# on-prem On-prem is a software deployment model in which resources are hosted and operated on an organization's own physical computers, servers, and infrastructure. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Datacenter developers want your backyard. FAS says negotiate harder DevFeed: [Datacenter developers want your backyard. FAS says negotiate harder]() Original publisher: [Read original article]() Author: Dan Robinson Published: 2026-09-14T17:26:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [datacenter](), [on-prem]() Tags: [ai](), [community-benefits-agreements](), [data-center-policy](), [datacenter](), [datacenters](), [federation-of-american-scientists](), [infrastructure](), [on-prem](), [report]() ### AI overview A report advises local officials to negotiate carefully with datacenter developers before approving projects, focusing on tax breaks, water use, noise, and decommissioning obligations. ### Source excerpt Tax breaks, water, noise, decommissioning - report tells local officials what to nail down before signing ## Deploying AI You Control Doesn't Need to be So Hard DevFeed: [Deploying AI You Control Doesn't Need to be So Hard]() Original publisher: [Read original article]() Author: Jeetu Patel Published: 2026-09-10T09:00:50Z Content type: news Language: en Sources: [Cisco Blogs]() Topics: [Artificial Intelligence](), [AI Platforms/Deployment](), [AI Factory](), [Nvidia](), [Critical Infrastructure](), [post-training](), [data](), [Nemotron](), [on-prem](), [Fine-tuning](), [Cloud](), [Benchmark]() Tags: [ai](), [ai-factory](), [ai-infrastructure](), [ai-strategy](), [cisco-cloud-control-framework](), [cisco-secure-ai-factory](), [cloud](), [critical-infrastructure](), [data](), [executive-platform](), [fine-tuning](), [infrastructure](), [model](), [models](), [nemotron](), [nvidia](), [on-prem](), [post-training]() ### AI overview Cisco announces a collaboration with Palantir to deliver Palantir's Ontology for Cybersecurity through Cisco's Secure AI Factory, using NVIDIA as a preferred full-stack foundation for Palantir's Sovereign AI OS. The article argues that enterprise AI decisions should balance intelligence, cost, and control, including custom evaluations, post-training with proprietary data, and deployment in the cloud, at the edge, or on-premises. ### Source excerpt Announcing a collaboration with Palantir to deliver Cisco's Secure AI Factory with NVIDIA as a preferred full-stack foundation for Palantir's Sovereign AI OS. ## On the Ground at VMware Explore 2026: How Lenovo Is Partnering with VMware for Turnkey AI and Taming the Memory Crunch DevFeed: [On the Ground at VMware Explore 2026: How Lenovo Is Partnering with VMware for Turnkey AI and Taming the Memory Crunch]() Original publisher: [Read original article]() Author: Tom Fenton Published: 2026-09-01T18:28:19Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Generative AI](), [on-prem](), [cloud-infrastructure](), [datacenter](), [Cloud](), [data]() Tags: [2026](), [ai](), [cloud](), [data-centers](), [enterprise](), [enterprise-storage](), [generative-ai](), [infrastructure](), [on-prem](), [technology]() ### AI overview At VMware Explore 2026, StorageReview discusses Lenovo's partnership with VMware around turnkey generative AI infrastructure, the return to on-premises deployments, and memory supply constraints. The article describes moving memory tiering into the VMware Cloud Foundation hypervisor layer, using NVMe drives to reduce dependence on expensive DRAM without requiring application or operating-system rewrites. ### Source excerpt At VMware Explore 2026, we had the chance to sit down with Stuart McRae, Executive Director of Lenovo's Enterprise Storage, Software, and Solutions Offering Group. Rather than focusing on a specific product or technology, as we often do, this conversation took a broader view. We covered everything from how AI is reshaping buyer options and The post On the Ground at VMware Explore 2026: How Lenovo Is Partnering with VMware for Turnkey AI and Taming the Memory Crunch appeared first on StorageReview.com. ## Chaining Activities -- from text to vectors DevFeed: [Chaining