Operations | Monitoring | ITSM | DevOps | Cloud

Can you see what your sales org did with $206,000 of AI last month?

Activity categorization classifies every AI request by the work it performs, such as meeting prep, code review or account research, then assigns that spend to the team, person, model and provider behind it. It turns a provider invoice that shows only totals by model and API key into a breakdown finance can allocate, charge back and budget from. CloudZero AI Signals does this as the spend happens.

Amazon told its engineers to stop chasing tokens

Amazon told its own engineers this week to stop tokenmaxxing. The memo circulated internally in September and Amazon published it on Tuesday, Oct. 27. Tokenmaxxing is the practice of using AI as heavily as possible with little regard for whether the output justifies the spend. The memo calls token usage, adoption rates, and code-change counts “deeply flawed” ways to measure a team.

PHP2Shell explained: blocked 22 hours before the patch existed

PHP2Shell let attackers run code on PHP servers without logging in, and updating a CMS or its plugins didn't help. Only a new version of PHP fixed it. In this explainer, we break down how the two flaws work (CVE-2026-109618 and CVE-2026-109619, both rated 9.2 critical) and what Upsun Cloud did between PHP's one-day warning and the fix. We get into.

Why haven't UK organizations moved off the hyperscalers?

Two-thirds of UK IT leaders say they would switch cloud providers to take back control. Fifteen percent have actually done it. That is the headline from Civo's Digital Sovereignty Revolution 2026, a survey of 1,000 UK IT leaders. The intent is loud and getting louder. The movement is almost silent. It would be easy to call that apathy. It is not. UK organizations are staying on hyperscale infrastructure they no longer fully trust for four very specific reasons. Three of them are structural.

Shipped: Fit more of your cost data on one screen

Most cost questions get answered the same way. You read a chart and the table under it together. Maybe you spot a jump on the chart, and then you go looking in the table for the service, account, or team that caused it. When you can see more of that table while the chart is still in view, the whole review goes faster. You’re also a lot less likely to lose your place halfway through.

Google Cloud outage on October 20, 2026: a europe-west2 power event and 171 possibly affected services

A power event in Google’s europe-west2-b (London) zone triggered a wide Google Cloud outage on October 20, 2026, taking down dozens of services including Cloud SQL, BigQuery, Compute Engine, and Kubernetes for customers across the UK and beyond. StatusGator sent an Early Warning Signal at 07:02 UTC, 1 hour and 35 minutes before Google Cloud publicly acknowledged the incident at 08:37 UTC. The ripple effect was wide: StatusGator tracked 171 other services showing signs of impact during the outage.

CloudZero in 3 Minutes: See What Your AI and Cloud Spend Actually Buys

Cloud and AI spend in one place, tied to the teams, products, and customers behind it. Your bill tells you what you spent. CloudZero tells you why, and whether it paid off. This quick tour shows how CloudZero pulls cost from AWS, Azure, GCP, Kubernetes, Snowflake, OpenAI, Anthropic, and more, then allocates it to what your business actually cares about. No perfect tagging required.

What is a Sovereign Cloud?

A sovereign cloud is a cloud computing environment designed to keep data, infrastructure, and operations under the control of a specific country or jurisdiction. It helps organizations maintain control over who can access their data and how it is managed. Depending on the provider and service, this may include storing data within national borders, using locally governed infrastructure, and ensuring that operations comply with regional regulations.

AI Signals Demo: See Exactly What Your AI Spend Is Buying | CloudZero

See who’s spending on AI, on which models, for what work, and what you got for it. Most companies can tell you what they spent on AI last month. Very few can tell you what it bought. In this demo, we show how AI Signals captures AI usage as it happens and ties every dollar to the person, team, and work behind it, across every model and provider.

Shipped: Log and track Insights from your AI assistant

Insights are how your team tracks interesting observations, analysis and optimization work in CloudZero. Each one records the opportunity and its status, so the work gets sized, assigned, and closed out. Teams now do a lot of this work in an AI assistant. For example, you can ask Claude which instances sat idle last month, and it will tell you. But if you have to switch to CloudZero and fill out a form to log that finding, some findings won’t get logged.

AI cost per customer: measure it and protect your margins

AI cost per customer is each customer's attributed share of your AI spend: API tokens, inference infrastructure, and per-seat AI tools, measured against that customer's revenue. It matters because AI usage is wildly uneven across customers, so healthy blended margins can hide individual customers you serve at a loss. It's cost to serve, not acquisition cost. Cost per customer is the metric CloudZero was built around.

AI cost per query: how to calculate and optimize it

AI cost per query is your total AI serving spend divided by the number of queries served over the same period. It combines model API charges (input and output tokens), retrieval and orchestration overhead, and shared infrastructure. Most production text queries cost between $0.0005 and $0.02. It's the base unit of AI unit economics, and the number finance needs before any ROI claim holds up.

