Monte Carlo
The first end-to-end Data Observability Platform for AI-ready data reliability.
The Enterprise Reliability Management platform to detect and fix risks before they become outages.
Gremlin is a leading reliability management platform that evolved from pioneered chaos engineering to a comprehensive suite for measuring and improving system resilience. By 2026, Gremlin has positioned itself as the 'Reliability-as-Code' standard, allowing organizations to automate the detection of systemic risks across multi-cloud and Kubernetes environments. The platform provides a unified Control Plane that orchestrates targeted fault injection—such as network latency, resource exhaustion, and state-change failures—to validate system health. Its 2026 architecture leverages AI-driven 'Reliability Scores' which map technical failure data directly to business KPIs. Gremlin allows SRE teams to run automated GameDays and integrate resilience testing directly into CI/CD pipelines, ensuring that every deployment is vetted for high availability. By integrating with major observability stacks like Datadog and New Relic, Gremlin creates a closed-loop system where failures are simulated, detected by monitors, and automatically mitigated before they impact end-users. This proactive approach transforms reliability from a reactive fire-fighting effort into a measurable, governed engineering discipline.
Automated system that monitors external observability metrics during an experiment; if a threshold is breached, the experiment is instantly rolled back.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A proprietary algorithm that calculates a 1-100 score for services based on passed/failed experiments and monitoring coverage.
Pre-built, complex failure chains (e.g., 'Availability Zone Outage') that mimic real-world historical outages.
Granular targeting allows users to specify exactly which containers, pods, or IP ranges are affected by an attack.
Defining reliability tests and thresholds within YAML files that reside in the application repository.
A safety-first mechanism that restores the system to its original state within seconds of a failure or manual abort.
Allows teams to schedule recurring experiments and coordinate multi-team resilience training sessions.
Ensuring that the cluster correctly redistributes pods when a worker node unexpectedly dies.
Registry Updated:2/7/2026
Verify that traffic remains uninterrupted via the Load Balancer.
Validating that traffic automatically reroutes to a secondary AWS region during a primary region outage.
Testing application behavior under heavy database latency to ensure circuit breakers trip correctly.