Category: Compute

The Betting and Gaming industry operates in one of the most heavily regulated environments in technology. Gaming operators and suppliers must demonstrate compliance with rigorous technical standards, many of which reference standards published by Gaming Laboratories International (GLI). The most widely referenced GLI standards for this industry are GLI-19 (Interactive Gaming Systems) and GLI-33 (Event Wagering Systems), establishing technical benchmarks for platform security, data integrity, player protection, and operational transparency.

In Part 1 of this series, we described how AWS security best practices, using the AWS Security Improvement Program (SIP) and the Security pillar of the AWS Well-Architected Framework, can help Betting and Gaming operators strengthen foundational cloud security, covering information security, identity and access management, audit and logging, network security, and multi-account strategy. Part 2 turns to gaming-specific controls and resilience: geolocation, random number generation, responsible gaming and anti-fraud, change management, and disaster recovery.

The most difficult engineering problem when employing AI for gaming isn’t selecting the correct model—it’s selecting the infrastructure that supports the functionality needed to create compelling user experiences. A generative non-player character (NPC) that forgets every conversation, an AI dungeon master that loses narrative context mid-quest, or an agentic loop that drops state across an AWS Lambda cold start all produce sub-par experiences for the same reason: no durable, globally consistent state storage layer.

In this post, we compare four AWS storage options for P4 depot volumes: Amazon Elastic Block Store (Amazon EBS), Amazon FSx for NetApp ONTAP, Amazon Elastic File System (Amazon EFS), and Amazon Simple Storage Service (Amazon S3) archive depots. We evaluate each against real-world game development workloads and provide a decision framework to help you choose.

Over 230 million Americans identify as gamers. Yet most will never buy a $500 console or a $1,500 PC, not because they don’t want to play, but because the cost of entry keeps climbing: hundreds (sometimes thousands) in hardware, hundreds of gigabytes in downloads, and frustrating maintenance just to keep things running.

For developers and publishers, the gap is even wider. Building a great game is only half the battle; without a way for players to experience it firsthand, building a player base relies on secondhand impressions. While trailers generate hype and reviews build credibility, neither can replicate the engagement of letting players experience the game for themselves.

In this post, we discuss some of the differences between the two hosting options and cover how to migrate from an Agones-based implementation to Amazon GameLift Servers.

Learn how for one games studio, their operations team spend 60% of their time context-switching between AWS Console interfaces and troubleshooting capacity issues during launch events. During one major content release, manual scaling decisions led to a 2-hour queue time spike that resulted in 12% player churn.

In this post, we walk through how we built an autonomous QA agent on Amazon Bedrock that connects to a running Unity game and executes test scenarios without human intervention. We cover connecting to a game’s internal object hierarchy, a perception and reasoning loop inspired by recent research on autonomous testing agents, and game-agnostic tools that extend as complexity increases. The approach was validated against a turn-based mobile strategy game built in Unity.

This is the first post in a series that will discuss the foundational data workloads: player profiles, inventory, session management, and content configuration. Part 2 addresses live operations and social features: leaderboards, matchmaking, chat, social graph, and game analytics.