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Embrace is Now a Unity Verified Solution

Embrace is now a Unity Verified Solution.

The Embrace SDK has been fully vetted by Unity to ensure rigorous technical quality and compatibility standards. Unity developers will have access to Embrace's powerful performance monitoring SDK for their mobile games via a verified listing in the Unity Asset Store.

Embrace helps improve the speed, stability, and performance of mobile games at every stage of development. With complete, unsampled data, engineers using Embrace get the full context to solve any issue, whether it's a crash, slowdown, error, ANR, network failure, or more.

Embrace is designed for early discovery, enabling developers to catch performance risks as soon as they impact even a handful of users. By capturing every technical event that occurs within each and every player session, engineers can quickly resolve issues with relevant technical context.

Eric Futoran, Co-Founder and CEO of Embrace, said: "In mobile, every user counts. By capturing every technical event that occurs within a player session, teams can understand where and when their users encounter poor app experiences. Resolving issues quickly helps teams focus on what they love to do – build amazing games."

Whether pre-launch, soft launch, or in production making feature updates, Embrace provides mobile-specific tooling for engineers such as:

- Complete user session timelines: Recreate every user experience with the full technical and behavioral details in a play-by-play format that takes the guesswork out of understanding how a user experienced an issue. Get to the root cause of any issue, affecting any player, without tedious manual reproduction steps.

- ANR and crash resolution: Surface and resolve high priority ANRs and crashes that impact app rankings and discoverability. See Google Play Console ANR data directly within Embrace, combined with thread profiles captured whenever a game begins to enter an ANR state, with powerful flame graphs for faster prioritization and resolution.

- Dedicated Unity exceptions reporting: Automatically capture both native-layer and Unity-layer exceptions, with stack traces in native code and C#. View exceptions thrown on background threads in addition to the main thread for faster identification of the root causes of crashes and exceptions.

- Automatic network request logging: Get the client-side performance of every single network call to gain insight that server-side monitoring just can't see. Discover when third-party SDKs (like ad SDKs) are causing slowdowns and freezes that impact gameplay and cause users to abandon game sessions.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

Embrace is Now a Unity Verified Solution

Embrace is now a Unity Verified Solution.

The Embrace SDK has been fully vetted by Unity to ensure rigorous technical quality and compatibility standards. Unity developers will have access to Embrace's powerful performance monitoring SDK for their mobile games via a verified listing in the Unity Asset Store.

Embrace helps improve the speed, stability, and performance of mobile games at every stage of development. With complete, unsampled data, engineers using Embrace get the full context to solve any issue, whether it's a crash, slowdown, error, ANR, network failure, or more.

Embrace is designed for early discovery, enabling developers to catch performance risks as soon as they impact even a handful of users. By capturing every technical event that occurs within each and every player session, engineers can quickly resolve issues with relevant technical context.

Eric Futoran, Co-Founder and CEO of Embrace, said: "In mobile, every user counts. By capturing every technical event that occurs within a player session, teams can understand where and when their users encounter poor app experiences. Resolving issues quickly helps teams focus on what they love to do – build amazing games."

Whether pre-launch, soft launch, or in production making feature updates, Embrace provides mobile-specific tooling for engineers such as:

- Complete user session timelines: Recreate every user experience with the full technical and behavioral details in a play-by-play format that takes the guesswork out of understanding how a user experienced an issue. Get to the root cause of any issue, affecting any player, without tedious manual reproduction steps.

- ANR and crash resolution: Surface and resolve high priority ANRs and crashes that impact app rankings and discoverability. See Google Play Console ANR data directly within Embrace, combined with thread profiles captured whenever a game begins to enter an ANR state, with powerful flame graphs for faster prioritization and resolution.

- Dedicated Unity exceptions reporting: Automatically capture both native-layer and Unity-layer exceptions, with stack traces in native code and C#. View exceptions thrown on background threads in addition to the main thread for faster identification of the root causes of crashes and exceptions.

- Automatic network request logging: Get the client-side performance of every single network call to gain insight that server-side monitoring just can't see. Discover when third-party SDKs (like ad SDKs) are causing slowdowns and freezes that impact gameplay and cause users to abandon game sessions.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...