Parinita  /  Products  /  Parinita Maestro
Foundation Layer

Parinita Maestro

Kubernetes Lifecycle for Multi-Silicon AI Edge. 909+ Clusters. Zero-Touch.

11 immutable per-plane cluster profiles. Zero-touch bootstrap. Self-healing across 101 POPs.

Maestro is the Kubernetes lifecycle platform purpose-built for Parinita's nine-plane infrastructure. It manages 909+ RKE2 clusters (plus K3s on edge planes) under 11 immutable per-plane cluster profiles — the first K8s management platform purpose-built for multi-silicon AI edge.

101
Sites Worldwide
909 +
K8s Clusters
11
Per-Plane Profiles
<12min
Node Bootstrap Time
Request a Demo → Explore Capabilities
01 / The Problem

Kubernetes Cluster Drift Across 100+ Sites With Heterogeneous Silicon Is a Persistent Incident Source

Managing Kubernetes across 900+ clusters on heterogeneous silicon — MI350P, NVIDIA Blackwell, EPYC Turin, AmpereOne — with no per-plane cluster profiles means constant drift.

Standard Kubernetes management tools assume homogeneous node pools. Multi-silicon AI edge requires per-silicon resource configurations, GPU device plugins, and kernel parameters that differ by plane type. Without per-plane profiles, every cluster diverges.

  • Multi-Silicon Cluster Management Has No Per-Plane Profile Model

    Standard K8s management assumes homogeneous node pools. MI350P, NVIDIA Blackwell, EPYC Turin, and AmpereOne each need different resource configs, GPU plugins, and kernel parameters — no standard tool handles this.

  • 900+ Cluster Updates Require Manual Coordination

    Updating Kubernetes version, GPU device plugins, or network policies across 900+ clusters without a coordinated rollout model means per-cluster manual work or risky simultaneous fleet-wide updates.

  • No External Dependency Means No Centralized Control

    Management planes that require external cloud connectivity to coordinate cluster upgrades create a dependency that breaks at exactly the wrong time — a regional outage.

Capability
Current / legacy
What's needed
Multi-silicon support
Generic K8s management — one node pool model, no per-silicon resource configuration
11 immutable per-plane profiles — each silicon type gets the correct resource limits, GPU plugins, and kernel parameters
Cluster update model
Manual per-cluster coordination or risky simultaneous fleet-wide update
Canary, staged rollout with automatic rollback — never manual, never simultaneous fleet-wide
External dependency
Management plane requires external cloud connectivity — fails at regional outage
Control plane runs on the infrastructure it manages — regional outage does not block local reconciliation
Bootstrap time
Manual node configuration — hours per node at scale
Zero-touch — Instrument registration triggers full bootstrap in under 12 minutes per node
Cluster audit trail
K8s audit logs — no cryptographic lineage of cluster lifecycle events
Chrysalis-anchored cluster events — provision, upgrade, cert rotation all on-chain and independently verifiable
Drift management
Manual drift detection — operators periodically audit cluster configs for divergence
Continuous profile drift detection with autonomous remediation — no operator action required
02 / Core Capabilities

Per-plane cluster profiles. Zero-touch bootstrap. Self-healing operations.

Maestro's per-plane cluster profiles — versioned, immutable definitions per silicon type — are what make 909+ clusters manageable as one fleet rather than 909 individual engineering problems.

A profile change triggers a controlled canary rollout — never a manual reconfiguration of hundreds of clusters.

  • Per-Plane Cluster Profiles

    11 immutable per-plane profile definitions — one per silicon type — pinning resource limits, GPU device plugins, kernel parameters, and network policies appropriate for that hardware.

  • Zero-Touch Bootstrap

    From Instrument node registration to Orchestra seat availability — RKE2 bootstrap, GPU/NPU device plugin install, registry join — all in under 12 minutes, zero manual steps.

  • Precise Northbound API

    gRPC/REST API consumed by Orchestra — list clusters with capacity and health, provision namespace and ResourceQuota per seat purchase, deprovision on churn, query node health.

  • Self-Healing Operations

    A/B image distribution with auto-rollback, self-healing node replacement, certificate rotation, etcd snapshot/restore, and continuous profile drift detection — all autonomous.

  • No External Dependency

    Maestro's control plane runs on the infrastructure it manages. A regional outage does not prevent local Maestro from reconciling its assigned clusters — autonomy is architectural.

  • Canary Cluster Profile Rollouts

    A profile change triggers a canary rollout — one cluster validates first before the change propagates. Never a manual reconfiguration of hundreds of clusters simultaneously.

