Logz.io announced support for the AWS for Games initiative from Amazon Web Services (AWS) to simplify the process of developing and running games for gaming industry observability customers.
AWS for Games is an initiative featuring services and solutions from AWS and AWS Partners, built specifically for Games customers. The initiative makes it easier for game developers, publishers, and platforms to select the right tools and partners to build, run, and grow their games. For customers looking to accelerate deployments with solution-specific support, AWS for Games also identifies dedicated AWS Game Tech industry specialists, AWS services, and leading AWS Partners in each solution area. Logz.io is available in five AWS regions, supporting integrations to over 38 AWS services.
Game developers are challenged when it comes to gathering deep telemetry data in their games. Milliseconds matter! How do you manage to keep your overhead low while minimizing data gaps to help you understand players’ in-game experience at scale? Logz.io’s cloud-native observability platform enables gaming industry engineers to use the best open source tools to monitor and troubleshoot their games and supporting infrastructure; allowing teams to understand how many servers, how many players, and on which platform are impacted at any given time. Companies including Unity and Mediatonic use Logz.io to increase productivity, reduce MTTR, and improve player experience.
Logz.io has helped Unity save valuable engineering time that was previously spent on manual and inefficient operational logging tasks. Moving to a centralized and hosted logging solution that offers proactive analysis tools, the Unity team is now able to gain more visibility into the data, identify and uncover issues that would otherwise have gone unnoticed, and ultimately, better differentiate between noise and signals. Using Logz.io, Unity can identify and troubleshoot issues faster, ultimately boosting development and facilitating faster delivery.
“Launching and running games is not easy. Some games scale from zero to millions of users in no time. By adding observability early in the design of a game, it can help teams improve time to identify and troubleshoot performance issues,” said Tomer Levy, CEO at Logz.io. “Combining our leading open-source observability platform with the services and solutions available through AWS for Games will help our mutual gaming customers tackle difficult challenges, from supporting game launches to handling customer issues when critical transactions fail.”
The Latest
Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...
Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...
When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...
If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...
Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...
AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...
Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...
Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...
Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...
In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...