Activities -- from text to vectors]() Original publisher: [Read original article]() Author: Houman Kargaran Published: 2026-08-12T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [data](), [Embeddings](), [on-prem](), [pii](), [Redis](), [retry](), [Sequences]() Tags: [article](), [community](), [data-pipeline](), [embeddings](), [on-prem](), [pii](), [redis](), [retries](), [storage]() ### AI overview This guest post explains a Temporal workflow that chains discrete Activities to process complaints while maintaining a PII boundary. It uses an on-premises all-MiniLM-L6-v2 model for embeddings, caches vectors in Redis, stores them externally, and relies on deterministic workflow sequencing and retry policies. ### Source excerpt Learn how to build a durable, PII-conscious data pipeline with Temporal using on-prem embeddings, idempotent Activities, external vector storage, and retries. ## "Just rewrite it": What platform teams really think about modernization DevFeed: ["Just rewrite it": What platform teams really think about modernization]() Original publisher: [Read original article]() Author: Aarthi Mahesh Published: 2026-08-06T16:00:00Z Content type: opinion Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Platform Engineering](), [Kubernetes](), [on-prem](), [Cloud]() Tags: [ai-infrastructure](), [cloud-native](), [cost](), [infrastructure](), [kubernetes](), [legacy](), [modernization](), [nutanix](), [platform](), [platform-engineering](), [post](), [sponsor-nutanix](), [sponsored](), [sponsored-post]() ### AI overview The article argues that platform teams often operate separate cloud-native/Kubernetes and virtual-machine environments, creating duplicated infrastructure, expertise, governance, and costs. It contends that rewriting mission-critical applications is often not a practical modernization strategy and recommends retaining them while expanding cloud-native platforms. ### Source excerpt Mergers, acquisitions, and the steady churn of business and technology initiatives are creating something nobody asked for: Duplicate infrastructure and The post "Just rewrite it": What platform teams really think about modernization appeared first on The New Stack. ## I spent two months screen-sharing to ship one integration. So I rebuilt it on Temporal. DevFeed: [I spent two months screen-sharing to ship one integration. So I rebuilt it on Temporal.]() Original publisher: [Read original article]() Author: Stephen Montague Published: 2026-08-06T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [proxy](), [networking](), [legacy application](), [Messaging](), [on-prem](), [retry](), [Cloud]() Tags: [legacy-application](), [messaging](), [networking](), [on-prem](), [proxy](), [retry](), [software](), [temporal-voices]() ### AI overview The article explains how to connect a cloud application to a firewalled on-premises edge site using an egress-only proxy and a single outbound Temporal connection. It describes the operational difficulties of a legacy, one-way integration and the need for bidirectional messaging, retry handling, and testing. ### Source excerpt How to durably reach a firewalled edge site with no inbound ports: an egress-only proxy that hot-reloads config over a single outbound Temporal connection. ## ClusterControl 2.5.0 brings ClickHouse support to on-prem, cloud and hybrid environments DevFeed: [ClusterControl 2.5.0 brings ClickHouse support to on-prem, cloud and hybrid environments]() Original publisher: [Read original article]() Author: Kyle Buzzell Published: 2026-08-05T08:38:18Z Content type: release Language: en Sources: [SeveralNines]() Topics: [clickhouse](), [Cloud](), [on-prem](), [Databases]() Tags: [analytics](), [clickhouse](), [cloud](), [clustercontrol](), [clustercontrol-releases](), [clustercontrol-v2-5-0](), [database](), [olap](), [on-prem](), [release](), [sovereign-dbaas]() ### AI overview ClusterControl 2.5.0 adds support for ClickHouse across on-premises, cloud, and hybrid environments. The release enables deployment and lifecycle management for standalone or replicated ClickHouse clusters, alongside PostgreSQL backup workflow upgrades, usage metering, and