Shipped: Azure cost totals that match your invoice

Azure cost exports sometimes fail with a server error and then succeed on the next try. Some reports cover only part of the month. Large tenants get rate-limited when they request a lot of data at once. Each of these things can leave a gap in your Azure history. You normally find the gap when finance compares the Azure total in CloudZero to the Microsoft invoice.

Shipped: AI dimensions, organized around how your team works

AI Signals adds a standard set of AI dimensions to your dimension picker, plus custom dimensions built from your own data. Not every team needs all of them. The ones you don’t use make the ones you want harder to find. Cleaning that up used to require an engineer to run a script every time an account team needed a change. We identified the bottleneck and automated it, so account teams can manage dimensions themselves and engineering stays focused on product work.

Amazon WorkSpaces vs Amazon WorkSpaces Applications: What's the Difference and Which AWS Solution Should You Choose?

If you’re researching AWS desktop virtualization and application delivery services, it is likely that you have come across Amazon WorkSpaces and Amazon WorkSpaces Applications. While the names sound similar, these two AWS services address different requirements. Amazon WorkSpaces provides a complete cloud desktop, while Amazon WorkSpaces Applications focuses on securely delivering individual applications through AWS-managed application streaming.

7 weak points in your delivery model to fix before your next audit or outage

An auditor asking for evidence of your deployment process and an incident review asking what went wrong are asking about the same things. The difference is timing. One catches the weak point before it costs you anything. The other catches it in the middle of an outage, with a customer on the phone.

Shipped: See which billing connections stopped sending data, and why

Every number in CloudZero comes from a billing connection. When a credential expires or a permission changes, that connection stops sending data, and your reports show less spend than you actually have. If you catch it early, you can fix the connection before month-end close. You also need to know what broke. For Azure and Snowflake connections, CloudZero now tells you which check failed, so you can send the fix to the right person without opening a support ticket.

How much does it cost to train an AI model? Frontier figures, graded [2026]

Training an AI model costs anywhere from a few hundred dollars to several hundred million, depending entirely on what “train” means. Fine-tuning an existing model: tens to thousands of dollars. Pretraining a small model from scratch: tens of thousands to millions. A frontier run in 2026: hundreds of millions, with Epoch AI projecting the largest runs will pass $1 billion by 2027.

5 Questions to Ask Before the Next Cloud Outage

On October 20, a power failure in one of Google Cloud’s three London zones disrupted Revolut, Lloyds, HSBC and others for hours. One zone failed, errors spread region-wide, and Google warned customers they might not be able to create new workloads to fail over to. Most teams find out how resilient they are during an outage, not before one. These five questions let you find out first.

Strengthening the Security of Qovery's Access to Your AWS Account

Qovery now sends your organization ID as the sts:ExternalId when it assumes the IAM role in your AWS account, and the role's trust policy only accepts it on behalf of your organization. Your role is already updated. If you manage it with the qovery-role-creation CloudFormation stack, update the stack to the latest template. Romain is a staff engineer at Qovery working on the control plane and AI integrations. He writes about distributed systems, incident response, and MCP-based tooling.

Adding friction to your AI spend isn't saving money. It's costing you growth.

Enterprises are putting hard ceilings on AI spend. Accenture now centrally funds $29 a month per person for Claude and charges anything above that to a project budget code. Uber used up its 2026 AI budget in four months and restricted access. A cap stops waste and useful work at the same rate. The alternative is seeing AI cost by team, by project and by outcome, then funding what returns. Companies spent the past year telling employees to use more AI. Now a lot of them are putting a lid on it.

Shipped: Slice AI spend eight ways in AI Explorer

You can already break down cloud spend by team, product, or customer in Explorer. But AI spend has been harder to decompose. Which models is each team running? What activity is driving the cost growth you saw this month? Before AI Explorer, answering those questions meant exporting raw event data and building your own breakdowns, or waiting days for reconciled billing to land in Explorer.

Before you build your own internal platform, run these numbers

"We'll just build it ourselves" is usually said with confidence and a rough headcount estimate. Six months later, the team that was supposed to spend two sprints on it is still maintaining it a year in, and the platform that was meant to save engineering time has become one of the things engineering time goes to. Build isn't automatically the wrong call. Sometimes it's the right one.

How Upsun handled PHP2Shell: protection in place 22 hours before the patch

On 21 October 2026, the team behind PHP released an emergency fix for two critical security flaws. PHP is the programming language behind a large share of the web, including WordPress, Drupal and many online stores. Researchers at Wiz named the flaws PHP2Shell. An attacker could use them to take control of a website without ever logging in. Updating a website, its CMS, or its plugins doesn't help. The only real fix is (and was) a new version of PHP itself.

