Spectro Cloud
by Spectro Cloud
Kubernetes and AI-factory management from bare metal to model, cloud to edge
Spectro Cloud's Palette is an enterprise Kubernetes management platform, and PaletteAI extends it into GPU and AI-factory operations. It gives platform, IT operations and AI infrastructure teams one place to design, deploy, patch and govern full-stack clusters across cloud, data center, bare metal and edge, replacing hand-assembled AI stacks with versioned, repeatable blueprints.
Spectro Cloud is a San Jose, California company founded in 2019. Its Palette platform manages the full Kubernetes lifecycle, from the operating system and Kubernetes distribution through networking, storage, add-ons and applications. It covers public clouds, VMware vSphere and Nutanix data centers, bare metal and edge devices, and runs VM and container workloads side by side. The core building block is the Cluster Profile, a versioned blueprint for everything in a cluster. The architecture is decentralized: management intelligence runs inside each cluster, so policy is enforced locally and upgrades roll out in parallel. The AI side arrived with PaletteAI, which became generally available on March 16, 2026. It launched with an ecosystem that builds in NVIDIA AI Enterprise and adds integrations with Supermicro, Canonical, WEKA, F5, ClearML and others. PaletteAI finds GPU hardware and deploys complete AI stacks from a curated catalog called PaletteAI Studio. Each stack is packaged as a Profile Bundle: infrastructure plus applications such as Run:ai and ClearML. It also offers GPU-as-a-Service and Model-as-a-Service to AI practitioners, within tenant quotas and role-based access controls. On July 21, 2026 the company added PaletteAI Inference Launchpad. It routes requests between local and frontier models, meters token usage and enforces governance and quota policies on NVIDIA or AMD GPUs. The vendor claims up to 70% lower token costs, depending on workload mix. Palette VerteX and PaletteAI VerteX are built for air-gapped, heavily regulated and sovereign-AI deployments, with FIPS 140-3 validated cryptography. The company closed a $75M Series C led by Growth Equity at Goldman Sachs Alternatives in November 2024. It also contributed Kairos, an operating system for edge Kubernetes that is now a CNCF Sandbox project, to open source. Customers it names include GE HealthCare, T-Mobile, Airbus and Yum! Brands.
The head of platform engineering or AI infrastructure who runs many Kubernetes clusters, or is building on-premises GPU capacity, across cloud, data center and edge. Palette manages the whole stack in all of those places.
One versioned, repeatable blueprint for the whole stack, from operating system to AI application, so cluster fleets and GPU clusters are deployed, patched and governed the same way everywhere.
At a Glance
- Category
- Infrastructure & Cloud
- Pricing
- Usage-based, Subscription, Contact for pricing
- Target Market
- CIOs, CTOs, Platform Engineering Teams, IT Operations, AI Infrastructure Teams
- Deployment
- Multi-cloud, Hybrid, Self-hosted
- Founded
- 2019
- Headquarters
- San Jose, California, USA
Key Features
- ✓Cluster Profiles
Versioned blueprints that cover the OS, Kubernetes distribution, networking, storage, add-ons and applications, so every cluster is built and upgraded the same way.
- ✓PaletteAI Studio and Profile Bundles
A curated catalog of validated AI stacks. Each bundle packages infrastructure plus applications such as Run:ai and ClearML so teams can redeploy it the same way every time instead of assembling it by hand.
- ✓GPU discovery and GPU-as-a-Service
Finds GPUs during setup, pools and allocates them under per-tenant quotas, and lets AI practitioners serve themselves within limits the platform team sets.
- ✓PaletteAI Inference Launchpad
Locally managed inference that routes requests between local and frontier models, meters token usage, and enforces governance and quota policies to control spend.
- ✓Decentralized management architecture
Management intelligence runs inside each cluster, so policy is enforced locally and upgrades roll out in parallel across large fleets.
- ✓VerteX editions for regulated environments
Palette VerteX and PaletteAI VerteX use FIPS 140-3 validated cryptography and are built for air-gapped, heavily regulated and sovereign-AI deployments.
- ✓Kairos open-source edge OS
Spectro Cloud contributed Kairos to open source. It is an Apache-2.0, CNCF Sandbox immutable Linux distribution for edge Kubernetes with atomic A/B upgrades.
Use Cases
- •Standing up an enterprise AI factory
Take GPU servers from bare metal to production AI stacks with NVIDIA AI Enterprise built in, using validated partner integrations instead of custom glue code.
- •Cutting inference token spend
Send suitable requests to locally hosted models through Inference Launchpad, metering tokens and enforcing quotas to reduce reliance on paid frontier model APIs.
- •Running large edge fleets
Run one standardized Kubernetes stack across thousands of sites; the vendor says Yum! Brands runs one edge stack across more than 40,000 restaurants.
