What would your MSP look like if AI handled the alerts, predicted the capacity crunch, and powered a whole new line of customer services? The AWS MSP AI Hackathon is your chance to build it, and to show AWS and the partner community what an AI-first MSP looks like in practice.
Build production-ready AI solutions with AWS AI Services such as: Amazon Bedrock, Amazon Bedrock AgentCore, Strands Agents, Amazon Quick, Kiro, the AWS Frontier Agents, etc. (See the full list on the FAQs) Use AI to run your MSP better, or to deliver new AI-driven services your customers can’t get anywhere else. We’re not looking for proofs of concept. We want real solutions that deliver measurable impact and scale across MSP environments.
Why You Should Participate
- Build What’s Next for Your MSP: Get hands-on with AWS’s latest agentic AI services and turn them into solutions your team and customers can use.
- Solve Real Problems: Tackle the challenges MSPs face every day, from incident response and FinOps to customer-facing AI services.
- Win Big: Compete for $64,000 in cash prizes across two tracks and 10 Special Prizes.
- Get Your Solution in Front of AWS: Track winners earn a spot in the MSP AI Use Case Library, priority consideration for AWS go-to-market support, and white-glove enablement for a potential AWS Marketplace listing.
- Get Recognized: Standout solutions may be highlighted at the AWS Global Partner Summit.
Get started
- Confirm Your Eligibility: Make sure your organization is an MSP partner or part of the AWS Partner Network. (Click here for full eligibility requirements)
- Register and Join the Community: Sign up on Devpost and join the hackathon Discord to connect with AWS experts and other partners.
- Claim Your Credits: Request $200 in AWS credits for your AWS account. One set of credits per AWS Account ID. Credits are available while supplies last. Click here to apply.
- Pick Your Track: Review the examples on the Resources page, choose Track 1 or Track 2, and start building!
Requirements
What to Build
Build a working, publicly hosted, production-ready AI solution using at least one of the required AWS AI services: (See FAQs for a full list of services).
Choose A Track
Your solution must fit one of two tracks:
- Track 1: Run Your MSP Better
Show how AI improves your internal MSP operations with measurable efficiency gains. - Track 2: Deliver AI-Driven Outcomes for Customers
Show how you deliver differentiated, AI-powered services to your end customers.
Check the Resources for examples of each track
Bringing an existing project?
It’s welcome as long as it is significantly updated during the submission period. Just tell us what you changed in your submission.
What to Submit
- Track Selection: Choose Track 1 or Track 2.
- AWS AI Service: Identify the AWS AI Service used to build your project.
- Text Description: Explain your solution’s features, the problem it solves, and the measurable business outcomes it delivers.
- Deployed URL: A link to your publicly hosted, working solution.
- Code Repository URL: A link to your repo on GitHub or GitLab with all the source code, assets, and instructions needed to run it. The repo must show the required AWS AI services being used in your code. It must be private and shared with [email protected] and [email protected].
- Demo Video: About 3 minutes, showing your solution working end to end. Upload it to YouTube and make it public
- Proof of production readiness: Provide both Architecture Diagram and High Level Design Doc (HLD)
Prizes
Track 1: Run Your MSP Better (1st Place)
Inclusion in the MSP AI Use Case Library (published reference architectures)
Priority consideration for AWS GTM support and partner marketing
White-glove enablement for potential AWS Marketplace listing (where applicable)
Track 1: Run Your MSP Better (2nd Place)
Inclusion in the MSP AI Use Case Library (published reference architectures)
Priority consideration for AWS GTM support and partner marketing
White-glove enablement for potential AWS Marketplace listing (where applicable)
Track 1: Run Your MSP Better (3rd Place)
Inclusion in the MSP AI Use Case Library (published reference architectures)
Priority consideration for AWS GTM support and partner marketing
White-glove enablement for potential AWS Marketplace listing (where applicable)
Track 2: Deliver AI-Driven Outcomes for Customers (1st Place)
Inclusion in the MSP AI Use Case Library (published reference architectures)
Priority consideration for AWS GTM support and partner marketing
White-glove enablement for potential AWS Marketplace listing (where applicable)
Track 2: Deliver AI-Driven Outcomes for Customers (2nd Place)
Inclusion in the MSP AI Use Case Library (published reference architectures)
Priority consideration for AWS GTM support and partner marketing
White-glove enablement for potential AWS Marketplace listing (where applicable)
Track 2: Deliver AI-Driven Outcomes for Customers (3rd Place)
Inclusion in the MSP AI Use Case Library (published reference architectures)
Priority consideration for AWS GTM support and partner marketing
White-glove enablement for potential AWS Marketplace listing (where applicable)
Special Prize: Best Amazon Bedrock AgentCore Implementation (Track 1)
Special Prize: Best Amazon Bedrock AgentCore Implementation (Track 2)
Special Prize: Best Amazon Bedrock Application (Track 1)
Special Prize: Best Amazon Bedrock Application (Track 2)
Special Prize: Best Amazon Quick Application (Track 1)
Special Prize: Best Amazon Quick Application (Track 2)
Special Prize: Best Strands SDK Implementation (Track 1)
Special Prize: Best Strands SDK Implementation (Track 2)
Special Prize: Best AWS Frontier Agents Application (Track 1)
Special Prize: Best AWS Frontier Agents Application (Track 2)
Devpost Achievements
Submitting to this hackathon could earn you:
Judges
Maria Adair
Judging Criteria
-
Technological Implementation
How well is the solution built? How deeply does it use the required AWS AI services? Is the AWS service a working, essential part of the solution rather than a surface-level add-on? Does the project show technical depth and production readiness? -
Design
Does the project deliver a complete product experience ready for production, not a proof of concept? Is it built to scale? Can it be reproduced across different partner and customer environments? -
Potential Impact
Does the project make a credible case for solving a real MSP problem, backed by measurable business outcomes? Does the solution actually deliver those outcomes based on what’s demonstrated? -
Innovation/Idea
Is this a creative use of AWS AI services in MSP operations or customer delivery? Is the approach novel, and does the project show a real understanding of the MSP problem space?
Questions? Email the hackathon manager
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