  • Chrysalis-Anchored Cluster Events

    Every cluster lifecycle event — provision, upgrade, cert rotation, image push — anchors on Chrysalis as an immutable record. Cluster history is cryptographically verifiable.

  • RKE2 and K3s Support

    RKE2 for planes 1-8 (FIPS-compliant, hardened). K3s on Plane 7 edge clusters where resource constraints favor the lighter distribution. Both under the same Maestro profile model.

  • Continuous Profile Drift Detection

    Maestro continuously validates every cluster against its assigned per-plane profile. Drift triggers automatic remediation — no operator action required.

03 / Architecture

The Plane Model

Orchestra introduces "planes" — logical groupings of hardware optimized for a specific workload class. Unlike Kubernetes node pools, planes represent fundamentally different hardware architectures with different drivers, network requirements, and scheduling semantics.

The plane model is what makes Orchestra different from every other orchestration tool. Kubernetes sees nodes. Orchestra sees purpose-built hardware tiers and routes workloads accordingly.

P1
Reasoning Cortex
AMD Instinct MI350P
Primary AI inference · LLM serving
1,450+ nodes · 288GB HBM3e · high-bandwidth accelerator
P2
Training & Generation
NVIDIA RTX PRO 6000 Blackwell
Training · TTS · creative compute
950+ nodes · 96GB GDDR7
P3
Chain & CPU Compute
AMD EPYC Turin 9005
Chrysalis validators · CPU inference
700+ nodes · Zen 5c
P4
Knowledge & Retrieval
Intel Sierra Forest
Almanac vector search · RAG anchor
1,250+ nodes · 144 E-cores
P5
Long-Term Memory
NVMe Storage
Enclave · Stratum immutable object
850+ nodes · ransomware-resistant
P6
Media & Acceleration
RTX 4500 BSE · Alveo MA35D
Four tiers · GPU + FPGA + CPU
2,150+ nodes · 4K/8K hardware acceleration
P7
Edge Reflex
Qualcomm Cloud AI 100 Ultra
Ultra-low-latency edge inference
2,000+ nodes · sub-10ms response
P8
Coordination Layer
AmpereOne A128
Orchestra · Chorus routing · agents
2,400+ nodes · 128 ARM cores
P9
Nervous System
Cisco 8000 · Palo Alto · Arista
Routing · firewall · dual fabric
3,500+ devices · ConnectX-7 NICs
04 / Cluster Lifecycle Model

Per-plane profiles. Canary rollout. Autonomous operations.

Maestro provides a precise northbound gRPC/REST API consumed exclusively by Orchestra — list clusters with capacity and health, provision a namespace and ResourceQuota for a seat purchase, deprovision on churn, query node health.

  1. 01
    Per-plane profile
    Each of the 9 plane types has a versioned, immutable cluster definition pinning resource limits, GPU plugins, kernel parameters, and network policies for that silicon.
  2. 02
    Bootstrap sequence
    Instrument node registration triggers Maestro to validate silicon ID and POP, select the correct profile, bootstrap RKE2 or K3s, install device plugins, and join the registry.
  3. 03
    Profile change rollout
    A profile version update triggers a canary rollout — one cluster validates first, then staged fan-out across all clusters of that plane type. Never simultaneous fleet-wide.
  4. 04
    Northbound API
    gRPC/REST API consumed exclusively by Orchestra. Maestro surfaces cluster capacity, health, namespace provisioning, and node state — not workload-level operations.
  5. 05
    Self-healing operations
    Node replacement, certificate rotation, etcd snapshot/restore, and profile drift correction all run autonomously — no human approval required for routine cluster operations.
  6. 06
    External dependency
    Maestro control plane runs on the fabric it manages. Regional outage does not block local cluster reconciliation — autonomy holds at the POP level.
  7. 07
    Chrysalis anchoring
    Cluster provision, upgrade, cert rotation, and image push all anchor on Chrysalis. Cluster lifecycle is cryptographically auditable.
  8. 08
    Workload boundary
    Maestro manages clusters. Workloads deploy through standard K8s APIs. No Maestro-specific manifest or chart format required.
python 
import maestro

client = maestro.Client(seat_token='your_seat_token')

# List clusters by plane type with capacity
for cluster in client.clusters.list(plane_type='mi350p'):
    print(f"Cluster: {cluster.id}, POP: {cluster.pop_id}, Profile: {cluster.profile_version}, Seats: {cluster.available_seats}")

# Provision a namespace for a new seat
ns = client.namespaces.provision(
    cluster_id='ord-t1-01-mi350p-cluster-01',
    seat_id='seat-12345',
    resource_quota='standard'
)
print(f"Namespace: {ns.name}, ResourceQuota: {ns.quota}, Status: {ns.status}")
05 / Proof

Proven at scale. Not in a lab.