The Upsun Performance Agent: a diagnosis instead of a call graph

The data to explain a slow endpoint is usually already sitting in your project. Blackfire has the profile. Continuous profiling has the flame graph. The traffic numbers show which route matters. The code is in git. What takes the afternoon is putting those four things next to each other and working out which function is responsible. That afternoon is what the Upsun Performance Agent is for.

The Best Cloud Providers for Data Sovereignty and Residency in 2026

Data residency is about where data is stored and processed. Data sovereignty is about whose laws and operators can reach it. The strongest setups put regulated data in in-country regions or with sovereign providers, hold their own encryption keys, enforce placement with policy, and keep the ability to move. No single provider covers every country, so most teams combine two or three. Disclosure: Control Plane publishes this guide and is one of the options listed. This guide is not legal advice.

The Best Cloud Migration Tools and Services in 2026

The best cloud migration tool depends on what you are moving, from where, to where, and whether you can afford downtime. Native tools (AWS Transform MGN, Azure Migrate, and Google Cloud Migration Center) move servers into their own cloud. Replication tools move VMs and data with minimal downtime. Kubernetes tools move containerized apps. Platforms like Control Plane let you run the same workloads in the old and new environment at once, so you cut over gradually.

AWS Interconnect Is Coming to Megaport

AWS Interconnect is coming to Megaport, giving enterprises and AI infrastructure providers another way to scale private AWS connectivity with automation, encryption, and built-in resilience. Megaport customers just got a new AWS connectivity option, and it fits perfectly with how we’ve always believed infrastructure should work: direct, automated, resilient, and easier to operate.

Capping the AI free-for-all? Know what each dollar returns first.

Most companies gave everyone AI access and thought they’d sort out the costs later. Engineering, support, product, marketing, and finance all got licenses to the best models. The early results looked good, and for a while it didn’t matter there wasn’t an explicit business case. Let’s call the first half of 2026 the free-for-all period for many companies. AI adoption spread across the org fast, people and teams loved it, and then the invoices started coming in.

Shipped: Create a Jira ticket from a cost spike, with status synced both ways

When Monitors flags a cost spike, the fix usually belongs to an engineering team, and that team might track their work in Jira. Now you can hand the incident to them from CloudZero in one click. The Jira ticket and the CloudZero incident stay in step. When the engineer resolves the ticket, the incident resolves too. You track the work in one place, and the status you see in CloudZero matches what the team is doing.
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Monitoring CloudNativePG: What Changes When the Operator Owns Your Logs

Postgres operators like CloudNativePG (CNPG) have lowered the threshold for running highly available Postgres clusters on Kubernetes. Failover, replicas, backups, and rolling upgrades are handled by the operator, along with helpful niceties like automatic configuration of essential extensions. A few changes to a YAML manifest can provide a lot of value very quickly.

Shipped: Views now save your full Explorer state

Many CloudZero users ask the same few questions. What did my team spend this month? How is our AI spend trending over the last 30 days? What does one MCP tool call cost? Now you can save the exact Explorer setup behind each answer as a View and get back to it instantly. It’s not just filters and grouping anymore, but the full picture. You can share a link with a teammate or finance and they see the same View, filtered to the spend their role allows.

Shipped: Break down AI spend by your projects and teams, within minutes

Your company talks about AI spend in its own terms, like projects, teams, and products. Now AI Signals can show AI spend in those terms, too. Write a CostFormation rule in CloudZero that groups AI spend by categories you care about, and the new breakdown appears in AI Signals the same day you decide to track it. There’s no waiting for a full processing cycle.

Building a resource optimization agent with Kubex: Patterns

The rise of large-language models (LLMs) that can understand and create text, and reason about problems has opened the door for software agents that use the reasoning power of LLMs to interact with the wider world and solve problems. At Kubex, our bread and butter is compute and GPU resource optimization, so it’s natural for us to ask how agents could help our customers more effectively optimize their resources.

How to Let AI Agents Operate Production Infrastructure Safely

Yes, it can be safe to let an AI agent change production infrastructure, if the agent works through the same controls you would demand of a new engineer: scoped permissions, short-lived credentials, a tamper-proof audit trail, changes that roll out gradually behind health checks, and a fast rollback. The infrastructure also has to be built so that one bad change cannot take everything down.

Cloud Outage Tracker: Major AWS, Azure, Google Cloud, and Cloudflare Outages Since 2020

Last updated: October 21, 2026. This tracker lists every major AWS, Microsoft Azure, Google Cloud, and Cloudflare outage since January 2020, with each provider’s own stated duration, root cause, and source. It covers 62 major incidents. The median outage lasted 6h 12m, and the most common cause was a configuration or deployment change (20 of 62, 32%), not hardware failure. We add new incidents as providers publish their post-incident reports. Control Plane publishes this tracker.

