
Middleware, an AI-based cloud observability platform provider, raised $6.5 million in seed funding to simplify and supercharge cloud observability.
The capital infusion will enable the company to revolutionize how businesses utilize observability stacks in the age of AI.
8VC led the round and was joined by Fin Capital, Vercel CEO and founder Guillermo Rauch and Tokyo Black. Additionally, several notable angel investors and other funds participated including Decent Capital, Begin Capital, Beat Venture and Gokul Rajaram.
The funding will enable Middleware to expand its team, develop new features and grow its customer base. The company also plans to build an advanced AI advisor based on generative AI to further improve the cloud observability stack.
"We are excited to have the support of all the investors as we continue to build out our platform and help our customers achieve greater visibility and control over their systems," said Laduram Vishnoi, CEO and founder of Middleware. "Our AI-based approach provides better insight into applications and infrastructure, making it easy for customers to debug issues faster and minimize downtime."
Middleware's cloud observability platform amalgamates data from various sources and leverages machine learning algorithms to identify patterns and anomalies that indicate performance issues and other problems. The platform also can provide recommendations for how to resolve issues and automate the resolution process.
Middleware's ultimate objective is to provide development and operations teams with effortless access to observability data throughout the entire software development lifecycle, reducing mean time to detection (MTTD) and mean time to resolution (MTTR).
"Our investment in Middleware reflects our confidence in its ability to deliver innovative cloud observability solutions that help development and operations teams identify and resolve issues quickly," said Bhaskar "BG" Ghosh, partner at 8VC. "Its AI-based approach is a game-changer for the industry, and we are excited to support the company's continued growth and success."
The Latest
As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...
Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...
The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...
Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...
Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...
If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...
Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...
APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...
APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 4 covers negative impacts of AI ...
APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 3 covers barriers and challenges for AI ...