Operations | Monitoring | ITSM | DevOps | Cloud

How SD-WAN Works and What to Monitor at Every Branch

Why does one branch report unusable voice and video every afternoon while the WAN dashboard at head office shows every site green? Most network teams have fielded that escalation. The device responds, the tunnel status reads up, and application performance is still unacceptable. SD-WAN was built for the distance between a connection that responds and an application that performs.

Unified Endpoint Management: Capabilities, Rollout, and Metrics

How much of your device inventory could you verify today without opening more than one console? Most IT teams can report what they purchased. Far fewer can say which of those machines are patched, encrypted, running approved software, and still checking in, because that answer spans several systems. Unified endpoint management consolidates those systems into one. Most IT teams inherit that split one platform at a time.

Selector named in the 2026 Gartner Reference Architecture Brief: Next-Generation Enterprise Networks

A reference architecture is a set of design decisions made explicit, and the interesting parts are usually the consequences the authors chose to name. Data center, campus, WAN, cloud, and edge each run on their own platforms, often from different vendors, and the architecture takes that fragmentation as its starting point rather than a problem to wish away.

Megaport Signs Open Weights and American AI Leadership Letter

Megaport has signed the Open Weights and American AI Leadership letter, supporting open-weight AI, customer choice, and the infrastructure that powers AI innovation. Artificial intelligence is reshaping how organizations build, deploy, and scale digital infrastructure. As AI adoption accelerates, the conversation is evolving beyond model performance alone to the broader ecosystem needed to support long-term innovation.

Software is a team sport. Most AI tools forgot that.

Every AI coding tool ships the same promise: your developers, faster. Autocomplete in the IDE, agents in the terminal, a working prototype before lunch. And it delivers, at least for the person holding the keyboard. The problem is that most of what it takes to ship software was never a solo activity, and that is the part the market keeps skipping.

Azure cost calculator: how to estimate Azure spend before the bill does it for you

An Azure cost calculator is a tool that estimates your monthly Microsoft Azure bill before you deploy anything. Microsoft's official Azure pricing calculator covers 600+ services and produces free, exportable estimates. It is excellent at telling you what Azure charges and terrible at telling you what your teams will consume. This guide covers how to use it, what real workloads cost in 2026, and why estimates and invoices keep breaking up.

What is FP&A? Financial planning and analysis in the AI spend era

FP&A stands for financial planning and analysis. It is the corporate finance function responsible for budgeting, forecasting, variance analysis, and decision support. If you're asking what is FP&A in practice: FP&A teams build the annual operating plan, project revenue and expenses, explain gaps between plan and actuals, and give leadership the numbers behind strategic decisions. Accounting reports what happened. FP&A models what happens next.

LLM cost optimization: 7 strategies to cut inference spend

LLM cost optimization is the practice of cutting what you spend on large language models, mostly inference, without losing the quality that makes the AI worth running. The biggest levers are routing requests to cheaper models, caching repeated tokens, batching anything that can wait, trimming prompts, right-sizing models, cutting calls you do not need, and putting one gateway and cost view in front of all of it.

Prioritize security findings with the Datadog Runtime Prioritization Engine

If you run a cloud security program, two questions follow almost every security finding: Who owns this? And how important is it? Many security tools answer those questions with static metadata such as owner tags, business criticality labels, and manually maintained inventories of critical assets, known as crown jewels. But cloud environments aren’t static. Teams reorganize, services change hands, and dependencies evolve.

Comprehensive Postgres observability: is your database healthy, and will it stay that way?

A Postgres database is one of the most complex pieces of software you run. It stores your data durably so a power loss doesn't lose a committed transaction. It hands out real transactions while thousands of queries fight over the same rows. It stays available when a node dies, lets you rewind to a point in time after someone drops the wrong table, and constantly tunes itself with background jobs. Two questions matter about all of that machinery: is it healthy right now, and will it stay healthy?