GCP Out of Capacity? How to Offload Workloads to AWS Without Running a Second Platform

Google says it is compute constrained, and GKE teams now hit ZONE_RESOURCE_POOL_EXHAUSTED on CPU machine families, not only GPUs. Here is what the error means, what Google recommends, what AWS can and cannot offer, and how to offload workloads from GCP to AWS with one platform instead of two. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

Shipped: See who changed a cost monitor, and when

A cost monitor is only useful if people trust its settings. When a threshold moves or a monitor goes quiet, the team needs to know who made the change and when. Every monitor in CloudZero keeps that record. You can check it yourself in a few seconds, confirm a change was intended, and put a setting back if it wasn’t.

Enterprise AI ROI: proving returns at scale

Enterprise AI ROI is the return on AI investment measured across a portfolio: dozens of initiatives, multiple business units, shared platforms, and one board asking for the total. It fails differently than single-project ROI, usually because baselines and unit costs were never established, and it's proven with per-initiative denominators rolled into portfolio-level reporting.

The hidden cost of environment drift

Two teams, same company, same platform, same quarter. Team A ships to production three times a day. Their environments are defined in code, provisioned the same way every time, and a new engineer can get a working setup before lunch. Team B ships once every two weeks, if the release window holds. Their staging environment has diverged from production in ways nobody fully remembers approving. Getting a new engineer set up takes a week of Slack messages to whoever still remembers how it was done last time.

Best Multi-Cloud and Multi-Region Failover Solutions (2026)

The best failover solution keeps your application serving users from another location without a manual cutover. The strongest approach is active-active across two or more locations, where every location already serves live traffic, rather than restoring from backups after an outage.

How to Ensure Reliability of Data in the Cloud

The reliability of data is crucial when managing files in the cloud, especially for businesses, enterprises, and even the data reliability of your own personal files. In this article, we’ll cover everything about data reliability: what it is, why it’s important, assessing the reliability of data, and cloud storage solutions to keep your data protected, accurate, and complete.

Cloud Monitoring and Incident Response: A Guide to AWS, Azure, and Google Cloud

Effective cloud monitoring and incident response starts with understanding service health, collecting useful diagnostic data, and establishing a clear process for investigating and resolving problems. Whether you use AWS, Azure, Google Cloud, or several providers, the goal is to understand how your systems are performing and take action when reliability is at risk. A dashboard showing healthy virtual machines does not necessarily mean customers can complete a transaction.

ChatGPT Enterprise pricing in 2026: seat costs, minimums, and the new token rate card

ChatGPT Enterprise has no public list price. Reported 2026 contracts average about $60 per user per month within a $45 to $75 range, with 150-seat minimums and annual prepayment, putting the floor near $108,000 a year. Newer eligible agreements add usage-based billing under OpenAI's published token rate card.

Cloud Log Monitoring & Root Cause Analysis | Logs for Lunch

Cloud log monitoring in LogicMonitor connects a service alert to the log event behind it. This Logs for Lunch July demo is for ITOps, CloudOps, and SRE teams. Tom Chavez hosts Ismath Mohideen (Product Marketing) and Nick Doane (Senior Sales Engineer). They cover cloud observability and multi-cloud monitoring across AWS, Azure, GCP, and OCI in LM Envision, using cloud provider APIs with no extra collectors.

AI wrote the bug. You need production data to reproduce it

A pull request lands with a function an AI assistant generated. It reads cleanly, passes the existing tests, and gets merged. Three weeks later it breaks, but only for accounts with a specific billing history. Nobody wrote a test for that condition because nobody was thinking about it when the code went in. Now someone has to debug logic they didn't reason through, against a failure they can't picture without the exact data that triggered it. Debugging someone else's code has always worked this way.

The 10 Best MCP Servers for DevOps and Cloud Infrastructure in 2026

The best MCP servers for DevOps depends on what you want an AI assistant to do: read code and pipelines, plan infrastructure as code, call one cloud’s APIs, observe production, or deploy and operate workloads. The safest setups combine scoped permissions, a read-only mode for investigation, confirmation before destructive actions, and an audit trail of every change.

Cloud Cost Optimization in 2026: 10 Levers That Cut Your Compute Bill

Most cloud compute waste comes from paying for capacity your workloads do not use: over-provisioned requests, idle nodes, standby capacity, and per-service add-ons. The biggest savings come from changing how capacity is reserved and packed, not only from discounts or dashboards.
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The hidden cost of IT sprawl: how fragmented infrastructure drains business performance

Most IT estates grow through years of individual decisions instead of a single plan. A cloud account gets opened for one project. A data centre contract stays in place because switching is a migration project the business keeps deferring. Those choices get made workload by workload, and the estate that results rarely holds together as one plan.

