Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Every major cloud provider has had a serious outage since mid-2025, each from a different cause, so any single provider is a single point of failure.

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.

Updated October 2026. Published by Control Plane. Ranking criteria and sources are listed below.

Isolation keeps an agent’s code away from your infrastructure. A smart sandbox also lets the agent into the systems where real bugs live, without handing it the keys.

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.

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.

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.