a restored ClusterControl v1 capability. ### Source excerpt ClusterControl 2.5.0 is here, and it marks a milestone for the platform: ClickHouse joins the family of supported database engines -- and with it, a capability no analytics vendor's cloud can offer you. Wherever you run ClusterControl -- on-premises, in any cloud, or hybrid -- you can now deploy ClickHouse either as a standalone OLAP [...] The post ClusterControl 2.5.0 brings ClickHouse support to on-prem, cloud and hybrid environments appeared first on Severalnines. ## Инференс LLM: от KV-кэша до продакшен-деплоя DevFeed: [Инференс LLM: от KV-кэша до продакшен-деплоя]() Original publisher: [Read original article]() Author: a\_ryzhov (hh.ru, Конференции Олега Бунина (Онтико)) Published: 2026-07-27T05:30:45Z Content type: tutorial Language: ru Sources: [HeadHunter RU]() Topics: [Large Language Model](), [Caching](), [on-prem](), [genai](), [MLOps](), [sglang](), [vllm]() Tags: [2026](), [compute](), [genai](), [http](), [inference](), [kv-cache](), [llm](), [mlops](), [on-prem](), [sglang](), [sram](), [vllm]() ### AI overview This Russian-language developer article explains how LLM inference behaves in on-premises production environments in 2026. It argues that GPU memory management is the main efficiency constraint, describes how KV caching shifts decoding from compute-bound to memory-bandwidth-bound work, and introduces vLLM and SGLang as ways to address the problem. ### Source excerpt Привет! Я Саша Рыжов, MLOps-инженер в hh.ru, уже три года занимаюсь развитием инфраструктуры для искусственного интеллекта. Компании, которые развивают GenAI, рано или поздно приходят к задачам по запуску LLM на собственном железе. В статье я расскажу, как обстоят дела с движками инференса в 2026 году и как запустить on-prem-прод и не изобрести при этом велосипед. Читать далее ## How Alien uses Turso to make BYOC deployments reliable DevFeed: [How Alien uses Turso to make BYOC deployments reliable]() Original publisher: [Read original article]() Author: Alon Gubkin Published: 2026-06-22T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [Deployment](), [Cloud](), [Databases](), [AWS IAM](), [telemetry](), [on-prem]() Tags: [aws](), [azure](), [database](), [embedded](), [gcp](), [iam](), [infrastructure](), [monitoring](), [on-prem](), [security](), [software](), [telemetry](), [turso]() ### AI overview Alien uses an embedded, encrypted Turso database to support reliable Bring Your Own Cloud deployments across customer-controlled AWS, GCP, Azure, and on-premises environments. The article explains how Alien's centralized control plane and local operator handle deployments, updates, telemetry, and failures without requiring unrestricted access to customer infrastructure. ### Source excerpt How Alien uses an embedded, encrypted Turso database to keep BYOC deployments manageable inside environments it does not control. ## ScyllaDB Customer Experience Spotlight: Faisal Saeed DevFeed: [ScyllaDB Customer Experience Spotlight: Faisal Saeed]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-06-11T12:01:56Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [ScyllaDB Cloud](), [data-architecture](), [migration](), [Automation](), [Database](), [on-prem](), [MariaDB]() Tags: [app](), [automation](), [blog](), [cloud](), [community](), [customer](), [cx-profiles](), [data-modeling](), [database](), [demo](), [india](), [migration](), [on-prem](), [scale](), [scylladb](), [scylladb-cloud](), [testing](), [tool]() ### AI overview Faisal Saeed, Principal Customer Engineer at ScyllaDB, describes his work supporting existing customers, evaluating deployments, data modeling, and migrations to ScyllaDB Enterprise or ScyllaDB Cloud. He also discusses the ScyllaDB Automation Framework, which automates cluster operations, workload execution, and stress testing, and highlights ScyllaDB Cloud and a large-scale customer use case in India. ### Source excerpt Meet Faisal Saeed, Principal Customer Engineer on the Customer Experience team here at ScyllaDB. ## Reliability Engineering for Air-Gapped Systems DevFeed: [Reliability Engineering for Air-Gapped