How CFOs can control AI spend and prove AI's value

Neil Sawyer, Senior Vice President and Managing Director for HP Europe, Middle East and Africa, recently published “Scaling AI Without Losing Control” in Forbes Business Council. His starting point will be familiar to any finance leader. AI pilots looked affordable on pay-per-token and pay-per-query pricing, and that same pricing can get far more expensive at enterprise scale. Gartner forecasts worldwide AI spending will reach $2.7 trillion in 2026, up 49.5%.

AI infrastructure costs in 2026: what the stack is, what each layer costs, and how much to commit

AI infrastructure costs span four buying tiers in 2026: API-only teams spend hundreds to tens of thousands per month on tokens, GPU renters typically pay $2 to $10 per GPU-hour by chip class, committed-capacity buyers sign six-to-eight-figure reservation contracts, and owners spend millions per rack. The stack has six layers, and most budgets underestimate everything below the GPU line.

How to Avoid Cloud Vendor Lock-In: A Practical Guide for 2026

You avoid cloud vendor lock-in by keeping the parts that are expensive to move portable: the compute runtime, identity, data, networking, and deployment tooling. You accept proprietary services only where their value clearly exceeds the exit cost, so that leaving or adding a provider is a configuration change rather than a rewrite.

The Cheapest Personal Cloud Storage Offers 2026

Finding the cheapest personal cloud storage doesn’t mean compromising on privacy or security. In this guide, we compare the best and most affordable cloud storage options for personal use in 2026, covering free plans, monthly subscriptions, storage capacity, security features, and lifetime deals to help you find the best value for your needs.

Best open-source LLMs in 2026: ranked by cost to run

The best open source LLMs by family: DeepSeek V4 for frontier reasoning under MIT, Qwen3.8 and Gemma 4 for clean Apache 2.0 licensing, GLM-5.3 for coding, Kimi K3 for long context, Llama 4 for single-GPU scale. The real ranking depends on your deployment lane and token volume, and this guide prices all three lanes. At CloudZero, Bill Buckley, our SVP of Engineering, gives every engineer a $5,000 monthly AI token budget and published the math behind it.

Shipped: Find the GCP buckets with high cross-location read charges

You can now see which of your Multi-Region Cloud Storage buckets have cross-location read charges worth acting on, and start with the ones where a cache would save the most. It runs automatically for every organization with GCP Cloud Storage, so there’s nothing to set up. Google bills network data transfer when a Google Cloud service in one location pulls data from a Multi-Region bucket in another.

What Infrastructure Do You Need to Run an Inference Layer? A Production Guide

A production inference layer needs GPU compute sized to the model, a serving runtime such as vLLM or SGLang, orchestration that autoscales on queue depth, one endpoint with health-aware routing and failover, model weights cached near the GPUs, and observability, security, and cost controls.

How to Build Active-Active High Availability Across Regions and Clouds

Active-active means two or more locations serve live traffic at the same time, with the application running in each and its data replicated across them, so no location sits idle waiting for a failover. You build it in five layers, and data is the hardest.

Is VPA "Good Enough"? How Kubex Delivers Enterprise-Ready Workload Optimization

Kubernetes workload rightsizing is one of those tasks everyone agrees is important and almost nobody enjoys doing by hand. Set requests too high and you pay for capacity that sits idle; set them too low and you invite CPU throttling, out-of-memory kills, and late-night pages. It’s no surprise, then, that many teams reach for the Vertical Pod Autoscaler (VPA) as their first step toward automated rightsizing.

AWS saw the value of expanding support for Google Cloud #aiven #opensource #shorts

Tony’s first RFC landed in AWS’s OpenSearch migration project, a few people inside AWS asked, “Who’s this guy?” The first AWS maintainer he spoke with saw the value add Google Cloud support, and next, build the Terraform solution their customers want. AWS got what its customers asked for. Aiven customers benefited with being able to choose and so did anyone running OpenSearch on Google Cloud, even self-managed or with another vendor.

From bug report to verified fix in a single workflow

Most debugging content shows one feature in isolation. This walkthrough covers the whole path. The bug: a discount code stopped applying for customers on annual billing plans. It passed every existing test, worked for monthly accounts, and failed only for a subset of real annual customers. Nobody had reproduced it outside production. Here's how it went from ticket to verified fix.

Honeycomb Adds Another AWS Competency: Resilience Technology

We're excited to share that Honeycomb has achieved yet another Amazon Web Services (AWS) Competency—the Resilience Technology Competency—which recognizes our ability to help customers detect, investigate, and recover from failures in production systems running on AWS. While receiving the competency designation is a fantastic recognition of the capabilities of the Honeycomb platform (we also received one in Financial Services earlier this year), it's ultimately not the point.

