AWS Will Require AI Cost Observability and Evidence of Results From MSP Partners in 2027

September 10, 2026
DoiT PartnerOps now supports AWS MSP Program Validation Checklist 8.0, which adds requirements for AI observability, cost controls and customer outcomes.

Amazon Web Services is adding artificial intelligence requirements to the audit its managed service provider partners must pass to keep their status. MSP Program Validation Checklist 8.0 replaces the current version on January 1, 2027, according to DoiT, which said in a press release that it has built support for the new checklist.

The requirements cover observability for generative AI and agents, including token use and inference costs for each agent. They also require cost controls on AI workloads and measurable customer outcomes from AI services, meaning partners must show evidence at the customer level that AI delivered business results.

DoiT has built tooling for the new requirements. Channel Management, part of DoiT PartnerOps, assigns control owners, divides controls into tasks, reviews evidence, routes approvals and tracks compliance between audits. Other DoiT products provide billing records, customer cost reports, AI cost attribution, anomaly data and optimization evidence.

We hope you enjoyed this article.

Consider subscribing to one of our newsletters like Enterprise AI Brief, Silicon Brief or Daily AI Brief.

Also, consider following us on social media:

Enterprise AI AI Chips & Datacenters AI Brief AI Brief (X)

More from: Enterprise

09/10
Centric Software Unveils Centric AI for Product Decisions
09/10
Mid-American Conference Selects uReport for Content Creation
09/10
T. Rowe Price Expands Claude Across Investment Teams
09/10
Abacus.AI Releases Three Smaug Open Weight Models
09/10
Summation Opens AI Analysts to Business Teams

More from: Data Centers

09/10
CargoSense Acquires Adapt-IP for Wireless Asset Tracking
09/10
Massachusetts Sets Clean Power Rules for Large Data Centers
09/10
CATL Leads DeepCtrls Series B+ Funding Round
09/09
Innventure removes DarkNX AI data centre cooling project from bookings
09/09
Besxar Tests Semiconductor Manufacturing Canisters on SpaceX Rocket

Subscribe to Enterprise AI Brief

Weekly report on AI business applications, enterprise software releases, automation tools, and industry implementations.

AI Token Webinar

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You May Also Like