We run heavy performance and correctness testing on our engine and its connectors, and much of it is still slow and partly manual. We need automation that handles large JVM codebases, runs well for remote engineers, and includes benchmark tooling we can repeat. Budget is modest, so pricing must stay small-team friendly. Tools built only for small codebases won't work for us.
DevTools · Data Platforms · 50–200 · Europe #b018_21
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We build an enterprise stream processing engine and managed platform for companies running real-time data applications.
- Industry
- DevTools & Software · Data Platforms
- Size
- 50–200 employees
- Region
- Europe
- Tools budget
- $1K–5K per month
- Needs
- Test automation & QA. Our engine and its connectors need extensive performance and correctness testing that is slow and partly manual.Today: qa engineers hired to write automated browser and unit tests for the platform · Urgency: nowData pipelines & integration. We keep building new connectors and catalogs, and keeping them compatible with many external systems is costly.Today: connector and catalog work done by dedicated engineers building each integration for the streaming stack · Urgency: nowFaster CI/CD. Builds of a large JVM codebase slow our remote engineers down.Today: release checks run in a hosted source-control pipeline with container-based environments · Urgency: now
- Who decides
- vp engineering · team trial
- Expects from suppliers
- Strong JVM build support; Works for remote engineers; Benchmark tooling
- Not a fit
- Tools that only support small codebases
- Stack
- CI/CD pipeline in place, Kubernetes, multi-cloud
- Buying triggers
- scaling transaction volume, cost pressure
Needs
Cooperation
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We run claims operations reporting through analyst-built spreadsheets and dashboards, and by the time managers see them the picture is already stale. We need reporting that connects natively to our cloud data warehouse, lets operations staff drill down to individual claims, and uses clear per-member pricing. It must work alongside our existing claims vendor.
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.