Best Cloud Storage UK: Affordable, Secure, and Private Options

If you’re looking for alternatives to US tech giants spying on your data and need a secure, private cloud storage solution for your files, then these best cloud storage for UK customers offer the best alternative to major cloud companies, with the added advantage of extra features, and no more subscriptions with lifetime options.

How to Add a Neocloud to AWS or GCP Without Running a Second Stack

There is no single best neocloud for AWS or GCP teams. The right one depends on the workload. Bursty fine-tuning and batch jobs reward flexible billing and low hourly rates. Always-on inference and agents reward capacity you can keep plus a second place to run, and data-heavy pipelines reward cheap data movement.

Shipped: Drill into your cloud bill up to eight levels deep

Most cost questions have more than one layer. Which team spent the most, which services drove it, and which accounts were behind those services? Previously, following that path meant changing dimensions, adjusting filters, and re-running the query at every step. Multi-dimensional drill-down has been one of the most-requested features in Explorer. The team shipped two-level group-by earlier this year, collected feedback, and kept going. Now the path goes eight levels deep.

Shipped: Period-by-period costs in Explorer, no spreadsheet required

Finance teams reconcile cloud spend period by period. They want each month, week, or day in its own column, so they can scan across a row and see how a service or team changed over time. Explorer’s Aggregate view already answers “what changed and by how much.” But when the question is “what did we spend in June, July, and August, line by line,” that summary needs to become a grid.

Kimi API pricing in 2026: the K3 ladder, the August retirement, and what Moonshot really charges

Kimi API pricing, per million tokens: the flagship Kimi K3 costs $3 input and $15 output with a $0.30 cache-hit rate and 1M context. Kimi K2.6 and the coding-tuned K2.7 Code both run $0.95/$4.00 on 256K context. Kimi K2.5 and the legacy V1 family were retired August 31, 2026.

Spin up a full-stack preview environment in seconds, zero config

A pull request goes up. The reviewer wants to see it running, not just read the diff, so someone pings the team: can I get an environment for this? Depending on who's around, that could take an hour. It could take until tomorrow. By the time the environment exists, the reviewer has moved on to something else, and getting their attention back is its own task. What if the preview URL was already sitting in the PR before anyone had to ask?

Cloud Storage Costs UK: Everything You Need to Know

If you’re looking for a comparison of cloud storage costs in the UK, you’ve come to the right place! We cover how much does cloud storage cost for the most popular cloud storage providers, including Internxt, Google, and more. In this article, you can compare monthly, annual, and lifetime plans from different providers, storage amounts, and additional features so you get the best cloud storage at the best prices. Prices are based on current conversion rates at the time of writing.

AI at scale: are your foundations ready?

What does it take to move from AI experimentation to real-world adoption, and where does sovereign AI fit into the picture? In Episode 2 of Season 2 of Perspectives from the Edge, Andrew Dutton,Enterprise Sales & Marketing Director at SCC, joins Pulsant’s Wendy Shearer to explore the challenges businesses are facing as AI moves from experimentation into production and scale.

How to Manage Cloud Alerts: AWS, Azure, and Google Cloud

To manage cloud alerts effectively, define which conditions require immediate action, include useful incident context, and route critical notifications to the engineer currently on call. Add escalation rules for unanswered alerts and test the entire path from detection to response. This process applies whether your team uses AWS, Azure, Google Cloud, or several providers. Cloud monitoring can tell you something is wrong.

Your AI Economics Pulse for October 2026

Across a same-store panel of 422 CloudZero customer organizations, AI reached 3.02% of the median company's cloud bill in September 2026, up 37 basis points from 2.66% in August, the largest monthly gain since we began tracking. The 75th percentile rose to 12.55%. The share of organizations spending at least $1,000 a month on AI barely moved, to 68.0%. Companies already spending drove the growth. Last month we called the median the least informative number in this report. September proved us wrong.

Cut your debug time by reproducing production on demand

If a bug only shows up under real production conditions, the fastest path to a fix is removing the guesswork about those conditions, not layering on more debugging tools. This guide walks through what changes when every branch gets its own preview environment, a full clone of production available on demand, and what that actually looks like day to day.

The October 8, 2026 Google Cloud Storage Outage

Google Cloud Storage in us-central1 (Iowa) had elevated latency and error rates on upload and write operations on October 8, 2026, and a smaller subset of reads was also affected, according to Google's incident page. Google records the start at 11:00 UTC and the end at 23:30 UTC the same day - 12 hours 30 minutes. The outage report - fetched on October 12, 2026 - includes a preliminary incident report as of this writing.

