Sentry announced the launch of Size Analysis, a new product designed to help mobile development teams monitor, understand, and reduce the size of their iOS and Android applications.
Size Analysis gives teams clear visibility into how source code and resources contribute to overall app size, paired with automation to catch regressions early. Developers can set size thresholds and receive alerts on pull requests, visualize detailed size breakdowns, and get automated recommendations — such as identifying duplicate files or unoptimized assets — to keep apps lean over time.
“Knowing total app size is one thing,” said Max Topolsky, product manager at Sentry. “Size Analysis goes further by showing what changed, why it matters and what to do about it, so teams can make informed tradeoffs instead of guessing.”
Sentry’s Size Analysis is built on technology from Emerge Tools, which Sentry acquired in May 2025. The Emerge Tools Size Analysis product is already trusted by companies including Tinder, Spotify and Square to manage mobile app size at scale. Sentry’s version builds on that foundation while integrating directly into Sentry’s broader developer workflow.
The launch reflects Sentry’s continued expansion into the pre-release phase of software development, helping teams catch issues earlier in the lifecycle rather than reacting after release.
“To deliver great mobile experiences at scale, teams need end-to-end visibility into quality across the entire app lifecycle,” said Milin Desai, CEO of Sentry. “Size Analysis strengthens Sentry’s mobile platform by helping teams address app size as a core quality concern, not an afterthought.”
Size Analysis integrates into existing CI and version control workflows, allowing teams to manage app size without slowing down development.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...