Autopilot vs CAST AI

This page compares Autopilot by Xano with CAST AI for teams evaluating Kubernetes cost optimization platforms or planning a migration.

Executive summary

	Autopilot	CAST AI
Deployment	Self-hosted controller in your cluster	SaaS — agent in cluster, decisions in CAST AI cloud
Data residency	All metrics and decisions stay in-cluster	Cluster data sent to CAST AI’s platform
Pricing	Fixed infrastructure cost	Percentage of savings (~20–30%) or per-node licensing
Multi-cloud	GKE-focused (EKS support in progress)	AWS, GCP, Azure
Setup time	~15 min per cluster (automated)	~10 min (Terraform module)
In-place pod resizing	Yes (KEP-1287, K8s 1.33+)	Yes (KEP-1287, K8s 1.33+)
Decision transparency	Full algorithm visibility	Black-box optimization

Feature comparison

Node scaling

Capability	Autopilot	CAST AI
Per-node machine type selection	Yes (heterogeneous mode)	Yes
Bin-packing algorithm	Configurable series priority + headroom	Proprietary
Spot/preemptible support	Yes	Yes
PV-aware zone provisioning	Yes — provisions nodes in the same zone as bound PVs	Limited
Zone-aware series filtering	Yes — removes unavailable series per zone via the GCE API	Unknown
Scale-down protection	10-min grace period + proportional removal + pending pod guard	Evictor with configurable policies
Machine type catalog	Explicit allowed series + min CPU/memory filters	Full catalog, automated selection

Autopilot advantage: PV-aware zone provisioning prevents the runaway scale-up problem where pods with zonal PersistentVolumes stay unschedulable because nodes are provisioned in the wrong zone. Autopilot resolves the PV’s zone from the PVC chain and provisions in the correct zone automatically. CAST AI advantage: Broad multi-cloud support (AWS, Azure, GCP) today.

Pod right-sizing (vertical optimization)

Capability	Autopilot	CAST AI
In-place resize (no restart)	Yes — KEP-1287 on K8s 1.33+	Yes — KEP-1287 on K8s 1.33+
Recommendation algorithm	Transparent — percentile-based with configurable buffer	Proprietary
HPA awareness	Yes — switches to P95 for HPA-managed CPU	Unknown
OOM response	3-layer protection (bump, floor, death spiral eviction)	Basic OOM handling
Change sensitivity	Dynamic threshold (~25% small, ~5% large)	Configurable threshold
Startup spike filtering	Yes — filters the first 5 min + container restart spikes	Unknown
Recommendation stability	5-layer mechanism (P99 peaks, hysteresis, split queries, etc.)	Unknown

Both support in-place resizing on K8s 1.33+ via KEP-1287. The differentiators are in the recommendation algorithm and operational transparency. Autopilot advantage: Full algorithm transparency. Every recommendation includes the percentiles, buffer, data source, sample count, and hysteresis decisions — so when a recommendation oscillates or seems wrong, you can see exactly why. CAST AI’s recommendations are opaque.

Cluster rebalancing

Capability	Autopilot	CAST AI
On-demand rebalance	Yes — CLI or API with cost analysis	Yes — via dashboard
Rolling drain strategy	Yes — batches of 5–10, ~0.5 min/node	Unknown batch behavior
Provision-first strategy	Yes — creates replacement VMs before draining	Yes
Greenfield bin-packing	Yes — recomputes optimal fleet from scratch	Unknown
Cost comparison	Before/after cost analysis per node	Savings estimate
Crash recovery	Full — persisted state in ConfigMaps, resumes on restart	Unknown
Maintenance windows	Yes — scheduled deep consolidation including fragile pods	Limited
Fragile pod detection	Yes — single-replica, no-PDB, OrderedReady StatefulSets	Unknown

Autopilot advantage: Rolling drain mode doesn’t require double capacity. It drains nodes in small batches and lets the scaler provision right-sized replacements. Provision-first approaches require enough headroom to run the old and new fleet simultaneously.

Scheduled scaling

Capability	Autopilot	CAST AI
Time-based HPA overrides	Yes — ScheduledScalePolicy CRD	No native equivalent
Pre-warming	Yes — configurable pre-scale minutes	No
HPA behavior overrides	Yes — Instant/Aggressive/Balanced/Gradual speed presets	No
Weekly/daily schedules	Yes — with timezone support	No

Autopilot advantage: CAST AI doesn’t have scheduled scaling. If you have predictable traffic patterns (morning login surges, batch processing windows), Autopilot can pre-warm pods before the burst arrives.

Observability & dashboard

Capability	Autopilot	CAST AI
Dashboard	Self-hosted Angular SPA (port 9090)	SaaS dashboard at cast.ai
GCP service monitoring	Cloud SQL, Redis, NAT, Storage, DNS, VPC, Artifact Registry, Secret Manager	Not included
Package cost attribution	Yes — per-package cost with spillover breakdown	Namespace-level cost
Network monitoring	Yes — throughput, top talkers, per-node errors/drops	Not included
Cluster health checks	Automated health-check battery	Basic health status
Recommendation audit trail	Full history with algorithm transparency	Limited history

Autopilot advantage: GCP managed-service monitoring is unique to Autopilot. It surfaces Cloud SQL CPU, Redis memory, NAT port exhaustion, and more in the same dashboard with health thresholds and Slack alerts. CAST AI advantage: Their SaaS dashboard requires zero infrastructure — no Prometheus, no port-forwarding.

