A five-plane fabric, engineered for autonomy.

Each plane is independently versioned and rolled. No single point ever takes the cluster down. Below is how the system is actually wired — not a marketing diagram.

The five planes

01
Edge Plane
120+ POPs. Anycast TLS 1.3 + QUIC. Median 14 ms TTFB globally. DDoS scrubbing at L3-L7, geo-aware routing, and zero-downtime cert rotation built into the dataplane.
02
Control Plane
Raft-replicated state across 5 regions with sub-second leader election. CRDT-backed config so cross-region writes never conflict. Survives a full-region outage with no data loss.
03
MUMIT AI Plane
Continuous reinforcement loop training on cluster telemetry — placement, retries, scaling, retry budgets. Models are sandboxed and gated by online A/B canaries before they take action.
04
Data Plane
Hexagonal mesh fabric with eBPF datapath. Per-workload mTLS without sidecars, line-rate L7 policy enforcement, and zero packet copies between containers and the kernel.
05
Storage Plane
Erasure-coded object store (12+4), zero-copy snapshots, point-in-time across regions, and async cross-region replication with RPO < 5s for tier-1 volumes.

Request lifecycle

From client TCP SYN to MUMIT updating its model — typically under 18 milliseconds.

Request hits the edge

Anycast lands the connection at the nearest POP. QUIC terminates, JWT/mTLS validated in <2ms.

Policy + placement

MUMIT decides which region and which replica based on warm cache, tenant SLO, and live latency map.

eBPF datapath

Packet handed to the workload via kernel-bypass path. No sidecar hop. Inline observability metadata added.

Telemetry loop

Per-request signals stream back to MUMIT. The model updates online, influencing the next 1000 requests.

Core pillars

The architectural primitives that make the planes above possible.

Hexagonal Mesh

Distributed compute fabric — every node is a peer. No master bottleneck, no leader to lose.

Global Edge Network

120+ low-latency regions stitched by anycast and BGP-aware routing with sub-millisecond convergence.

Predictive Autoscaling

ML-driven capacity planning that scales ahead of demand, not behind it — measured in tokens, not pods.

Live Sandbox CLI

Test deploys in-browser against a real ephemeral cluster slice — same kernel, same scheduler, same fabric.

Engineering principles

Autonomy by default

Every plane has a control loop. No human action is required to keep the cluster running.

Defense in depth

Identity, network, runtime, and supply-chain — every layer assumes the previous one is compromised.

Latency is a feature

Every hop is budgeted. A new feature ships only if it fits the existing latency envelope.

State is first-class

Stateful primitives use the same scheduler as stateless. No second-class data tier.

At a glance

Regions

120+

Median TTFB

14 ms

Failover

< 800 ms

SLO

99.999%

Datapath

eBPF, kernel-bypass

Identity

SPIFFE + TPM

Storage durability

12+4 EC, 14-nines

Audit retention

7 years, immutable