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By Noah Grinstein
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.
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By Noah Grinstein
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.
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By Noah Grinstein
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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By Noah Grinstein
Workload identity replaces stored cloud keys with an identity the platform proves for each workload. The cloud exchanges that proof for short-lived, least-privilege credentials at runtime, so there is no long-lived key to leak, rotate, or copy.
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By Noah Grinstein
A Kubernetes alternative is a platform that runs containers in production without your team operating Kubernetes clusters. The right one depends on how much reliability you need, whether you stay on one cloud, and whether your stack depends on the Kubernetes API.
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By Noah Grinstein
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.
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By Noah Grinstein
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.
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By Noah Grinstein
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.
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By Noah Grinstein
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.
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By Noah Grinstein
You move workloads off a single hyperscaler without re-architecting by running the container images you already have on more than one provider, keeping the native services worth keeping, and adding providers one location at a time.
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.
AI-generated code security is the part nobody plans for. Ross Hendrickson, CTO of Inspectiv, on what happens after the AI writes it. He can think of a feature and ship it the same day, generating more code than a week of writing it by hand would have produced. The vulnerabilities don't disappear along with the typing. Something still has to review what was generated, secure it, and get it out the door without becoming the new bottleneck.
I pulled every core-service outage AWS, Azure, and GCP published from January 2025 through September 2026 and measured what each deployment pattern would have saved.
If you're trying to run more than a few AI coding agents at once, the real problem shifts from prompting to managing where they live and what they can reach. This video walks through the five levels of agent management, from the IDE all the way to a fleet running in the cloud with access to your own services.
Kintsugi puts sales tax compliance on autopilot for 7,300 companies selling into 110 countries, and its tax engine sits inside other companies' checkouts. The answer has to arrive before the shopper finishes paying. "Typically, a customer's expectation is that we are returning the sales tax estimate on the invoice within 100 milliseconds." The cost of missing is not abstract: "For every minute that our sales tax API, if it ever goes down, our customers are not able to collect roughly $4 million in sales tax that they should be collecting.".
Matt Zytaruk, VP of Technology at Minga, on what changed once his developers could point their AI tools at their own infrastructure. Minga checks in 1.5 million students across the eastern seaboard every school morning. They run that platform on Control Plane, which gives their team an MCP server, a CLI, and Terraform-native resources — so a developer can investigate an issue and generate the code for the change without waiting on anyone.
"Hey, I built this over the weekend. I want to get it in front of customers." And it always hits architecture, security, and infrastructure. Ross Hendrickson, CTO at Inspectiv, calls that gap the chasm. His team crosses it on Control Plane: AI-written code secured, reviewed, and released in a day. Control Plane combines AWS, GCP, Azure and your own hardware into one virtual cloud shaped to your workloads.
When your platform is tied to a single cloud, every move turns into a migration. This team decided it didn't have to be. In this clip, a Control Plane customer explains how separating their platform from the cloud provider underneath changed the math on infrastructure. Instead of being locked to one vendor, they can pick the best services from each cloud and move workloads to AWS without jumping through hoops. The kicker: a migration whose sheer scale would have "scared them off" becomes a non-event once your platform isn't welded to a single provider. Control Plane is an AI-native virtual cloud.
"The biggest surprise was how much support we've gotten from the Control Plane team." — Minga Most teams know the drill: unless you're paying a lot, you don't get real support from GCP or AWS. Minga found the opposite with Control Plane. Whenever an infrastructure issue comes up, the Control Plane team jumps in — digging through logs, tracking down the root cause, and staying in the trenches even when the problem isn't theirs to fix.
Whether utilizing AI (Claude, Gemini, ChatGPT, etc.) or authoring by hand - BoomURL.com is the easiest and fastest way to get a never expiring URL online. Share it with your friends - or with the whole planet. Free forever..
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Run compute anywhere, combine cloud services, boost developer productivity, and slash costs.
Control Plane is a hybrid platform enabling cloud architects to combine the services, regions, and computing power of Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure and any other public or private cloud to provide developers with a flexible yet unbreakable global environment for building backend apps and services.
On Control Plane, microservices can run simultaneously on any combination of cloud compute and consume any combination of cloud services without embedded credentials. The platform handles identity conveyance and authorization uniformly, utilizing best-practices/least privilege principles consistently and securely.
Benefits of Control Plane:
- Ops without the Pain: Best in class Ops, codified, using tools you already know and trust.
- Virtual Cloud: Mix-n-match services from AWS/Azure/GCP in a single workload.
- Multi-Region: Ultra-low latency and unbreakable 99.999% availability.
- Spend Less: Only pay for the CPU/RAM/egress workloads actually consume.
The Global Compute Platform Enabling Multicloud-Native Apps.