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Is this your company? Claim itWe are a large maker of wearable drug delivery devices, with mobile apps and cloud services that connect patients, clinicians and partner platforms.
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We ship many credit products in parallel, and our regression coverage cannot keep pace. Releases slow down or carry risk. A good solution must plug into our existing pipeline, cover lending flows like applications, servicing and payments, let QA teams across regions author tests without heavy engineering help, and pass a security review.
When inference demand surges, our on-call engineers drown in manual steps. We need remediation that runs automatically from runbooks, understands custom hardware telemetry, and can be self-hosted on our own private infrastructure. Generic tools built for commodity servers fall short. Anyone else automating response across hardware and cloud layers?
Since moving to the cloud, our hosting bill has become something engineering leads review by hand, with no dedicated tool. As usage grows we want it to stay predictable. A good solution gives cost visibility per service, fits a mature monolithic web app, and lets one team trial it without a long rollout. Not interested in tools that assume a microservice estate.
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.
We wish for a shared container platform built for mature application servers, not greenfield microservices. It should let many teams adopt it gradually while customer-hosted installs keep running. We would judge it on support for older server stacks, regional data residency, public sector references, and how little each team must rewrite to ship.
Telemetry from our many production clusters keeps growing faster than the budget we set for observability. We want to keep what matters for debugging while cutting noise and storage spend. A good solution must handle huge distributed fleets, offer clear, predictable pricing, let us choose how long data is kept, and keep all data inside our regions.
We're a small team running clusters, deployments and observability, and it eats time our product engineers should spend building. We want a leaner internal platform without hiring a dedicated platform group. A good solution must be light to operate, give clear audit logs, keep customer workloads isolated, and pass a security review.
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.
When a database or platform incident hits, our customers' payments stall, and we feel it immediately. Today our engineers lead live incidents with in-house alerting and scripts. We need earlier detection, clear on-call coordination across European and Asian time zones, and a full audit trail of every response step. It must fit our existing ticketing and cloud setup.
We run infrastructure-as-code with GitOps, but our engineers still file tickets whenever they need to provision or change platform components. It's slow, and mistakes creep in. We want a safe self-service layer on top of Kubernetes with guardrails, audit trails and approvals. It must work with open source tooling and run in our region.
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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