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.
Fintech · Banking & BaaS · 200–1000 · Europe #b006_19
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We run an open banking platform that lets banks and fintechs access account data, initiate payments and enrich transactions through one interface across Europe.
- Industry
- Fintech · Banking & BaaS
- Size
- 200–1000 employees
- Region
- Europe
- Tools budget
- $5K–20K per month
- Needs
- Internal developer platform. We want a shared engineering foundation so product teams stop rebuilding deployment and runtime tooling.Today: engineering foundations team builds shared developer platform for product squads · Urgency: this yearThreat detection & response. We hold bank data for millions of end users and need fast detection and response with verifiable trust for customers.Today: security operations engineer uses log analysis and alerting with incident drills · Urgency: this year
- Who decides
- vp engineering · security review
- Expects from suppliers
- Eases integration into a larger parent group's controls; Security operations tooling for container workloads
- Not a fit
- Tools that duplicate parent group platforms; Vendors without connector-heavy platform experience
- Stack
- cloud: GCP-class hyperscaler, Kubernetes, CI/CD pipeline in place
- Buying triggers
- tool consolidation, security incident risk
Needs
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We connect many hospital sites, each with its own identity system, and adding or removing users across them is slow and error-prone. We need a solution that federates with a wide range of hospital identity providers, automates provisioning and offboarding, keeps clear audit trails for regulated work, and prices simply per connected site. Proven references in clinical settings matter.
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.