Your details

So we can follow up with the right team.

First Name

Last Name

Email

Phone Number

Company

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Company type ^*

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Company name ^*

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Your role ^*

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Primary region(s) of operation ^*

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Which edge locations are in scope? ^*

Cell sites / towers

Central offices / exchanges

MEC (Multi-access Edge Computing) sites

Regional edge data centers

Core data centers

Customer premises (CPE)

Other

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Approx. number of edge sites targeted in the next 12–24 months

Enter a rough estimate (e.g., 50, 500, 5000).

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Primary AI/ML workloads you want to run at the edge ^*

Video analytics / computer vision

Network optimization / RAN intelligence

Predictive maintenance

Fraud detection / security analytics

AR/VR / low-latency inference

IoT analytics

Generative AI inference (LLMs)

Data pre-processing / feature extraction

Other

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Describe your top 1–2 edge AI use cases

Include latency, data locality, and scale expectations if known.

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Target end-to-end latency requirement for key workloads ^*

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Typical inference model size (if known)

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Compute accelerators required at the edge

NVIDIA GPUs

AMD GPUs

Intel GPUs

TPUs

NPUs / AI ASICs

CPU-only

Not sure yet

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Which edge hardware form factors are you planning for?

Ruggedized servers

Standard 1U/2U servers

Micro data center racks

CPE / appliance

Bare metal at colocation

Other

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Connectivity/backhaul constraints that matter most

Intermittent connectivity

Limited bandwidth

High packet loss / jitter

Strict cost constraints

Need local autonomy during outages

Other

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Data sources used for edge AI

RAN / network telemetry

Core network telemetry

OSS/BSS data

Video feeds

IoT sensors

User device data (aggregated/anonymized)

Third-party data

Other

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Data locality & privacy requirements ^*

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Compliance / standards relevant to your deployment

3GPP

ETSI MEC

ISO 27001

SOC 2

GDPR

HIPAA

PCI DSS

NIST 800-53

Other

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Preferred deployment model for edge AI workloads ^*

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How important are the following requirements? ^*

	Not important	Nice to have	Important	Critical
Centralized fleet management across thousands of sites

Offline/air-gapped capability

GitOps-based deployment and drift control

Multi-cluster networking / service connectivity

Multi-tenancy and strong isolation

Edge-aware observability (logs/metrics/traces)

Policy enforcement (OPA/Gatekeeper, etc.)

Secrets management and key rotation

GPU scheduling and resource isolation

Secure boot / attestation / device identity

Automated upgrades with minimal downtime

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Observability & operations requirements

Single pane of glass across sites

SLO/SLA reporting

Distributed tracing

Edge device health monitoring

Automated remediation/runbooks

Capacity planning

Cost/chargeback reporting

Integration with existing NOC tools

Other

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AI/ML lifecycle needs

CI/CD for models

Canary deployments

A/B testing

Feature store integration

Model registry integration

Drift detection

Edge retraining / federated learning

Other

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Integration needs

OSS/BSS integration

RAN Intelligent Controller (RIC) / xApps/rApps

ETSI MEC APIs

Kafka / streaming

Service mesh (Istio/Linkerd)

API gateway

Identity provider (Okta/Azure AD/etc.)

Other

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Where are you in your edge AI journey? ^*

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Target timeline to deploy or expand edge AI ^*

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Decision-making stage for platform selection ^*

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How do you want to evaluate Stack8s?

Technical demo

Architecture workshop

Proof of concept (PoC)

Pricing/packaging discussion

Security review

Reference customers

Other

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What does success look like for you?

e.g., deployment speed, reduced outages, lower cost, compliance, performance.

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Anything else we should know?

/

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Thanks for sharing your requirements

We’ll review your responses and follow up with next steps for a stack8s k8s based control plane for edge and distributed use cases.

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Distributed AI Edge Requirements Survey

How important are the following requirements? ^*

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Distributed AI Edge Requirements Survey

How important are the following requirements? *

Thanks for sharing your requirements

How important are the following requirements? ^*