Tsuga raises $35m to keep AI-era observability inside the customer’s own cloud

The business move

Tsuga, a Paris-based startup founded by two former Datadog engineers, has raised $35 million in a Series A round. The company officially emerged from stealth less than six months ago and aims to reshape observability software for modern AI workloads. Its core pitch is to ditch the standard per-byte pricing model just as telemetry data volumes explode due to AI agents.

Why it matters

AI workloads generate vast amounts of telemetry data as they interact with complex systems and multiple data streams. Traditional cloud observability services typically charge based on data volume, exposing customers to skyrocketing costs as AI usage scales. Tsuga’s approach keeps observability data inside the customer’s cloud environment, reducing dependency on external storage or processing and potentially avoiding huge bill spikes.

This model matters for enterprises and operators with growing AI infrastructure. It can lower the financial risk associated with telemetry costs and offer more predictable expenses. It also offers control over sensitive monitoring data, which grows in both volume and business sensitivity alongside AI adoption.

Who gains and who gets squeezed

AI-driven businesses and cloud operators that struggle to forecast observability costs under per-byte models stand to gain the most. Tsuga’s funding signals investor confidence in demand for more scalable, cost-effective monitoring tools tailored for AI.

Conversely, traditional observability platforms that depend on usage-based billing face pressure to adapt or risk losing customers to newer entrants who deliver scale without escalating costs. Cloud providers charging for telemetry ingestion and storage could also see margin compression if customers move parts of their monitoring infrastructure in-house.

What to watch next

Tsuga’s ability to execute on this model will depend on how well it integrates with existing AI systems and cloud platforms. Its pace of customer acquisition and use cases will reveal whether the market is ready to shift away from byte-based billing for observability.

The startup’s next moves on product features, partnerships, and geographic expansion will indicate how fast cost pressures are accelerating changes in the AI observability ecosystem. Watch for how competing vendors respond, either by innovating pricing models or enhancing in-cloud data processing to keep up.

AI Quick Briefs Editorial Desk