Day 2 Operations for AI Infrastructure: How to Keep Model Serving Running 24/7

Keeping AI infrastructure running 24/7 takes automated patching and upgrades, autoscaling, three layers of observability (infrastructure, serving, and model and data), tested incident response, and policy-controlled automation. You can staff all of it yourself, buy managed services piece by piece, or use a platform that handles the infrastructure layer for you. Control Plane handles Day 2 operations for the workloads it runs, including autoscaling, Capacity AI (Control Plane’s right-sizing of CPU and memory reservations from historical usage), built-in observability, and security, so teams focus on models and data instead of clusters.

Disclosure: Control Plane publishes this guide.

See Control Plane’s guides on what is AI-ready infrastructure and what infrastructure you need to run an inference layer.

TL;DR

• Day 2 for AI is everything after the first successful deployment: the ongoing work of patching, upgrading, scaling, monitoring, responding to incidents, controlling costs, and enforcing policy across inference infrastructure.
• The operations that matter most are OS and node patching, GPU driver and CUDA upgrades, model server upgrades, autoscaling, capacity planning, certificate rotation, backups, observability, incident response, and cost control.
• Three observability layers are required: infrastructure metrics (CPU, memory, GPU utilization), serving metrics (latency percentiles, error rates, tokens per second), and model and data metrics (prediction quality, input drift, cost per request).
• Build vs. managed vs. platform : you can run raw Kubernetes yourself, use hyperscaler managed services, or adopt a platform that operates the infrastructure layer for you. Each trades off control, staffing, and portability differently.
• Control Plane is the platform option built for Day 2. It runs workloads active-active across locations with no Kubernetes cluster you have to operate.

What are Day 2 operations for AI infrastructure?

Day 1 is getting a model into production: provisioning compute, deploying a model server, and routing the first request. Day 2 is everything after. It covers patching, upgrades, scaling, monitoring, and incident response. A typical Day 2 failure is a GPU driver update that breaks CUDA compatibility and takes down a model server.

AI infrastructure amplifies Day 2 complexity compared to traditional software. GPU resource constraints, model versioning, drift detection, and bursty workloads create failure modes that standard web services rarely encounter. Skipped Day 2 tasks rarely cause an outage right away. The risk builds until it shows up as a production incident, often during a traffic spike or an unrelated change.

How can AI operable infrastructure automate patching, upgrades, and policy enforcement for clusters?

Automate them with managed node auto-upgrades, pinned and tested GPU driver images, rolling and canary rollouts, and admission-control policies that also run in CI.

Automated node image updates. All three major managed Kubernetes services can roll out new node images for you. Amazon EKS managed node groups update to the latest EKS optimized AMI in one operation that you start, and EKS Auto Mode replaces nodes automatically at least every 21 days. Google Kubernetes Engine enables node auto-upgrade by default. Azure Kubernetes Service offers node OS auto-upgrade channels and defaults new clusters to the weekly NodeImage channel. When a node must be replaced, it is drained and replaced in a rolling fashion.

GPU driver and CUDA version management. GPU driver upgrades are higher risk than standard node patches because a mismatch between the driver version, CUDA toolkit, and the model framework (PyTorch, TensorFlow, vLLM) can stop the model server from starting, crash inference, or quietly cost performance. The safe pattern is to pin a tested driver and CUDA version in your node image, validate it against your model server in a staging environment, and only then roll it out to production nodes. Avoid letting auto-upgrade channels change GPU drivers without testing.

Rolling upgrades with surge capacity. Rolling upgrades replace pods or nodes one at a time (or in small batches) while maintaining enough healthy replicas to serve traffic. For CPU nodes, a surge of one or two extra nodes prevents capacity dips. GPU surge capacity may be unavailable or costly, so many teams set surge to zero, keep enough spare replicas to lose one node, and upgrade off-peak. Set a PodDisruptionBudget, and gate traffic on a readiness probe that passes only after the model is loaded, so a replica is not drained before its replacement can serve.

Canary upgrades for model servers. Before promoting a new model server version to all replicas, deploy it to a single replica and compare its latency, error rate, and output quality against the existing version under real traffic. If metrics degrade beyond a threshold, roll back automatically.

