Spacelift Expands Observability Reach to OpenTofu

Spacelift this week at the KubeCon + CloudNativeCon North America 2026 conference extended the reach of its observability platform to OpenTofu, an open source infrastructure-as-code (IaC) tool that is being advanced under the auspices of the Cloud Native Computing Foundation (CNCF).

Dimitri Vlachos, chief marketing officer for Spacelift, said that as OpenTofu continues to gain traction as an alternative to HashiCorp’s Terraform IaC tool, more DevOps teams now need to be able to observe the code being used to, for example, configure cloud infrastructure. The Run Observability platform provided by Spacelift can show within seconds why an OpenTofu run is slow or failing and, just as importantly, how to fix it, he noted.

Spacelift is leveraging the OpenTelemetry Protocol to collect traces that are then checked against a set of rules and the run history of an IT platform to provide those insights. That ability to generate traces was just recently added to OpenTofu. In the absence of that capability, a DevOps team is not going to be able to determine whether an issue stems from the version of OpenTofu they are using, a third-party service or the code they deployed, noted Vlachos.

It’s not clear how many DevOps teams have adopted OpenTofu, but whenever a greenfield application is deployed or there is an update made to an IT environment the percentage of instances where Terraform is being usurped continues to increase, said Vlachos.

At the same time, more DevOps teams are adopting observability platforms as IT environments become more complex, an issue that will only be further exacerbated as organizations start to deploy artificial intelligence (AI) agents at scale, he added.

Hopefully, there will come a day when the insights surfaced by observability platforms are fed directly into automation platforms that will be able to proactively resolve issues before there is an actual incident. The challenge is the cost of adopting an observability platform, instrumenting the applications and collecting, analyzing and storing telemetry data is substantial, so the amount of visibility DevOps teams have into their environments is often limited.

On the plus side, more IT leaders are now willing to invest in observability in the AI era. A recent  survey of 839 software lifecycle engineering decision-makers conducted by the Futurum Group finds 64% plan to raise observability spending over the next 12 months. However, only 19% plan an increase of more than 15%, the lowest share among the five core software lifecycle engineering areas Futurum tracks. 

The paradox is that as organizations spend more on observability the total cost of observability, in some cases, now exceeds the cost of building and deploying the applications that have been deployed. The goal now is finding the best way possible to cost effectively turn a passive archive of telemetry data into a control layer that drives autonomous software development at levels of scale that not too long ago would have seemed completely unattainable.