Fintech · Wealth & Investing · 50–200 · Asia-Pacific #b042_16

We wish for an observability tool that treats device telemetry differently from service logs, with tiering that keeps recent high-frequency data hot and compresses the rest automatically. It must run inside our own cloud account with regional residency. We would judge it by cost per device as our fleet grows, query speed on old data, and pricing we can predict.

Usage telemetry from the models we host and the ones people download keeps growing, and keeping it all is getting pricey. Right now engineers just hoard training and release logs with no central view. A good solution must tier retention by age, sample high-volume streams, price predictably for a research-heavy team, and run across several clouds.

Our engineers across several countries lose hours chasing issues through chains of APIs, because each team owns its own dashboards and nobody sees one request end to end. We want tracing that builds on open telemetry standards, works on managed containers, handles many tenants at scale, and lets teams adopt it step by step without ripping out what they have.

Every new client launch brings its own card and credit configuration, and our test coverage keeps falling behind. Scripted regression suites run by a small quality group can't keep pace. A good solution must generate and maintain tests per configuration, handle payments test data safely, fit our Java stack and CI/CD pipeline, and run in regulated regions.

We ship our mobile savings app updates often, yet too much release confidence still rests on manual checks and a small team stretched thin. We need broader automated coverage that runs inside our existing pipeline, handles real devices and load, and shows clear results to engineers. Quick team trial, light onboarding and transparent pricing as we scale matter most.

Our multi-tenant service produces heavy telemetry, and monitoring cost climbs with every new region we open. Our reliability engineers run the clusters themselves, and the bill is starting to hurt. A good solution must cut ingest and storage spend, keep per-tenant visibility, work across several clouds, and stay light for a lean team.

We ship investing features fast, and our backend engineers write and run tests themselves. As we grow, we worry a rushed release could break money movement. We need automated tests around transfers and account flows that plug into our existing pipeline, take little setup time, and fit a tiny budget. Heavy enterprise tooling is out.

We added live calling to a product that began as email, and our on-call process was built for slow, asynchronous work. Now an outage means customers on active calls hear silence. We need paging that routes across engineers in distant time zones, ties directly into a public status page, and keeps responders and customers informed together. Point tools that only page people do not help us.

Our telemetry from new sites and cloud services is outgrowing our observability budget. We're stitching logging, monitoring and alerting from basic parts, and costs climb faster than our visibility. A good solution must let us control data volume and retention, expose everything through APIs, and scale across new sites without repricing fights or long lock-in before proving value.

We train and serve our own language models, and compute is one of our biggest cost lines. Today we struggle to see spend per workload, so trimming it is guesswork. A good solution must show cost per workload, run on premises or in sovereign clouds, work without permanent internet access, avoid large minimum commitments, and fit a small monthly budget.