Policy as code. Admission controllers such as Open Policy Agent Gatekeeper and Kyverno enforce rules at deploy time: requiring resource limits on GPU workloads, blocking images from untrusted registries, mandating labels for cost allocation, and preventing privileged containers. Running the same policies in CI catches violations before they reach the cluster.

How do I design observability and operations for AI-ready infrastructure supporting 24/7 model serving?

Design it around three observability layers, SLOs with error budget burn-rate alerts, and a tested incident runbook, so every alert maps to a layer and a response.

How can I add observability for AI infrastructure including model metrics, data drift, and system logs?

Add observability in three layers: infrastructure, serving, and model and data.

Layer 1: Infrastructure metrics. This layer covers the compute substrate. Key signals include CPU and memory utilization, GPU utilization and GPU memory headroom via NVIDIA DCGM, node health, disk I/O, network throughput, SM activity (a truer load signal than GPU utilization), power and thermal throttling, and GPUs that sit allocated but idle. System logs from the OS, kubelet, and container runtime surface hardware errors, OOM kills, and GPU Xid errors. Some Xid codes point to failing hardware (Xid 79 means the GPU has fallen off the bus), and others point to application or driver faults.

Layer 2: Serving metrics. This layer covers the model server and its request handling. Key signals include request rate, server error rate (5xx) tracked apart from 429 rate-limit responses, latency percentiles (p50, p95, p99), time to first token and inter-token latency for streaming LLM endpoints, tokens per second, queue depth, batch size, KV cache usage and request preemptions (vLLM exports both as Prometheus metrics), model load time, and cold start frequency.

Layer 3: Model and data metrics. This layer covers the correctness of what the model produces. Key signals include prediction quality (accuracy, F1, or domain-specific metrics when labels are available), drift in input feature distributions compared to training data, feedback signals from users or downstream systems, and cost per request broken down by model and team. Silent failures, where the endpoint returns 200 OK but the output is wrong, are only caught at this layer.

Tooling recommendations. OpenTelemetry provides a vendor-neutral standard for collecting traces, metrics, and logs. Prometheus scrapes and stores time-series metrics. Grafana builds dashboards and supports alerting. Define SLOs for latency, availability, and error rate, then set alerts on error budget burn rate rather than raw thresholds. Use multiwindow, multi-burn-rate alerts as described in the Google SRE Workbook. A fast window catches sharp spikes and pages. A slow window catches gradual burn and opens a ticket.

What managed operations services help run AI infrastructure 24/7 with reliability and incident response?

Managed options include hyperscaler Kubernetes services (Amazon EKS, Google Kubernetes Engine, Azure Kubernetes Service), hyperscaler ML platforms, and platforms such as Control Plane that handle Day 2 operations for the workloads they run.

Which providers offer managed AI operations and maintenance for AI infrastructure?

The three approaches differ in what you still operate, how many platform engineers you need, and how portable your workloads are.

Hyperscaler managed Kubernetes services (Amazon EKS, Google Kubernetes Engine, Azure Kubernetes Service) reduce the burden of managing the Kubernetes control plane but still leave GPU driver version testing, node pool configuration, autoscaling policies, observability setup, and security hardening to you. Each cloud also offers managed ML platforms (Amazon SageMaker AI, Google Gemini Enterprise Agent Platform (formerly Vertex AI), Azure Machine Learning), which further abstract infrastructure but tie your training and serving pipelines to that cloud’s APIs, identity, and storage, making a move to another cloud harder.

How should I handle incident response for AI model serving?

Handle incidents with SLO-based detection, triage by observability layer, rollback before root-cause analysis, failover to healthy locations, early stakeholder updates, and a written post-incident review.

1. Detect with SLO-based alerts. An error budget burn rate alert fires when errors are using up the budget fast enough to matter, well before it runs out.
2. Triage by layer. Is the problem infrastructure (node down, GPU error), serving (model server crash, queue backup), or model/data (drift, bad predictions)? Check each observability layer in order.
3. Roll back the model or the deployment. If a recent model update or infrastructure change correlates with the alert, roll back first and investigate second.
4. Fail over to another location. If the problem is localized to a region or availability zone, shift traffic to healthy locations. Multi-location serving makes this possible without manual intervention.
5. Communicate. Notify stakeholders with what is known, what is being done, and when the next update will come.
6. Write a post-incident review. Document the timeline, root cause, contributing factors, and action items. Share it broadly.

