Opsera announced a Series A Plus round of $12 million in funding. The latest round of funding will support Hummingbird AI, a new generative AI initiative, and accelerate the growth of Opsera – Hummingbird AI will introduce new and exciting features into the DevOps and DevSecOps categories.

The latest funding round is led by Taiwania Capital with support from existing investors Felicis Ventures, Clear Ventures, and others.

The Opsera Unified DevOps Platform, powered by Hummingbird AI, includes AI-driven unified insights to help improve developer experience and productivity, help identify bottlenecks and inefficiencies and provide remediations with the click of a button. Another feature within Hummingbird AI will empower enterprise developers to accelerate innovation by deploying large language models (LLM) across multiple clouds seamlessly using a templated approach. Hummingbird AI will bring compliance around security and quality assurance with bias scoring and cost management within LLMs.

The new Hummingbird AI capabilities within Opsera’s DevOps platform combine generative AI and MLOps technology to allow engineers to:

• Improve Organizational Efficiency, Security, and Quality through prompt-based recommendations, analysis, remediations and insights driven by the entire organization’s software development lifecycle data and toolchain.
• Leverage the Best-in-Class Opsera Platform Capabilities for MLOps Orchestration, making deployments faster, smarter and more secure across multi-cloud solutions. Gain visibility across efficacy scoring and bias detection.
• Embrace Holistic Security and Observability with security score cards, remediation efforts and recommendations to improve the organization’s overall security and quality posture.
• Gain AI-powered Critical Intelligence and Actionable Insights including DORA metrics, developer productivity and experience insights, and security and quality metrics.
• Launch Developer-Friendly, Automated Workflows across SDLC, SaaS, IaC, and COTS applications.
• Pipeline Summary and Remediations for each pipeline to improve the process and recover from the failures with reduced Mean Time to identification for issues/anomalies.