What Is High Availability? How It Works and How Many Nines You Actually Need

High availability (HA) is a system’s ability to keep serving users when individual components fail, from a single server up to a whole cloud region or provider. It is measured in nines: 99.9% allows about 8 hours 46 minutes of downtime a year, 99.99% about 52 minutes, and 99.999% about 5 minutes 15 seconds. HA works by removing single points of failure through redundancy, health checks, automatic failover, and replicated data.

10 Best AI Agent Infrastructure Platforms in 2026

AI agent infrastructure is the set of platforms that run agents and the code they write. It has three layers: sandboxes that isolate untrusted, model-generated code, runtimes that run the agent and its services in production, and orchestration layers that save an agent’s progress so a long run can resume after a failure. Most production agents need more than one layer. This guide compares 10 platforms across all three, with isolation, state, deployment, and compliance for each.

Shipped: Project this month's AI cost before the invoice closes

The question comes up on the 10th, the 15th, and again on the 25th. Where is AI spend going to end up this month? The invoice won’t tell you until it’s closes, and by then there’s nothing left to forecast. “If I’m looking at this on the 15th, I want to know where we’re going to land.” That’s how a finance lead put it during a persona session in September, and it’sthe whole job. You have half a month of real usage behind you.

Claude Opus pricing in 2026: every model, every rate, and whether it's worth it

Claude Opus pricing is $4 per million input tokens and $20 per million output tokens on Claude Opus 5.5, the current model, with cache reads at $0.20 and batch jobs at $2/$10. Opus 5 and the legacy 4-series bill at $5/$25. The 1M context window carries no surcharge. Every Opus model Anthropic shipped in 2026 held the same line: $5 in, $25 out, per million tokens. Opus 4.6 in February, 4.7 in April, 4.8 in May, Opus 5 in July. Four releases, one price. On September 22, 2026, the line broke.

7 Layers of Agent Security (1 in 6 repos has leaked credentials)

I scanned 10,705 public GitHub repos for leaked credentials. After removing obvious placeholders, 3,543 still had something that looked like a key, and 1,680 held almost 7,000 credentials in recognizable formats like Stripe keys. If you're new to coding with agents (or you've been a dev for decades), this is the setup I'd want you to have before you ship anything public.

Shipped: Views now work for every access level

Most people who open CloudZero care about one slice of the spend, like their team, their product, or their region. A View gives them that slice in one click, with the grouping and filters already set, so nobody has to rebuild the same Explorer query every week. Views now work for everyone in your organization, including people with scoped access.

That 2am incident cost you more than downtime

It's 2:14am. A pager alert wakes an on-call engineer for a checkout failure hitting a slice of customers. By the time they've pulled the right logs, cross-checked the deploy history, and finally gotten the bug to happen again in front of them, the sun's coming up. The incident report will list two hours of downtime. It won't say anything about the day that engineer just lost, or the fact that nobody on your team could have told you in advance how long that reproduction step was going to take.

10 Best Multi-Cloud Management Platforms in 2026

A multi-cloud management platform is software that lets you run, provision, govern, or optimize workloads across two or more environments, such as AWS, Google Cloud, Azure, and your own data centers, from one place. The products in this category do different jobs: some run your applications across clouds, some provision infrastructure as code, some govern hybrid estates, and some optimize cost. Most teams combine two or three.

18: Building an Agentic Future: AI and Optimization with Sachin Gharge

On today's episode, Andrew Hillier chats with Sachin Gharge, Head of Cloud Platform at Scandinavian Airlines (SAS). They discuss AI, agents, Kubernetes, MCP, and optimization. Sachin shares how he and his team are optimizing cloud costs, leveraging automation, and experimenting with agentic AI, including bots and Slack integrations, to make operations easier and more effective for developers and the business.

Run your first workflow in minutes, no sales call

The regression nobody catches passes a busy review and ships. An off-by-one, a change that reads as sensible and quietly breaks something, gets a nod from a tired reviewer and lands in production, where it erodes trust one small defect at a time. You can have an AI code reviewer running on your own repository in the time it takes to read this page. Get started without having to book a demo or contact sales.

Shipped: See what your AI spend is actually paying for

Most AI spend comes in with no tags and no owner attached. Your provider console shows total spend, maybe broken out by API key or model. It won’t tell you that the sales team spent $1,700 on Claude this week, let alone what the work was. And the problem is growing. McKinsey found that 56% of organizations now use AI in three or more business functions. More teams means more spend, and most companies respond with a spending cap. Set it too low and you slow down the work you wanted AI to help with.