Multi-location serving shortens incidents because traffic shifts to replicas already running in other locations. Size each location with enough headroom, or enough autoscaling room, to absorb a failed location’s traffic. This matters because major cloud regions experience multi-hour outages regularly. Control Plane’s cloud outage tracker documents these events across AWS, GCP, and Azure, showing that relying on a single region creates a predictable availability ceiling.

How do I implement CI/CD for AI infrastructure and machine learning pipelines on Kubernetes?

Version code, data, and models together, gate every model on evaluation results, and deploy through GitOps with canary rollouts and automatic rollback.

Version everything. Store application code in Git. Track data versions with DVC or a similar tool. Store model artifacts in a model registry with metadata about training data, hyperparameters, and evaluation results.

Build model-server containers. Use reproducible container builds that pin the base image by digest, the CUDA runtime version (which the host GPU driver must support), and the model framework version. Tag images with the model version and the commit hash.

Run evaluation gates before promotion. Automated tests compare the candidate model’s accuracy, latency, and resource consumption against the current production model. If the candidate fails any gate, the pipeline stops.

Deploy with GitOps or a CI tool. Argo CD and Flux watch a Git repository for changes and reconcile the cluster state to match. Alternatively, a CI tool can apply Kubernetes manifests or Helm charts directly.

Canary and roll back. Route a small fraction of traffic to the new deployment. Monitor serving metrics and model quality. If metrics degrade, roll back automatically.

Keep infrastructure as code. Define all infrastructure, including clusters, node pools, networking, and policies, in version-controlled templates.

Which tools automate AI infrastructure provisioning and policy enforcement for multi-cloud environments?

Infrastructure as code tools, policy engines, GitOps controllers, and multi-cloud platforms handle provisioning and policy enforcement across clouds.

Infrastructure as code. Terraform and OpenTofu use declarative HCL to provision resources across AWS, GCP, and Azure. Pulumi offers the same capability using general-purpose programming languages. All three support state management and plan previews, and can detect drift by comparing live resources with recorded state; scheduled drift detection is a feature of HCP Terraform and Pulumi Cloud paid tiers.

Policy engines. Open Policy Agent evaluates policies written in Rego against Kubernetes admission requests, Terraform plan output, and API calls. Gatekeeper runs OPA policies as a Kubernetes admission controller, and Kyverno is an alternative that uses YAML and CEL policies instead of Rego.

GitOps controllers. Argo CD and Flux reconcile cluster state against a Git repository, providing an audit trail of every change and enabling rollback by reverting a commit.

Platforms that expose one API across clouds. Rather than maintaining separate Terraform modules, CI pipelines, and observability stacks for each cloud, a platform approach provides a single API for deploying and operating workloads everywhere. Control Plane takes this approach, with one API across AWS, GCP, Azure, neoclouds, and your own servers. Control Plane ships its own Terraform and Pulumi providers, so it complements the infrastructure as code you already run.

What are the best AI controlled infrastructure platforms for managing spot instances?

Spot and preemptible instances offer deep discounts (AWS advertises up to 90% off On-Demand prices, Google Cloud up to 91%, and Azure up to 90% off pay-as-you-go prices) but can be reclaimed at short notice. AWS gives a two-minute interruption notice, and Google Cloud and Azure give about 30 seconds. Model load time often exceeds that window, so a replica on spot capacity must stop taking traffic and drain as soon as the notice arrives. Tools that manage this include Karpenter on AWS, which watches for interruption notices and replaces nodes, and GKE Spot node pools with the cluster autoscaler. Spot GPUs are also scarcer than spot CPUs, so expect reclaims and stockouts.

Good candidates for spot capacity:

• Batch inference jobs that can be restarted
• Training runs with periodic checkpointing, so progress is not lost on interruption
• Stateless inference replicas with sufficient headroom, where losing one replica does not drop all traffic

Poor candidates for spot capacity:

• Single-replica serving endpoints, where an interruption means a full outage
• Latency-sensitive endpoints without fallback replicas on on-demand capacity

The recommended approach is to mix on-demand and spot capacity. Run a baseline of on-demand replicas to guarantee minimum availability, and add spot replicas for burst capacity. Checkpoint training jobs frequently. Use multi-location fallback so that if spot capacity is reclaimed in one location, traffic shifts to replicas in another.

