualization offers security, isolation, flexibility, scalability, and near-bare-metal performance. AWS Nitro and Firecracker help reduce virtualization overhead, while bare metal remains reserved for specialized performance or hardware-access requirements. ### Source excerpt Why do cloud giants use VMs for Kubernetes? Learn why AWS, Azure, and Google prioritize Virtual Machines for containerized workloads over bare metal, focusing on security and scalability. ## 5 most overlooked but critical considerations in container platform selection DevFeed: [5 most overlooked but critical considerations in container platform selection]() Original publisher: [Read original article]() Author: Pankaj Gupta Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Containers](), [Kubernetes](), [Cloud Native Ecosystem](), [Scalability](), [Security]() Tags: [cloud](), [containers](), [infrastructure](), [kubernetes](), [operations](), [performance](), [platforms](), [scalability](), [security](), [strategy](), [vks](), [vmware](), [vmware-cloud-foundation]() ### AI overview The article identifies five often-overlooked factors in container platform selection. It emphasizes rapid access to new CNCF-conformant Kubernetes versions, long standard support periods, the ability to run multiple Kubernetes versions on one host, and treating VMs and containers as first-class citizens. It presents VMware Cloud Foundation with vSphere Kubernetes Service as an example of a platform offering rapid Kubernetes access and a long support window. ### Source excerpt Choosing a container platform that's built for the future requires careful consideration. Learn more about critical factors such as long support lifecycles, multi-version flexibility, and first-class treatment for both VMs and containers. ## How NIIT helped Kyndryl certify more than 100 platform engineers DevFeed: [How NIIT helped Kyndryl certify more than 100 platform engineers]() Original publisher: [Read original article]() Author: Karolina Semiuk Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [internal developer portal](), [Developer experience](), [Scalability](), [DevOps]() Tags: [best-practices](), [certifications](), [community](), [devops](), [efficiency](), [engineering](), [enterprise](), [organizational](), [platform](), [scalability](), [software-delivery]() ### AI overview The article describes how NIIT helped Kyndryl certify more than 100 platform engineers. It argues that team-scale certification creates shared language, reduces communication problems, standardizes best practices, and strengthens platform engineering capabilities across a distributed enterprise workforce. ### Source excerpt NIIT enabled Kyndryl to certify 100+ platform engineers, fostering shared language and organizational transformation through team-based certification programs [Next page]()