How to Guarantee a Website or Service Never Goes Down (And What You Can Actually Promise)

No one can guarantee that a website or service never goes down. What you can promise is a measured availability target, and with a multi-location, active-active design you can reach 99.999% (five nines), about 5 minutes 15 seconds of downtime a year. That takes redundancy at every layer, automatic health-based failover across regions and ideally providers, safe deployments, failure testing, and outside-in monitoring. Control Plane is built for that tier.

Azure in Bleemeo: your subscription next to your servers, with one read-only role

Most teams that run on Azure do not run only on Azure. There is a database on a VM nobody wants to move, a Kubernetes cluster somewhere else, a few servers in a rack, and a monitoring setup that grew around all of it. Azure Monitor sees the Azure part very well and nothing else, so the picture of an incident ends up split across two consoles, two alerting configurations and two sets of dashboards. Bleemeo now connects to Azure the same way it already connects to AWS.

How Honeycomb Private Cloud Drinks From the Fire Hose

Back in July, I wrote about how the Tenant team (the team behind Honeycomb Private Cloud (HPC)) has embraced the code review bottleneck to focus more of its work. One of the other challenges we have is that we're downstream of almost all the other teams at Honeycomb, meaning that we have to package up everyone's code and services, and how it gets provisioned! This is something impossible to handle through code review since there are so many engineers on other teams, and so few of us.

Shipped: Start every session where your work lives

Most people who use CloudZero spend their time in one or two places. For some it’s AI Signals, and for others it’s Optimize or Anomalies. If Explorer isn’t one of those places, every sign-in starts with a click to get where you need to be. Dates and numbers are another friction. A date like 04/07 means April 7 in the US and July 4 in much of Europe. When the platform shows a format your team doesn’t use, you end up having to convert each value before you can work with it.

Moving Mainframe Data to Snowflake and AWS Through Apache Kafka: Treehouse Software and meshIQ

Treehouse Dataflow Toolkit moves mainframe data from Db2, VSAM, and IMS through Apache Kafka pipelines into Snowflake and AWS targets. meshIQ keeps the streaming layer visible and stable. Together they give data science teams continuously updated enterprise data for AI and ML.

Get your agents off laptops and onto shared infrastructure

There's a specific, recognizable point where a team's use of AI agents changes shape. Not when they adopt agents; most teams already have. It's when agents stop running on someone's laptop and start running on infrastructure that the whole team can see. This is a real technical shift, not a policy change or a maturity score. Here's specifically what's different on each side of it.

Why engineers ignore cloud cost governance (and fixes)

Discover why engineers ignore cloud cost governance and how to build developer cost accountability. Learn how Harness helps empower engineering teams. Engineers often overlook cloud costs due to friction in traditional FinOps tools and a lack of real-time visibility. By embedding automated guardrails and shift-left cost insights into developer workflows, organizations can drive accountability without slowing velocity.

Where do you see organizations hitting their limits?

In this clip, Virtana Chief Product Officer, Amit Rathi explains why more data does not automatically lead to better operations. As system complexity grows, organizations are collecting more telemetry than ever while struggling to turn it into actionable insights. At the same time, rising observability costs are forcing some teams to monitor only part of their environments. Watch the video to learn why intelligence, not just visibility, is becoming essential for modern IT operations.

Azure Monitor pricing: What you pay vs. what you get

Azure Monitor doesn't have a price. It has a bill and those are very different. There's no plan tier to pick, monthly set fee, or simple number to sanity-check against your budget. Instead, you're charged across a handful of separate meters: log ingestion, log queries, retention, alert rules, web tests, and custom metrics. Each of these metrics scale independently. Most teams don't see the full cost until the bill arrives.

Cycle's Hosted MCP Release Announcement

We are stoked to announce that we have released our very own hosted MCP that allows you to use natural language to interact with and control anything you have running on Cycle.io. Alexander Mattoni, CTO and co-founder of Cycle, shares a few scenarios where the MCP can be useful. With the MCP, Cycle users can communicate with the Cycle platform directly from their preferred AI tool. In minutes, users can provision a new server, deploy an application, or troubleshoot an issue directly from their AI assistant like Claude, Claude Code, ChatGPT or Codex.

Cloud Shell, New Integrations, and More

VirtualMetric DataStream now includes a PowerShell console in the browser, more than a dozen new integrations, and a new way to collect data from servers and virtualization hosts, where teams choose exactly what they collect. This update also brings guided learning for new users, built-in monitoring rules, and new controls for organizations managing branding and sign-in. Here’s what’s new.

Shipped: Get alerted when AI spend spikes, with the cause attached

AI spend now comes from every department, and it can double in a week without anyone deciding it should. The invoice arrives after the month closes. By then the usual response is a spend cap, which slows every team, including the ones getting real work done with AI. You need to know about spend that breaks its normal pattern while there’s still time to act. The alert should reach the person who can act on it, with proper context and detail.