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Is this your company? Claim itWe are a long-standing security services provider offering emergency incident response, assessments and monitoring, recently folded into a large telecom group.
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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.
Flaky tests are quietly taxing every release for us. A pipeline goes red, someone reruns it, and nobody fully trusts the signal anymore. Our codebase is mature and our tests have piled up over years. A good solution must work with our existing language and container stack, surface and quarantine unreliable tests, and show measurable gains in build reliability without a rewrite.
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.
Serving speech and language models at national scale makes accelerator spend our biggest line item, and we can't tell which model or team drives it. A good solution must show cost per model, keep data on-site in region, fit our private cloud Kubernetes setup, and be trialable by a small team on a modest budget.
We run services across two regions, and when a security or production incident hits, the handoffs get messy. Who leads, who talks to regulators, and who owns the timeline is often unclear. We need a process and tooling that clarifies roles fast, keeps an auditable record, and works across both regions without slowing engineers.
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 run managed clouds at many remote sites with engineers across time zones. Network and storage faults keep bouncing between shifts, and ownership gets lost at handoff. We need escalation that follows the shift, clear handoff notes, support for physical gear, and one view across many customer sites. Not a tool that assumes a single public cloud.
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 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.
We analyse huge volumes of web request logs for our clients, and storage and query costs keep climbing every quarter. Today our engineers dig through managed-rule telemetry on our hyperscaler's native logging. A good solution needs tiered storage with fast hot queries, volume-based pricing, and must run on managed cloud services.
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.
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