Is it safe to let AI agents help operate AI infrastructure?

Yes, if agents start with read-only access and every change goes through scoped permissions, approval gates, and an audit trail.

Low risk with read-only access:

• Read-only diagnosis: querying logs, metrics, and deployment status (treat log content as untrusted input, and redact secrets)
• Summarizing error patterns across time windows
• Proposing configuration changes for human review
• Generating runbook steps from incident context

Requires guardrails:

• Restarting or scaling workloads (scoped permissions, approval gates)
• Modifying network policies or secrets (approval for destructive actions)
• Deploying new versions (CI/CD pipeline integration, not direct API calls)

Give agents least-privilege permissions and record every action they take. On Control Plane, the MCP server connects AI assistants such as Claude to the Control Plane API over the Model Context Protocol, so they can query logs and metrics, diagnose a failing workload, and restart or roll it back, within the orgs you authorize and the toolset profile you choose, including a read-only profile. Every action is recorded in Control Plane’s tamper-proof audit trail. The CLI, console, Terraform and Pulumi providers, and the MCP server all work through the same Control Plane API.

How Control Plane approaches Day 2 operations for AI workloads

Control Plane is the platform for running unbreakable apps and agents. It combines AWS, GCP, Azure, neocloud and bare-metal servers, and on-premises machines into Control Plane’s Global Virtual Cloud (GVC), a set of locations a workload runs across. On Control Plane-provided compute, there is no Kubernetes cluster you have to operate. Control Plane handles Day 2 operations for you, including autoscaling, Capacity AI right-sizing of CPU and memory, built-in observability, and security.

For teams that need to use their own servers, including GPU servers, Control Plane’s Managed Kubernetes (MK8s) turns those machines into managed locations. Control Plane operates the Kubernetes layer on servers you join, and the hardware stays yours.

Built-in observability collects metrics and logs for every workload in every location, plus traces once tracing is turned on for the org or GVC, with no third-party agent to install. Each org gets Grafana with built-in alert rules, and a workload can expose its own metrics, such as a model server’s tokens per second or queue depth, for Control Plane to scrape and autoscale on. Logs can be forwarded to Datadog, Coralogix, Syslog, Fluentd, or any OpenTelemetry target, and metrics can be federated into your own Prometheus. Model quality and drift monitoring stay in your ML tooling.

Multi-location availability is a core architectural feature. A workload deployed to a GVC runs active-active in every location of that GVC, and a global endpoint routes each request to the nearest healthy location. Readiness probes (HTTP, gRPC, TCP, or a command) decide when each replica can take traffic, and when a location has no ready replicas the global endpoint routes requests to another healthy location. The 99.999% availability SLA applies to workloads running at least two replicas in at least two locations. Those locations can be two regions of one cloud or locations on different providers.

This architecture proved its value during the October 20, 2025 AWS us-east-1 outage, which lasted about 15 hours and affected more than 100 AWS services. Control Plane customers with us-east-1 in their GVC had no downtime, and their workloads were “active and running in healthy locations within 10 seconds,” according to Control Plane internal incident telemetry.

GPU workloads. Workloads can attach NVIDIA GPUs, and the pricing page lists models from T4 to B200. On Control Plane-managed GPUs, drivers are installed automatically and Control Plane keeps driver versions current. Teams serving AI inference across several GPU providers group those GPU locations into one GVC. Control Plane-managed regions offer NVIDIA T4 and A10G, and for other models MK8s joins GPU servers from neoclouds or your own data center as additional locations. The workload’s GPU request applies in every location of the GVC.

Security defaults. Workloads are closed by default behind a deny-by-default internal firewall. Traffic between workloads uses mTLS. Each workload is isolated with cgroups, namespaces, and restricted syscalls and capabilities. Control Plane’s built-in secrets management keeps secret values out of code and images. Universal Cloud Identity, Control Plane’s credential-free cloud access, gives workloads short-lived, least-privilege access to 600+ services across AWS, GCP, and Azure, such as pulling model weights from Amazon S3 or Google Cloud Storage, with no long-lived cloud keys in your code.