Parinita AI Edge is the production deployment of the Parinita platform and the largest heterogeneous AI infrastructure deployment in the United States.

Reference Deployment

Parinita AI Edge

The most complex heterogeneous AI infrastructure in the United States. 101 sites, 9 planes, 12,000+ nodes, 4 accelerator vendors, dual network fabrics, four-layer tenant isolation — all through a single sovereign control plane.

101
Points of Presence
4 tiers: T1 (32), T2 (29), T3 (19), T4 (21)
909+
K8s Clusters
101 sites x 9+ plane types
12K+
Compute Nodes
Supermicro, Dell, Ampere, Cachengo
4
Accelerator Vendors
Intel Habana, NVIDIA, AMD, Qualcomm

Network & Security Infrastructure

2,491+
Cisco Switches
+ 303 routers (EVPN-VXLAN)
1,734+
Arista Switches
Lossless GPU backend fabric
367+
Palo Alto Firewalls
PA-5580/PA-5560 series
152+
Petabytes Storage
NVMe over RDMA
  • Multi-vendor accelerators
    Four accelerator vendors — Intel Habana, NVIDIA, AMD, Qualcomm — orchestrated through one control plane with unified scheduling, monitoring, and lifecycle management.
  • Dual-fabric networking
    Cisco production fabric and Arista GPU backend fabric operating as a coordinated system, bridged by identity-aware routing.
  • Nationwide scale
    101 sites across 42 U.S. states, each operating autonomously with a local control agent and a sovereign cross-site routing plane.
  • Multi-tenant isolation
    Four-layer defense-in-depth: VXLAN VNIs, identity-routing, Palo Alto firewalls, and Cilium eBPF — validated across every plane and site.
  • Compliance readiness
    FIPS 140-2 at launch, with FedRAMP Moderate, CJIS, and IL4/IL5 certification paths active through Parinita compliance profiles.
  • Sub-millisecond routing
    Every request classified and dispatched in under 1ms, enabling real-time SLA enforcement without perceptible overhead.
06 / Use Cases

Built for Kubernetes fleets where per-silicon drift is a recurring incident source.

Maestro manages clusters — your manifests, charts, and operators deploy through standard K8s APIs as usual. Maestro adds per-plane profile management, zero-touch bootstrap, and self-healing on top.

Multi-silicon operators who want progressive rollouts of cluster-level changes (not just workloads) with built-in rollback — a capability standard K8s management tools do not provide.

  • Multi-Silicon AI Edge

    Maestro is purpose-built for heterogeneous silicon — MI350P, NVIDIA Blackwell, EPYC Turin, AmpereOne all under one cluster management platform with per-plane profiles.

  • Large Kubernetes Fleets

    909+ clusters across 101 POPs maintain version coherence and profile compliance through Maestro's coordinated rollout model — no per-cluster manual coordination.

  • Zero-Touch Infrastructure Operations

    Node replacement, cert rotation, etcd operations, and profile drift correction all run autonomously. The operational burden of 909+ clusters does not scale linearly with the fleet.

  • Progressive Cluster Updates

    Canary rollout for cluster-level changes (profile updates, Kubernetes version, GPU device plugin changes) with built-in rollback — a capability no standard K8s management tool provides.

  • Air-Gapped and Sovereign Deployments

    Maestro's control plane runs on the infrastructure it manages — no external cloud dependency for cluster lifecycle operations. Works in air-gapped and sovereign environments by architecture.

07 / Getting Started

Deployment Models

Maestro's control plane runs on the infrastructure it manages. At a regional outage, local Maestro keeps reconciling its assigned clusters.

Node Registration
Zero-Touch Bootstrap

Instrument node registration triggers Maestro automatically. Silicon ID validation, profile selection, RKE2 bootstrap, device plugin install, registry join — under 12 minutes total.

Cluster Profile Updates
Canary Then Fleet

Profile version changes deploy canary to one cluster first. After validation, staged fan-out to all clusters of that plane type. Automatic rollback on any profile validation failure.

Continuous Self-Healing
Autonomous Cluster Operations

Node replacement, cert rotation, etcd snapshot/restore, and drift detection run continuously without human approval. 909+ clusters operate with Maestro — not with 909 operators.

08 / Get Started

Talk to Us

Our infrastructure team can walk through Maestro's per-plane cluster profile design and zero-touch bootstrap process for your specific silicon mix.

09 / Related

More from the fabric.

Products that compose with this one. Every Parinita product runs on the same 9-Plane Architecture across 101 edge POPs.