Gritiva vs Kubernetes#

A direct comparison. K8s is excellent at what it does. We're excellent at what we do. They overlap less than the marketing material would suggest.

TL;DR#

If you need to run >50 nodes, autoscale on demand, support hundreds of microservices, and have a team that can absorb the operational complexity: Kubernetes wins.

If you need to run 2-50 VMs with long-lived apps and want to spend your engineering time on the apps, not the infrastructure: Gritiva wins.

Most teams stuck between "ssh on a single box" and "we adopted K8s and now half our team is platform engineers" are actually in the second bucket. They just don't know it yet.

The honest size table#

What K8s does that we don't#

• Pod autoscaling (HPA, VPA) — react to CPU / RAM / custom metrics
• Cluster autoscaling — provision new nodes on demand
• Multi-region native — federations, multi-zone scheduling, region-aware routing
• Massive ecosystem — 10,000+ Helm charts, dozens of CNI/CSI options, every cloud has managed K8s
• Industry-standard ops — anyone you hire knows kubectl
• Battle-tested at hyperscale — Google, Stripe, Spotify, etc. run on it

What we do that K8s doesn't (well)#

• Single binary install — no etcd, no kubelet, no container runtime, no CNI choice paralysis
• Per-application netns isolation — pod-level isolation but without the pod abstraction
• Built-in mesh that works across clouds — gmesh ships with the agent; no Cilium/Calico setup
• Steady-state RAM <100 MB per node — vs 800 MB-2 GB for kubelet alone
• Domain → app in one CLI command — vs ingress controller + cert-manager + DNS provider plugin
• Read the entire source in a weekend — ~150 KLOC vs K8s' multi-million LOC

What we genuinely do BETTER (not just differently)#

These are claims we'll defend in any forum:

1. Time to first running app on a fresh VM: ~5 minutes for us, ~30-60 minutes for managed K8s, days for self-hosted K8s
2. Onboarding a new ops engineer: days for us, weeks-to-months for K8s
3. Resource overhead per node: ours is roughly 1/10th K8s'
4. The size of "things you need to debug when it breaks" — we have ~5 components; K8s has ~50

What K8s does BETTER (not just differently)#

These are honest:

1. Workload diversity — anything runs on K8s; we'd argue ours runs anything that runs in Docker, but K8s has more shapes (Jobs, CronJobs, StatefulSets, DaemonSets, etc.)
2. Operator pattern + CRDs — extending K8s is a known story; extending us is "fork and add a handler"
3. Talent pool — far more people know K8s than know our specific primitives
4. Vendor support — every cloud has a K8s offering with paid support

The migration question#

Should I migrate from K8s to Gritiva? Almost never. If K8s is working, leave it. The migration cost will dwarf any gains.

Should I migrate from "ad-hoc ssh + docker-compose" to Gritiva? Yes, this is our wedge.

Should I migrate from "we tried K8s but it crushed us" back to Gritiva? Yes, especially if your fleet is <50 VMs and you're not autoscaling.

Cost comparison (handwavy)#

For a 10-VM fleet:

This is for a 10-VM fleet. The math reverses fast above 50 VMs.

Coexistence#

You can run Gritiva on bare-metal hosts and reserve K8s for specific high-scale services. Some teams do exactly that:

• K8s cluster for the front-end + customer-facing API (autoscale-heavy)
• Gritiva fleet for data infra (Postgres, ClickHouse, queues, batch jobs) — long-lived, doesn't need autoscaling

We don't compete; we cover different ground.

Related#

• Why GritivaCore
• Feature Comparison
• Pricing