Systems]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-04-03T21:03:06Z Content type: article Language: en Sources: [Alex Ewerlof Notes]() Topics: [observability](), [systems](), [Resilience](), [on-prem](), [Security](), [Requirements]() Tags: [observability](), [on-prem](), [reliability-engineering](), [resilience](), [security](), [supply-chain-attacks](), [systems](), [work]() ### AI overview This article discusses reliability engineering for air-gapped systems where software teams cannot access runtime data, metrics, or logs. It describes using SLI and SLO practices, alerting, secure operational processes, and on-premises maintenance to manage reliability in systems isolated from the internet. ### Source excerpt Tips and tricks to work around inaccessible observability ## Virtual machines, virtually everywhere - and with real security gaps DevFeed: [Virtual machines, virtually everywhere - and with real security gaps]() Original publisher: [Read original article]() Author: Tomáš Foltýn Published: 2026-03-25T10:00:00Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [Cloud](), [Security](), [cloud-infrastructure](), [Amazon Web Services](), [Azure](), [on-prem](), [Amazon EC2](), [Amazon S3]() Tags: [amazon-web-services-aws](), [apis](), [aws](), [azure](), [breach](), [business-security](), [cloud](), [identity](), [infrastructure](), [ip](), [on-prem](), [operations](), [s3](), [scale](), [security](), [speed](), [storage]() ### AI overview Cloud adoption has created a widespread virtual-machine sprawl problem. Unmanaged or poorly monitored VMs may miss operating-system updates, retain outdated access policies, and evade security operations, creating subtle risks alongside more visible threats such as misconfigured storage buckets and exposed APIs. ### Source excerpt Cloud VMs offer unmatched speed, scale and flexibility - all of which could eventually count for little if they're left to fend for themselves ## 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 ## Gremlin Release Roundup 2025: Reliability across AI, on-prem, and applications DevFeed: [Gremlin Release Roundup 2025: Reliability across AI, on-prem, and applications]() Original publisher: [Read original article]() Author: Andre Newman Published: 2025-12-15T00:00:00Z Content type: release Language: en Sources: [Gremlin Blog]() Topics: [on-prem](), [Failure Flags](), [experiments](), [Artificial Intelligence](), [MCP Server](), [telemetry](), [SRE]() Tags: [2025](), [ai](), [analysis](), [experiments](), [failure-flags](), [features](), [gremlin](), [mcp](), [mcp-server](), [no-code](), [on-prem](), [outages](), [release](), [releases](), [sre](), [telemetry]() ### AI overview Gremlin's 2025 release roundup describes improvements aimed at preventing outages and making reliability testing easier. Highlights include Reliability Intelligence for analyzing failures and recommending remediations, a Gremlin MCP server for querying environments through an LLM, new experiments and Failure Flags capabilities, expanded platform support, streamlined onboarding, and web UI refinements. ### Source excerpt This year's release roundup covers our new on-prem offering, new Failure Flags features, intelligent analysis of failed experiments, and much more. ## Introducing: Devstral 2 and Mistral Vibe CLI. DevFeed: [Introducing: Devstral 2 and Mistral Vibe CLI.]() Original publisher: [Read original article]() Published: 2025-12-09T12:00:00Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [coding](), [Large Language Model](), [Open Source](), [Automation](), [GitHub Copilot CLI](), [Transformer](), [Command-line interface](), [context window](), [Terminal](), [Fine-tuning](), [on-prem]() Tags: [ai](), [ai-assistants](), [automation](), [autonomous-agents](), [claude](), [cli](), [code](), [coding](), [context-window](), [deepseek](), [fine-tuning](), [hardware](), [models](), [on-prem](), [open-source](), [terminal]() ### AI overview Mistral introduces Devstral 2, Devstral Small 2, and the Mistral Vibe CLI. The open coding models target autonomous software engineering, with deployment options spanning APIs, local consumer hardware, and on-premises environments. Devstral 2 supports a 256K context window, codebase-wide changes, failure recovery, and fine-tuning, while Mistral Vibe provides terminal-based code automation. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Gremlin's KubeCon '25 reliability track DevFeed: [Gremlin's KubeCon '25 reliability track]() Original publisher: [Read original article]() Author: Andre Newman Published: 2025-11-06T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Kubernetes](), [Resilience](), [observability](), [migration](), [on-prem](), [debugging]() Tags: [2025](), [best-practices](), [debugging](), [kubecon](), [kubernetes](), [migration](), [observability](), [on-prem](), [resilience]() ### AI overview Gremlin curates an unofficial reliability track for KubeCon North America 2025, highlighting talks on reducing Kubernetes costs while improving reliability, operational resilience, etcd failure modes, observability, and an on-premises migration to Kubernetes. ### Source excerpt Check out the Gremlin-curated unofficial track of reliability talks at KubeCon North America 2025. ## Why I Ditched the Cloud for On-Prem Infrastructure DevFeed: [Why I Ditched the Cloud for On-Prem Infrastructure]() Original publisher: [Read original article]() Author: Shivam Anand Published: 2025-10-25T16:30:21Z Content type: article Language: en Sources: [Shivam Anand - DevOps & Cloud Engineering Blog]() Topics: [on-prem](), [cloud-infrastructure](), [migration](), [networking](), [BGP](), [Firewall](), [Hardware](), [Artificial Intelligence]() Tags: [ai](), [ai-workloads-on-prem](), [bgp](), [bgp-and-asn-networking](), [cloud-connectivity](), [cloud-cost-optimisation-2025](), [cloud-infrastructure](), [cloud-infrastructure-savings](), [cloud-repatriation-case-study](), [cloud-to-on-prem-migration](), [enterprise-data-centre-setup](), [firewall](), [hardware](), [it-infrastructure-2025](), [managed-cloud-vs-on-prem](), [migration](), [multi-site-high-availability](), [network-cost-reduction](), [networking](), [nixi-ip-block-india](), [nvme](), [on-prem](), [on-prem-infrastructure-benefits](), [open-source-monitoring-stack](), [proxmox-vs-vmware-migration](), [subhash-choudhary-dukaan-cto]() ### AI overview The article recounts a migration from cloud infrastructure to on-premise data centres after cloud cost-optimization efforts produced minimal results. It describes the networking, hardware, and software setup, and reports that the monthly cloud bill fell from INR 40 lakhs to INR 3 lakhs after deployment. ### Source excerpt Explore why migrating from costly cloud infrastructure to efficient on-premise data centres can drastically reduce monthly expenses and complexity, especially for data-heavy AI workloads. Learn about real-world experiences, technical setups, and best practices for on-prem cloud repatriation in 2025. ## Introducing Mistral AI Studio. DevFeed: [Introducing Mistral AI Studio.]() Original publisher: [Read original article]() Published: 2025-10-24T12:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [AI Platform](), [Artificial Intelligence](), [MLOps](), [Security](), [DevOps](), [data](), [Large Language Model](), [Deployment](), [Prompt Engineering](), [on-prem](), [VPC]() Tags: [agents](), [ai](), [ai-adoption](), [ai-assistants](), [ai-platform](), [ai-studio](), [autonomous-agents](), [compliance](), [infrastructure](), [multimodal-ai](), [on-prem](), [privacy](), [provenance](), [vpc](), [workflows]() ### AI overview Mistral introduces AI Studio, an enterprise AI platform for customizing, fine-tuning, evaluating, governing, and deploying AI assistants, autonomous agents, and multimodal AI with open models. It addresses the gap between AI prototypes and production by providing evaluation, feedback and dataset workflows, provenance and versioning, governance, and flexible deployment across hybrid, VPC, and on-prem infrastructure. