Case Study: b.well achieves full-fidelity observability and predictable costs with groundcover

b.well cuts observability costs 50-60% with groundcover

b.well, a healthcare technology company, faced challenges with their observability stack as they prepared to launch large-scale AI healthcare workloads. Their existing tools, Datadog and Grafana, forced heavy sampling due to high costs and fragmented critical data, making it difficult to debug performance issues.

By implementing groundcover, b.well was able to eliminate all sampling and unify their traces, metrics, and logs in a single platform. This provided full-fidelity observability, which led to faster debugging, predictable costs that were cut by 50-60%, and organization-wide adoption. groundcover's solution allowed engineers to reliably identify and resolve critical performance issues during their product launch.

b.well