Right workload.
Right cloud. Right price.
Keep user-facing APIs on premium cloud. Move logging, backups, and AI training to cost-effective infrastructure at a fraction of the price. Same Stacks, same GitOps, same dashboard. Proven to cut cloud OpEx by 60%.
You're paying Tier 1 prices for Tier 3 workloads
Most teams run everything on a single cloud provider because moving workloads is too painful. The result: massive overspend on infrastructure that doesn't need premium SLAs.
Overpaying for infrastructure you don't need
Running logging, metrics, backups, and batch jobs on Tier 1 cloud at Tier 1 prices. Your non-user-facing workloads don't need 99.99% SLAs, but you're paying for them anyway.
Different tooling per cloud
EKS uses one set of IAM roles and deploy scripts, GKE uses another, on-prem uses a third. Triple the maintenance, triple the tribal knowledge.
Config drift between environments
Staging looks nothing like production. Helm values diverge silently. You only find out when something breaks in prod.
Vendor lock-in anxiety
Proprietary abstractions trap you. Migrating away from your current platform means rewriting everything, so you never do. The longer you wait, the more expensive it gets.
Rebuilding pipelines per provider
Every new cloud or on-prem cluster means rewriting CI/CD from scratch. Registry auth, deploy keys, ArgoCD setup. Half a day each time.
Cost scaling works against you
As usage grows, cloud bills grow faster. There's no lever to pull. You can't selectively move workloads without rebuilding your entire deployment pipeline.
Tier your workloads. Cut your bill.
Not every workload needs to run on premium infrastructure. User-facing APIs need low latency and high availability. Logging, backups, and AI training need compute and storage -- not a $0.10/GB egress fee.
Tier 1
AWS / GCP / Azure
User-facing. Latency-sensitive.
Tier 2
Hetzner / OVH / On-Prem
Internal. Throughput-optimized.
The math is simple
Dedicated / bare-metal servers vs. equivalent cloud instances
Move 60% of your workloads to Tier 2 infrastructure. Keep 40% on Tier 1 for user-facing services. Same Kubernetes. Same Stacks.
A startup saving $200k/year. And growing.
A real multi-cloud implementation with an early-stage startup. The more their usage scales, the more they save -- at an 11x cost efficiency ratio.
Before
Everything on AWS
After
Tiered with Ankra
The savings scale with usage
As this customer's usage grows, the delta widens. More data means more logging, more backups, more training jobs -- all running on Tier 2 infrastructure at a fraction of the cost. At 2x current usage, projected annual savings exceed $450k. The 11x efficiency ratio holds or improves as scale increases.
How Ankra unifies multi-cloud
One interface, one GitOps workflow, one Stack definition. Regardless of where your clusters run -- or how much they cost.
Provider-Agnostic Import
EKS, GKE, AKS, Hetzner, OVH, on-prem, bare metal. One Helm command to import. Same interface for every cluster.
Same Stack Everywhere
Build a Stack once in the visual builder. Deploy it to any cloud, clone it to a cheaper provider, promote it to on-prem. Identical dependency graphs, different cost profiles.
Git as the Single Source of Truth
Every deployment commits to your repo. Same GitOps workflow regardless of cloud. PR reviews, audit trails, rollbacks.
Zero Lock-In
Standard Helm charts in your Git repo. Standard Kubernetes manifests. If you leave Ankra, you take everything with you.
CLI, Terraform, and API
Ankra CLI for scripting, Terraform provider for IaC workflows, REST API for custom integrations. Automate across every provider.
Self-Healing Drift Detection
ArgoCD continuously reconciles. Manual cluster changes get reverted to the Git state. Consistency is enforced, not hoped for.
Works with any Kubernetes distribution
Not everything needs
99.99% uptime.
The decision to go multi-cloud isn't about moving your core product. It's about recognizing that most of your infrastructure bill comes from workloads that don't directly serve users.
Logging & Metrics
Prometheus, Grafana, Loki, ELK. Heavy on storage and compute, invisible to your users. Move to Tier 2 infrastructure, save 90% on these costs alone.
Backups & Disaster Recovery
Nightly snapshots, database replicas, archive storage. These need reliability, not low latency. Tier 2 infrastructure handles this perfectly.
AI Training & Inference
Model training, batch inference, embedding generation. GPU-heavy workloads that run in the background. No reason to pay cloud GPU premium.
Build Runners & CI/CD
GitHub Actions runners, build caches, artifact storage. Compute-intensive but not user-facing. Perfect candidate for dedicated servers.
User-facing services
stay where they belong.
Your APIs, frontends, and anything that directly impacts user experience stays on premium infrastructure. Low latency, global edge network, managed services -- where they actually matter.
REST APIs & GraphQL
Sub-50ms response times. Global CDN. Auto-scaling. Your users feel the difference -- keep these on AWS/GCP with full SLAs.
Web Frontends & SPAs
Static assets on CloudFront/CloudFlare. Server-side rendering on managed compute. Every millisecond of load time matters for conversion.
Auth & Payment Processing
PCI compliance, SOC 2, regional data residency. These have regulatory requirements that map to Tier 1 providers.
Real-time Features
WebSocket connections, live notifications, collaborative editing. Latency-sensitive by definition. Stays on the fastest network available.
60% OpEx reduction. Proven in production.
Real numbers from a real implementation. No asterisks, no "up to" disclaimers.
Implementation timeline
Import Tier 2 clusters
Provisioned 2 dedicated servers. Installed K3s. Imported into Ankra with one Helm command each.
Clone monitoring stack
Cloned the Prometheus + Grafana + Loki stack from the primary cloud cluster to Tier 2. Same dashboards, same alerts, different infrastructure.
Migrate non-critical workloads
Moved backup jobs, CI runners, and log aggregation. Updated DNS for internal services. Zero downtime -- old and new ran in parallel.
Move AI training pipeline
Cloned the ML training stack. Pointed data pipelines to Tier 2 storage. Training jobs run 11x cheaper per compute hour.
Decommission cloud resources
Confirmed all non-critical workloads stable on Tier 2. Terminated expensive cloud instances. First month's bill: 60% lower.
Stop overpaying for infrastructure
that doesn't need premium cloud
Tier your workloads. Cut your bill by 60%. Same Kubernetes, same Stacks, same GitOps -- just smarter placement.