Your springboard to edge AI

Edge AI is one of the hottest trends in IT, and with good reason. It’s the key to putting transformative AI workloads where they can make the biggest difference. Here’s how to make edge AI a reality.

Edge AI platform

What is edge AI?

Edge AI is when you run applications powered by artificial intelligence on distributed edge computing infrastructure, instead of in a data center or cloud.


An edge computing endpoint may be a telco carrier’s base station, a connected car, a small form-factor server in a retail store, or a piece of medical equipment in a hospital.


What all these things have in common is that they are out in the world, close to users (whether they’re customers, patients or employees), and embedded in the front lines of business processes where data originates.

What is Edge AI
$
157
 billion
The edge AI market will double from $77 billion to $157 billion by 2030, according to specialist edge analysts STL Partners

Why edge is critical for AI workloads

AI applications will increasingly be deployed in edge locations in order to get closer to the data, the process and the user. This is essential for:

Performance

AI inferencing workloads usually runs in real-time and therefore can’t tolerate latency for data to be transmitted to the cloud for analysis and action.

Availability

Mission-critical AI workloads such as in connected cars must remain active even if the edge location loses connection to central clouds or data centers.

Cost

Video, audio and other data sources may be data intensive therefore too costly to move over the network, with cloud ingress, egress and storage costs on top.

Security

Data gathered for AI inference may be sensitive, meaning strong compliance limitations on moving the data over the network and into the cloud.

Solving your Kubernetes edge challenges

Enterprises, startups and government agencies are choosing Spectro Cloud Palette Edge to help them deliver their edge projects.

To run AI workloads, each edge location needs servers with an OS, Kubernetes, integrations such as security and observability tools, a model serving framework such as Kubeflow, and one or more AI models deployed and configured. Doing this manually, hundreds or thousands of times, is cost prohibitive — especially if you need an expert to visit each site in person.


Palette’s Edge AI makes it easy, with:

  • Repeatable ‘blueprints’ of the full AI and infrastructure stack, from OS to Kubeflow and Seldon, using Palette’s Cluster Profiles. You can enforce your ‘desired state’
  • Low- and no-touch provisioning of all kinds of edge devices, including Arm and x86 architectures, with direct bootstrapping of bare metal devices
  • Unique resilience and scalability with its patented decentralized architecture
deploy and manage Edge AI

Update your edge artificial intelligence models frequently without risk of downtime

Competitive innovation comes from non-stop incremental improvements in your AI models. How can you push new code daily, and roll back safely with full control? Palette’s EdgeAI enables you to:

  • Access AI model control via your own private repositories and from Hugging Face
  • Support safe experimentation with declarative, version-controlled, GitOps-able model configurations and A/B testing
  • Make controlled changes fast, with zero downtime rolling upgrades that you can apply to single locations, groups of locations, or even your entire state of thousands of edge devices if you need to — executed in parallel
  • Control risk with canary model deployments and easy rollback to previous model versions
  • Monitor performance across multiple edge locations with native observability dashboards
update edge AI models

Secure critical intellectual property and sensitive data with AI on the edge

Any edge computing has risks — and with edge AI, the stakes are high. The models themselves may be your critical IP, and they may be deployed in vulnerable edge locations that you don’t own. The data they gather and analyze may be sensitive and covered by compliance requirements.


Palette’s Edge AI solves for security with:

  • Strict standards compliance. Palette is available with FIPS cryptography across all elements of its architecture. Of course it’s also compliant with ISO 27001, SOC 2 and other standards, and has passed infosec audits with some of the most stringent organizations
  • Multi-layered security controls, from immutable OS images and persistent data encryption to verified secure boot, cluster hardening, SBOM and other security scans, and zero trust with granular RBAC. You can trust that every node that you bring online is trusted and secure.
  • Full deployment flexibility. You can build your clusters with your preferred OS, security integrations and versions. You can deploy clusters air-gapped and even deploy your own instance of Palette in an air-gapped environment.
securing Edge AI
We are CRN AI 100 company for 2025

CRN rated us one of the 20 hottest AI cloud companies in its 2025 CRN AI 100!

The choice of AI pioneers

Spectro Cloud Palette is already supporting innovative enterprises pioneering AI at the edge, including:

Rapid uses the power of AI and Palette Edge to diagnose serious medical conditions across thousands of hospitals

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“When it comes to deploying our applications securely and easily to the edge, we trust Spectro Cloud’s Palette”
Amit Phadnis
Chief Innovation and Technology Officer

GE HealthCare uses our powerful edge AI platform to manage clinical apps including AI image analysis across medical locations.

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Flying autonomous robots with computer vision for perfect fruit picking.

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2025 GigaOM Radar leader for Kubernetes for edge computing

See why GigaOm rated us a leader in Kubernetes for edge computing!

Analysts at GigaOm rated us ‘leader’ and ‘outperformer’ on the 2025 Radar Reports for Kubernetes for Edge Computing.

Take your next step

Unleash the full potential of Kubernetes at scale with Palette. Book a 1:1 demo with one of our experts today.



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