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Middleware, a startup that uses artificial intelligence (AI) to simplify and enhance cloud observability, announced today that it has raised $6.5 million in a seed round led by 8VC. The company plans to use the new funds to grow its team, develop new features and acquire more customers. The company also intends to create an advanced AI advisor that uses generative AI to improve the cloud observability stack.
The seed round also included Fin Capital, Vercel CEO and founder Guillermo Rauch, and Tokyo Black, as well as several notable angel investors and other funds such as Decent Capital, Begin Capital, Beat Venture and Gokul Rajaram.
“We’re seeing an explosion of microservices, Kubernetes and distributed systems as more applications move to the cloud,” said Middleware CEO Laduram Vishnoi in an interview with VentureBeat. “These new architectures generate much more monitoring data than traditional systems. Our AI agents can quickly analyze this flood of data to pinpoint problems and their root causes.”
Middleware’s platform collects data from various sources and applies machine learning algorithms to detect patterns and anomalies that indicate performance issues and other problems. The platform also can suggest solutions for how to fix issues and automate the resolution process.
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The observability market has undergone dramatic shifts in recent years, as companies seek faster and more cost-effective debugging. However, real-time system behavior, which is essential for problem-solving, has become harder to understand due to the increasing use of microservices and distributed systems. That’s why more and more businesses are turning to automation that can monitor distributed architecture and enable deep-dive tracking and real-time observability.
AI-driven observability in cloud systems
The recent funding comes on the heels of Middleware’s graduation from Y Combinator’s Winter 2023 batch. The company plans to double its team size from 25 to 50 within the next year and is targeting a series A funding round by mid-2024.
“Right now, our plan is to make sure we provide end-to-end observability — from data collection to data storage to data streaming. Plus we want to do log metric trace events, synthetic monitoring, real-time user monitoring, and browser monitoring,” Vishnoi emphasized.
As the demand for microservices and Kubernetes continues to rise, Vishnoi sees a significant opportunity for Middleware. He cited Gartner’s prediction that 95% of systems will be cloud-native by 2025 and acknowledged the challenge legacy companies face in transitioning to these new technologies.
“Companies are moving to using microservices, and our core focus is to enable our users to debug the issue[s] inside the microservices,” Vishnoi said. “The biggest challenge in this is [that] legacy companies are still not moving; they’re taking time to move.”
Middleware has developed its monitoring platform from scratch, avoiding integration with existing tools. Its real-time AI agents install inside cloud infrastructure to analyze performance issues. The company then aggregates the telemetry data and uses large language models to generate human-like recommendations for troubleshooting problems and minimizing downtime.
“Our go-to-market strategy is to use a product-led growth model, offering a free version of our platform that can handle more data than the competitors,” Vishnoi said. “We also have a premium version that offers more features and support.”
With this recent round of funding, Middleware is well positioned to meet the growing demand for advanced, AI-driven observability tools. As more companies transition to cloud-native systems and microservices, Middleware’s tools could become increasingly critical for businesses navigating this new market landscape.
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