Tsuga raises $35 mn Series A to expand AI observability platform - Beinsure

2026-06-25 · Source: Series A" OR "Series B" OR "Series C" AI startup via Google News · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

Tsuga, founded in 2024, secured €30 million in Series A funding to expand its go-to-market operations and accelerate the deployment of its AI-Native Resilient Observability platform. Led by Singular with participation from General Catalyst and Databricks Ventures, among others, the investment reflects continued interest in AI infrastructure. Tsuga's platform operates within customers' cloud environments, including Microsoft Azure, AWS, and Google Cloud, ensuring telemetry remains inside their security perimeter. This architecture contrasts with traditional vendors who store data externally and charge based on increasing volumes, a model Tsuga argues is unsustainable for AI workloads generating substantially larger telemetry. The platform processes complete datasets without sampling, offering automated root cause analysis and AI agents. Six months post-stealth, Tsuga reported several million dollars in contracted annual recurring revenue with six-figure average contract values, serving customers like Le Monde and Camunda. The funding will also support hiring and development of its Skills library and agent-building toolchain.

Key takeaway

For AI Architects evaluating observability platforms for distributed cloud environments, Tsuga's approach offers a critical shift. Its architecture keeps all telemetry within your security perimeter, eliminating external data transfer and associated governance risks. You should consider solutions that process complete datasets without sampling and offer consumption-based pricing to manage escalating costs from AI workloads. This model ensures better data control and cost predictability for your AI operations.

Key insights

Tsuga's AI-native observability platform keeps telemetry within customer clouds, disrupting traditional external storage and volume-based pricing models.

Principles

• Traditional observability models fail with AI data volumes.
• Data residency and security are paramount for AI telemetry.
• Observability should optimize costs, not increase them.

Method

Tsuga's platform deploys inside customer cloud environments, processing complete telemetry datasets without sampling, and operating automated root cause analysis and AI agents within the customer's security perimeter.

In practice

• Deploy observability within your existing cloud infrastructure.
• Prioritize platforms that process complete telemetry without sampling.
• Seek observability solutions with transparent, consumption-based pricing.

Topics

• AI Observability
• Cloud Infrastructure
• Data Residency
• Telemetry Management
• AI-Native Applications
• Series A Funding

Best for: Investor, CTO, VP of Engineering/Data, MLOps Engineer, AI Architect, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Series A" OR "Series B" OR "Series C" AI startup via Google News.