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Expanding Chainguard VMs: Zero-CVE Application & Base Virtual Machine Images for Cloud and On-Prem DevFeed: [Expanding Chainguard VMs: Zero-CVE Application & Base Virtual Machine Images for Cloud and On-Prem]() Original publisher: [Read original article]() Published: 2025-09-03T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard vms](), [Cloud](), [on-prem](), [chainguard os](), [Java](), [Jenkins](), [nginx](), [Python]() Tags: [chainguard-vms](), [cloud](), [cloud-virtual-machines](), [cve](), [java](), [jenkins](), [nginx](), [on-prem](), [os](), [provenance](), [python](), [rebuilds](), [sbom](), [secure-by-default](), [security-vm-images](), [virtual-machine-images](), [zero-cves]() ### AI overview Chainguard VMs is expanding with zero-CVE application images for Jenkins, Nginx, and Squid Proxy, plus base images for Chainguard OS, Java, and Python. The virtual machine images support cloud and on-premises deployments and are continuously rebuilt from source with automated updates, CVE remediation, and SBOM-driven provenance attestations. ### Source excerpt Chainguard VMs is expanding with new Application and Base VM Images -- giving teams a secure, zero-CVE foundation to build and innovate faster. ## Le Chat. Custom MCP connectors. Memories. DevFeed: [Le Chat. Custom MCP connectors. Memories.]() Original publisher: [Read original article]() Published: 2025-09-02T04:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [Model Context Protocol](), [Artificial Intelligence](), [AI Platform](), [Automation](), [Deployment](), [Cloud](), [on-prem](), [databricks](), [GitHub](), [Notion](), [Prisma](), [pull-requests]() Tags: [agents](), [ai](), [ai-assistants](), [automation](), [cloud](), [databricks](), [docs](), [github](), [integrations](), [mcp](), [on-prem](), [platform](), [prisma](), [pull-requests]() ### AI overview Mistral introduces custom MCP connectors and Memories in Le Chat. The release adds more than 20 secure connectors for enterprise data, productivity, development, automation, and commerce workflows, with options to search, summarize, and act in connected tools. Memories provide personalized responses, user control over stored information, and import from ChatGPT. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Running our Docker registry on-prem with Harbor DevFeed: [Running our Docker registry on-prem with Harbor]() Original publisher: [Read original article]() Author: Farah Schüller Published: 2025-08-24T17:00:00Z Content type: tutorial Language: en Sources: [37signals Dev]() Topics: [docker registry](), [on-prem](), [Docker](), [Docker Compose](), [Deployment]() Tags: [deployment](), [docker](), [docker-compose](), [docker-registry](), [harbor](), [on-prem](), [open-source](), [registry]() ### AI overview The article describes 37signals' move from external container registries, including Docker Hub and Amazon Elastic Container Registry, to an on-premises Harbor registry. It explains cost, performance, security, governance, and independence concerns, then outlines a single-server Docker Compose deployment using the organization's own S3 storage and plans for replicated sites. ### Source excerpt On hosting images without the price tag. ## Integrating Xandr's Real-Time Data Provider On-Premises: Infrastructure Requirements and Engineering Challenges DevFeed: [Integrating Xandr's Real-Time Data Provider On-Premises: Infrastructure Requirements and Engineering Challenges]() Original publisher: [Read original article]() Author: Guy Watson Published: 2025-04-22T22:57:37Z Content type: article Language: en Sources: [GumGum]() Topics: [real-time](), [Requirements](), [on-prem](), [Latency](), [Low Latency](), [Hardware](), [data](), [digital]() Tags: [ads](), [advertising](), [challenges](), [clients](), [data-processing](), [decision-making](), [hardware](), [infrastructure](), [latency](), [low-latency](), [metric](), [on-prem](), [performance](), [platform](), [real-time]() ### AI overview The article describes GumGum's experience integrating Xandr's Real-Time Data Provider on-premises, focusing on infrastructure requirements, scaling challenges, latency, traffic volume, and the decision to own hardware. ### Source excerpt How We Slashed Our Infrastructure Costs by 80% While Successfully Integrating Xandr's Real-Time Ad Platform!Introduction In the fast-paced world of digital advertising, maximizing return on