Architecture differences

Data flow

Autopilot keeps everything within the cluster:

Prometheus (in-cluster) → Controller (in-cluster) → Dashboard (in-cluster)
                                                   → ConfigMaps (state)
                                                   → CRDs (config)

No external API calls except GCE for node provisioning. CAST AI computes decisions externally:

Agent (in-cluster) → CAST AI Cloud (SaaS) → Agent (applies changes)
                   ↑                        ↓
              Cluster metrics         Optimization decisions
              sent to SaaS           sent back to cluster

Node provisioning

Autopilot (heterogeneous mode):

Provisions GCE VMs directly via the Instances API.
Clones bootstrap metadata from existing GKE nodes.
Kubelet uses a ServiceAccount token for initial auth, then a CSR for the node certificate.
Autopilot handles CSR approval, pod CIDR assignment, and taint removal.
VMs are fully integrated into the K8s cluster networking and tracked in the NodeScalingPolicy status.

CAST AI:

Creates empty GKE node pools as placeholders (cast-pool, cast-pool-arm).
Provisions standalone GCE VMs named gke-<cluster>-cast-pool-<hash>.
VMs are NOT members of the GKE node pool despite the naming.
Deleting the cast-pool node pool does NOT delete the standalone VMs.

Why this matters: CAST AI’s decoupled VM lifecycle creates orphan risk. If the agent is removed or crashes, standalone VMs can persist indefinitely. Autopilot tracks every provisioned VM and includes orphan cleanup.

Conflict prevention

Autopilot includes a cluster-level CAST AI detector that scans every 5 minutes for CAST AI agent pods, webhooks, and CRDs. When detected, all Autopilot policies are forced to passive mode regardless of their configured mode, preventing the two systems from fighting over resource values. The dashboard shows a warning banner and the health endpoint reports the conflict.

Cost comparison

Autopilot total cost

Component	Monthly cost
Self-managed Prometheus	~$33 (50Gi PVC + compute)
Autopilot controller	~$15 (small controller node pool)
Total	~$48/month per cluster

No per-node fees. No percentage-of-savings fees. Cost is fixed regardless of cluster size.

CAST AI pricing

CAST AI charges based on savings delivered — typically ~20–30% of realized savings on the Growth plan, custom for Enterprise. On a cluster spending

5,000/mo where CAST AI saves 40% (

2,000), the fee would be ~

400–600/mo. Autopilot's fixed cost of ~

48/mo delivers the same optimization.

When to choose each

Choose Autopilot when:

Running on GKE (EKS support is being built out)
Data residency matters — metrics must stay in-cluster
You want full algorithm transparency and customization
In-place pod resizing (zero-downtime) is important
You need GCP managed-service monitoring in the same dashboard
You have predictable traffic patterns that benefit from scheduled scaling
You want fixed-cost optimization without savings-based fees

Choose CAST AI when:

Running across AWS, Azure, and GCP today
You prefer a fully managed SaaS solution with no infrastructure to maintain
Your team doesn’t want to operate Prometheus or manage controller upgrades

Migration path

Autopilot includes a fully automated, zero-downtime migration from CAST AI:

# One command — removes CAST AI, deploys Autopilot, adopts existing nodes
scripts/install-opsai.sh --context=<cluster>

# Validate health
scripts/verify-opsai.sh --context=<cluster>

The migration adopts existing CAST AI nodes in-place (no reprovisioning), strips CAST AI labels and taints, and runs a rebalance to right-size machine types. Typical migration time: 15–30 minutes per cluster with zero workload downtime.

Get Started

Platform

Operations

Migrating from CAST AI

Executive summary

Feature comparison

Node scaling

Pod right-sizing (vertical optimization)

Cluster rebalancing

Scheduled scaling

Observability & dashboard

Architecture differences

Data flow

Node provisioning

Conflict prevention

Cost comparison

Autopilot total cost

CAST AI pricing

When to choose each

Migration path

​Executive summary

​Feature comparison

​Node scaling

​Pod right-sizing (vertical optimization)

​Cluster rebalancing

​Scheduled scaling

​Observability & dashboard

​Architecture differences

​Data flow

​Node provisioning

​Conflict prevention

​Cost comparison

​Autopilot total cost

​CAST AI pricing

​When to choose each

​Migration path

Executive summary

Feature comparison

Node scaling

Pod right-sizing (vertical optimization)

Cluster rebalancing

Scheduled scaling

Observability & dashboard

Architecture differences

Data flow

Node provisioning

Conflict prevention

Cost comparison

Autopilot total cost

CAST AI pricing

When to choose each

Migration path