- •Patching fleets at scale
Patch large cluster fleets quickly and consistently; the vendor says GE HealthCare patched all 100 of its clusters in about four hours.
- •Air-gapped and sovereign AI
Run AI and Kubernetes infrastructure in disconnected government or regulated environments with FIPS 140-3 validated cryptography, using the VerteX editions.
- •Consolidating VMs and containers
Run virtual machines and containers side by side on one Kubernetes-managed platform; the vendor also markets a PaletteAI VM Launchpad for migration.
Ideal For
Best For
- ✓Platform engineering teams operating many Kubernetes clusters across public cloud, data center and edge
- ✓Enterprises building on-premises GPU AI factories on NVIDIA or AMD hardware
- ✓Distributed edge deployments in retail, healthcare and telecom that need one standardized stack across many sites
- ✓Government, defense and regulated organizations that need air-gapped environments with FIPS 140-3 validated cryptography
Not Ideal For
- ✗Small teams running a handful of clusters on one managed cloud Kubernetes service. A commercial cluster manager's cost, and the steep learning curve reviewers report for advanced features, are hard to justify there.
- ✗Buyers who want published, self-serve list pricing for a GPU platform. PaletteAI is sold through sales as a flat fee per GPU, with no public rate card on the vendor site.
- ✗Teams that only need model APIs or an MLOps tool. PaletteAI runs the infrastructure underneath tools like ClearML and Run:ai rather than replacing them.
Deployment
Market Analysis
Pros
- ✓Reviewers single out the unified management dashboard and automated cluster lifecycle management as big time savers
- ✓Cluster Profiles with variables keep environments consistent and enforce security policy across clusters
- ✓Covers a wide range of environments: public cloud, vSphere, Nutanix, bare metal, edge and air-gapped sites
- ✓Validated partner ecosystem with NVIDIA AI Enterprise built into PaletteAI
- ✓SOC 2 and ISO 27001 certifications, plus SAML and OIDC single sign-on
Cons
- ✗Reviewers report a steep learning curve for advanced features
- ✗One reviewer notes Palette cannot do very specific routing
- ✗Not cheap: a Hacker News practitioner says a cluster manager like this 'isn't cheap', and AWS Marketplace committed tiers start at $4,000 per month
- ✗Enterprise and PaletteAI pricing requires contacting sales, and the free SaaS access has no published limits
- ✗The up-to-70% token saving from Inference Launchpad is a vendor claim that depends on workload mix
Pricing
Palette (cloud and virtualized)
Contact for pricing
- ✓Usage-based pricing in kilocore-hours (worker CPU cores multiplied by time)
- ✓Kubernetes lifecycle management across cloud IaaS, vSphere and edge
- ✓VM and container workloads
Palette on AWS Marketplace
From $4,000/mo
- ✓$4,000 per month for 100-499 kCh on a 1-month contract
- ✓$40 per kCh with no commitment
- ✓Up to 50% savings on 12-, 24- or 36-month contracts
Palette (edge and bare metal)
Contact for pricing
- ✓Per-node licensing based on server size
PaletteAI
Contact for pricing
- ✓Flat fee per GPU under management
- ✓PaletteAI Studio catalog and Profile Bundles
- ✓GPU-as-a-Service and Model-as-a-Service
Palette VerteX / PaletteAI VerteX
Contact for pricing
- ✓FIPS 140-3 validated cryptographic modules
- ✓Air-gapped self-hosted deployment
- ✓Premium support from US citizens in the continental USA
Palette is metered in kilocore-hours (worker CPU cores multiplied by time) for cloud and virtualized environments, and per node for edge and bare metal. PaletteAI costs a flat fee per GPU under management. The vendor site does not publish list prices. The AWS Marketplace listing shows 1-month tiers from $4,000 (100-499 kCh) to $200,000 (5,000+ kCh), $40 per kCh with no commitment, and up to 50% off on 12- to 36-month contracts. The vendor advertises free access to its SaaS platform but does not publish the limits.
Security & Compliance
Sources
This page was written from 11 sources, 5 on domains other than spectrocloud.com.
- 1.spectrocloud.com — spectrocloud.comvendor
- 2.spectrocloud.com — paletteaivendor
- 3.spectrocloud.com — editionsvendor
- 4.spectrocloud.com — spectro cloud announces expanded paletteai ecosystemvendor
- 5.spectrocloud.com — spectro cloud launches paletteai inference launchpadvendor
- 6.spectrocloud.com — free tiervendor
- 7.docs.spectrocloud.com — saml sso
- 8.am.gs.com — growth equity spectro cloud
- 9.aws.amazon.com — B08J8383Q5
- 10.hn.algolia.com — search
- 11.github.com — kairos
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