How Glomo made every cross-border transaction traceable

Glomo was running observability on GCP's Stackdriver.

The pattern underneath all of this: Stackdriver was never built to give Glomo one coherent view of a request, just separate slices an engineer had to reconcile by hand.

Moving money across borders, Glomo's reliability isn't just an internal engineering concern, it needs to be demonstrable to regulators and banking partners. When a transaction behaves unexpectedly, the company needs to be able to show exactly what happened and where, and a platform without spans cannot produce that.

That requirement creates a second problem with scale: teams keep observability costs under control by sampling down, accepting that some requests won't have data behind them. Combined with compliance's long-term data retention requirements, Glomo was looking at a setup where staying fully auditable would only get astronomically more expensive as volume grew.

On top of which, they were about to move their infrastructure onto Kubernetes, a migration that would touch nearly every service they ran, and without tracing, that meant doing it partially blind. Glomo didn't need another tool for tracing, but a single, sophisticated platform in place before the migration started, not stitched together after the fact.

Glomo moved their infrastructure onto Kubernetes with base14 instrumented underneath, not bolted on afterward. Because tracing, metrics, and logs were already unified before the migration started, Glomo could see exactly how requests behaved across the new topology as it came online over 6 months, rather than finding out after the fact. The migration became a non-event: no scramble to reinstrument, no gap in visibility while the infrastructure changed shape.

Glomo now views APM, infrastructure, and database signals together instead of across separate tools, correlated rather than pieced together by hand. That shift has moved the team from reactive firefighting to diagnosing issues from data. Distributed traces are viewable down to the span level in Scout's TraceX. Database behavior runs through pgX, base14's integration for correlating PostgreSQL activity directly with application traces and infrastructure metrics, rather than treating the database as its own island.

base14's pricing model, with no egress costs and no sampling required, let Glomo keep full-fidelity data on every transaction without that completeness turning into a runaway bill as volume grew, and the same model kept long-term compliance retention economical rather than a cost that compounded with scale. Had Glomo stayed on Stackdriver, they'd have faced the tradeoff most companies do at their stage: either sample down and lose the transaction-level detail regulators expect, or keep full fidelity and watch retention costs climb faster than the business itself. Working with base14 meant they never had to choose.

Custom work isn't an exception at base14. Scout's PostgreSQL observability capability, pgX, took shape with Glomo as an early design partner, with their real database monitoring needs informing what it needed to cover and how it should surface that data. The result is a capability every base14 customer, across many industries, regulated or not, now has access to, sharpened early by a partner who was using it in production. It ran both ways: base14's roadmap moved because of what Glomo needed, and Glomo helped shape a feature that's since become part of the core platform.

Glomo's engineers have base14's Scout MCP available to them, letting them ask questions about their live telemetry, traces, and service topology in natural language rather than hand-building every query from scratch, closer to describing what's wrong than searching for it.