AppGlass
We handle production so you can focus on shipping.
AppGlass detects, prioritizes, investigates, and resolves incidents - using only the permissions you grant.
Igor GerasimovJetBrainsEngineering ManagerAppGlass helps us investigate on-call incidents far more accurately by showing what's actually happening in production. It gives us the runtime context logs and traces miss, and it saves hours on complex production issues.
David KunzlerMineral Minds Deutschland GmbHHead of Software EngineeringAppGlass lets us inspect production Java apps without blocking them - a lifesaver when logs don't tell the whole story. It feels like debugging locally, minus the fear of freezing a live environment with real users. And because it never opens a port into production, security and compliance were a non-issue.
Stephanie MillerGeneral CodeSoftware ArchitectWe have used AppGlass 20 or so times to fix real issues we were having in our various environments that would have been time consuming and difficult to recreate locally. It is definitely a good tool.
100+ integrations are ready to use. No need to build your own.
AppGlassPlatform team ProductionExample workspaceConnect once. Context is ready for every investigation.
AppGlass brings source code and recent changes into the investigation without a custom connector.
AppGlass combines incident history with live evidence from its debugger and profiler to improve future investigations.
AppGlassPlatform team ProductionExample workspacePast incidents and live runtime data inform the next analysis.
Evidence and investigation context are being captured for checkout-service.
Reuse the evidence, previous analysis, and recovery steps. Check them against the current incident before drawing conclusions.
Inspect live values and execution paths in the running application.
See hot methods, call stacks, and where the application spends time.
Choose on-premises or SaaS. Control what AI does independently, what needs approval, and when your team takes over.
AppGlassPlatform team ProductionExample workspaceChoose your deployment and the scope of AI remediation.
Keep incident data in infrastructure you control.
Example policy / checkout-service
AI prepares the action. Your team approves it.
No remediation has been executed in this example yet.
Guided setup included. Choose the first use case and agree on service coverage before your pilot.
Pay with AppGlass tokens based on AI usage.
Actual cost varies with investigation complexity.
A fixed monthly price tailored to your company.
Service coverage and usage agreed upfront.
Service coverage is agreed before your pilot.
AppGlass monitors your metrics and helps investigate production incidents. It connects observability signals, source code, team context, and live runtime evidence to assess possible causes.
AppGlass analyzes your on-call investigations and incident reviews, then automatically creates postmortems and runbooks. These capture what happened, what helped, and what to check during a similar incident.
Yes, for selected incidents and actions you have permitted. You configure the AI’s access and action boundaries. When an action requires approval or falls outside that scope, AppGlass brings it to your team.
No. Knowledge and permissions are separate. A generated runbook can inform the response, but the AI’s actions still depend on the permissions you configure.
You choose an on-premises deployment in infrastructure you control or AppGlass-hosted SaaS. You also configure which actions AI may take, which require approval, and when it must escalate.
AppGlass has 100 ready-to-use integrations for repositories, logs, observability signals, and team context. Connect your existing sources without building custom integration code.
Take a relaxed look at AppGlass and explore a first use case for your services. No commitment, no installation.
Book a Demo