Cost efficiency. Customers typically cut compute costs 30 to 50 percent compared with running directly on AWS, GCP, or Azure. On Control Plane-provided compute, you pay for the CPU and memory you reserve, by the millicore and the megabyte, with no contracts or minimums. Egress, storage, observability, dedicated load balancers, and GPUs are billed separately.

Control Plane is PCI DSS Level 1, SOC 2 Type II, HIPAA, and GDPR compliant.

FAQ

What are Day 2 operations for AI infrastructure?

Day 2 operations are the ongoing tasks required after initial deployment to keep AI inference services running reliably. They include patching, upgrading, autoscaling, observability, incident response, cost control, and policy enforcement.

How do Day 2 operations differ from Day 1 for AI systems?

Day 1 is provisioning compute and deploying a model for the first time. Day 2 is maintaining that deployment over weeks, months, and years, handling upgrades, scaling, monitoring, and incidents as they arise.

What observability do I need for AI infrastructure including model metrics, data drift, and system logs?

Three layers: infrastructure metrics (CPU, memory, GPU utilization, node health), serving metrics (latency, error rate, tokens per second, queue depth), and model and data metrics (prediction quality, input drift, cost per request). OpenTelemetry, Prometheus, and Grafana are widely used open-source tools for collecting and visualizing these signals.

Which providers offer managed AI operations for scalable and reliable deployment?

Hyperscaler managed Kubernetes services (Amazon EKS, Google Kubernetes Engine, Azure Kubernetes Service) manage the Kubernetes control plane but leave GPU operations, observability, and scaling to you. On Control Plane-provided compute, Control Plane operates the infrastructure layer, including GPU drivers, autoscaling, observability, networking, and security, so teams focus on their models and application logic. On your own servers, Control Plane’s Managed Kubernetes (MK8s) operates the Kubernetes layer while the hardware stays yours.

How do I implement CI/CD for AI infrastructure and machine learning pipelines on Kubernetes?

Version code, data, and models. Build reproducible model-server containers. Run evaluation gates before promotion. Deploy with GitOps tools like Argo CD or Flux. Use canary deployments with automatic rollback based on serving and model quality metrics.

What tools automate AI infrastructure provisioning and policy enforcement for multi-cloud environments?

Infrastructure as code tools (Terraform, OpenTofu, Pulumi), policy engines (Open Policy Agent with Gatekeeper, or Kyverno), and GitOps controllers (Argo CD, Flux) form the foundation. Control Plane adds a single API across clouds with its own Terraform and Pulumi providers.

How should I handle incident response for AI model serving?

Detect with SLO-based alerts, triage by observability layer (infrastructure, serving, model), roll back recent changes, fail over to healthy locations, communicate with stakeholders, and write a post-incident review. Multi-location serving shortens incidents because traffic shifts to replicas already running elsewhere, as long as those locations have headroom.

Is it safe to let AI agents help operate AI infrastructure?

Yes, if agents start read-only. Querying logs and metrics is low risk, though log content should be treated as untrusted input. Actions that modify state, such as restarting workloads or deploying new versions, need scoped permissions, approval gates, and a tamper-proof audit trail. On Control Plane, the MCP server can connect with a read-only toolset profile for safe diagnosis, reaches only the orgs you authorize, instructs the assistant to explain the impact and get your approval before it deletes or overwrites resources, and records every action in Control Plane’s tamper-proof audit trail.

What are the best approaches for managing spot instances for AI workloads?

Spot capacity works well for batch inference, training with checkpoints, and stateless inference with headroom. It is risky for single-replica serving. Mix on-demand and spot capacity, checkpoint frequently, and use multi-location fallback to handle interruptions.

How does Control Plane handle Day 2 operations for AI workloads?

Control Plane handles autoscaling, Capacity AI right-sizing, built-in observability, security, and multi-location failover with no Kubernetes cluster to operate. Workloads run active-active across locations with a global endpoint that routes to the nearest healthy replica. The 99.999% availability SLA applies to workloads running at least two replicas in at least two locations.

Talk to the Control Plane team or sign up free to try it.

Originally published on the Control Plane blog.