investment (ROI) for ad spend is paramount. Buyers need to make data-driven decisions in real time to ensure their ads reach the right audience at the right moment. For us, integrating Xandr's Real-Time Data Provider (RTDP) was essential to providing the real-time optimization signals that would help our clients maximize their ROI by boosting Attention Time -- the key metric driving the effectiveness of programmatic ad campaigns. Our story is one of navigating through technical hurdles, experimenting with new infrastructure, and finding creative solutions to challenging engineering problems. As we embarked on integrating Xandr Real Time Data Provider, the path was far from straightforward. From vendor selection to scaling issues, and even the decision to own our hardware, every step had its complications. Yet, each obstacle presented an opportunity for innovation and refinement. In this article, we'll take you through the key challenges we faced, how we overcame them, and the lessons we learned from integrating Xandr on-prem. Whether you're considering a similar project or are simply curious about the behind-the-scenes of large-scale advertising tech deployments, our experience might just offer the insights you need. Xandr Integration and Its Requirements Xandr, a leading programmatic advertising platform, provides powerful tools to streamline and optimize ad delivery, targeting, and performance tracking. For us, integrating with Xandr RTDP meant integrating with the real time optimisation Xandr platform. Xandr's system, built to handle vast amounts of data in real time, comes with certain infrastructure demands. These include: Volume: The platform must be capable of handling massive amounts of traffic at any given time. For example, billions of ad impressions are processed daily, ## How Vapi Uses Neon to Power the Next Generation of Voice Agents DevFeed: [How Vapi Uses Neon to Power the Next Generation of Voice Agents]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2025-02-21T18:02:38Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [voice ai](), [API](), [Language models](), [WebRTC](), [Streaming](), [Serverless](), [on-prem](), [Software as a service]() Tags: [ai](), [ai-agents](), [api](), [case-studies](), [language-models](), [on-prem](), [routing](), [saas](), [serverless](), [streaming](), [voice](), [voice-ai](), [webrtc]() ### AI overview The article presents Vapi as a platform for building voice AI applications and agents with a small number of API calls. It describes integrations with language models, text-to-speech engines, telephony infrastructure, function calling, multilingual support, WebRTC streaming, scalable concurrent-call infrastructure, and on-premises deployment. Example uses include customer support, onboarding, sales training, recruiting, health assistance, storytelling, and executive coaching. ### Source excerpt The time is now: you can build voice AI apps that converse with humans using just a few API calls. With Vapi, you can create voice AI agents that don't just understand users but also respond naturally and dynamically, just like a human. Check this recording out, where James sched... ## The all new le Chat: Your AI assistant for life and work DevFeed: [The all new le Chat: Your AI assistant for life and work]() Original publisher: [Read original article]() Published: 2025-02-06T16:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [AI Platform](), [Inference](), [Latency](), [on-prem](), [Software as a service](), [Code](), [VPC](), [Android](), [iOS]() Tags: [ai](), [ai-assistants](), [android](), [code](), [inference](), [ios](), [latency](), [models](), [on-prem](), [saas](), [vpc]() ### AI overview Mistral introduces a new version of le Chat, an AI assistant for personal and professional tasks. It offers web-grounded answers, document and image understanding, OCR, sandboxed code execution, scientific analysis, simulations, visualizations, and image generation. The announcement also covers free, Pro, Team, and Enterprise tiers, including deployment on SaaS, on-prem, and VPC infrastructure. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. [Next page]()