Sentry announced it has acquired Emerge Tools, a prominent provider of mobile app development solutions.
Emerge Tools enables teams to deliver smaller, faster and more reliable mobile apps. Its platform powers visual regression testing at OpenAI and app size monitoring at Spotify.
Emerge Tools gives developers the ability to proactively identify and resolve performance bottlenecks, optimize app size, and maintain a consistent user experience.
“Emerge Tools is a leader in mobile development, with products used by some of the biggest apps in the world,” said Milin Desai, CEO of Sentry. “The rise of AI is reshaping how software is built and shipped and the need for quality tooling has never been greater, especially for mobile. Adding Emerge’s products and team will help Sentry continue to build the best possible platform for mobile developers around the world.”
Sentry, which is well known for error, crash reporting, and tracing tools will integrate the Emerge Tools product line into their application monitoring platform – creating a complete mobile app monitoring solution. Sentry customers will now be able to connect the inefficiencies surfaced by Emerge Tools down to the line of code causing lag, long load times, or unresponsive UI elements. All resulting in a smoother and more enjoyable app experience for users.
By leveraging Sentry and Emerge Tools, mobile development teams can:
- Significantly improve app performance and responsiveness.
- Reduce app size, resulting in higher install rates and user satisfaction.
- Enhance UI stability and prevent visual regressions.
- Streamline development workflows and reduce debugging time.
- Deliver superior mobile experiences that drive user engagement and retention.
This is the fourth major acquisition for Sentry, having acquired Specto in November 2021, Codecov in November 2022, and Syntax in April 2023.
Emerge Tools was founded in late 2020 by Josh Cohenzadeh & Noah Martin. They raised funding from Y Combinator, Haystack, Upside Partnership, Liquid 2 Ventures, and Matrix Partners.
The Latest
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...