Prequel, the community-driven problem detection and management platform for cloud applications, emerged from stealth announcing it has raised $3.3 million in seed funding. The round was led by Work-Bench, with participation from Runtime Ventures and Operator Partners.
Founders of leading category-defining infrastructure companies joined the round: Shay Banon (CTO, Elastic), Jon Oberheide (CTO, Duo Security), Monica Sarbu (CEO, Xata), and Andrew Morris (CTO, GreyNoise).
Engineering teams are under pressure to accelerate development and optimize resources, while delivering reliable services. But problems introduced by internal developers, issues in open source dependencies, and service misconfigurations frequently block releases, lead to incidents, and increase cloud spend by 30%.
Today, thousands of avoidable problems go unresolved until end customers are impacted because accurate identification and mitigation requires specialized engineering knowledge and time-consuming manual analysis. As AI-driven development increases software output, engineers have more problems to manage and less expertise in the code.
Prequel was founded by Lyndon Brown and Tony Meehan, former engineering leaders from Elastic, Mandiant (acquired by Google), and the National Security Agency (NSA), to close this gap by bringing the practices of detection engineering and global intelligence to reliability.
The Prequel Platform
Prequel is the first problem detection and management platform for cloud applications. Conventional monitoring approaches rely on observability solutions that trigger noisy threshold or anomaly-based alerts in response to general symptoms like high latency or errors. Prequel flips the model by using deterministic detections to precisely flag underlying failure conditions from the bottom up in production, staging, or development environments. Each identified problem is mapped to current impact with clear mitigation steps.
The backbone of Prequel is a patent-pending AI-enhanced in-cluster detection engine purpose-built for reliability. It is capable of efficiently running thousands of real-time detections per second against a continuous stream of low-level telemetry, without raw data leaving a customer’s cluster. This is in stark contrast to observability products that rely on collecting, moving, and storing gigabytes of costly telemetry, 90% of which is never used.
Already deployed at Fortune 500 companies, Prequel serves as a first-line of defense improving the reliability of high-availability fintech, infrastructure, and SaaS applications. The platform proactively takes on tedious problem detection and analysis work, enabling site reliability engineers (SREs) and software engineers to shift valuable time from firefighting to feature development and future-proofing. Early Prequel users have reported a 37% increase in engineering velocity.
“Prequel independently flagged issues that were the underlying cause of complex bug reports. We’re able to catch problems earlier and save time on troubleshooting.” Andy Martin, Director, Infrastructure, Schrödinger, a leading provider of software solutions for the life and material sciences industries.
Prequel is engineered to provide immediate value without requiring developers to modify their code. Customers install Prequel by running a single command. Once installed, the platform automatically discovers and instruments workloads, running a dynamic set of complex problem detectors in real-time against traces, metrics, logs, Kubernetes events, cpu, memory profiles, and more.
Also Read: Rackspace Launches Adaptive Cloud Manager for Growth
Community-Driven Reliability Intelligence
Prequel is powered by reliability intelligence (RI), a continuous feed of global failure and problem detection knowledge. To achieve this, Prequel pioneered a number of industry firsts. At the core is the Prequel Reliability Research Team (PRRT), bolstered by former NSA bug hunters, who proactively analyze hundreds of open source projects to identify failure patterns. These findings, along with common developer-introduced problems, are encoded as machine-readable Common Reliability Enumerations (CREs), with the support of Prequel’s proprietary retrieval-augmented generation (RAG) models.
Earlier this year, Prequel launched the first open problem detection community, detect.sh, where reliability engineers exchange and contribute detection techniques. Prequel customers can augment the growing list of out-of-the box and community detections with their own custom detection rules, optionally sharing these failure fingerprints with the world.
“Prequel’s technology uniquely enables reliability outcomes, helping companies confidently accelerate releases while avoiding outages and optimizing CPU and GPU utilization. In the same way enterprises lean on community-driven intelligence to root out security vulnerabilities and malware, they can now rely on Prequel to deliver the equivalent outcomes for reliability,” said Kelley Mak, General Partner at Work-Bench.
“Engineering teams are stuck in an endless cycle of reactive firefighting. We lived this firsthand and leaned on our cyber security experience to define a better way.” said Lyndon Brown, co-founder and CEO of Prequel. “We are thrilled to have the trust of customers and the support of leading investors and proven founders.”
“There is no way for individual engineering teams to keep up with all the ways software can break. It is as challenging to precisely pinpoint that one of these specific failures is happening to you. Prequel continuously gives every team access to the world’s best deterministic techniques for finding problems and precise recommendations on how to fix them,” said Tony Meehan, co-founder and CTO of Prequel.
Source: PRWeb
