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.
DevTools · AI & ML · 50–200 · Europe #b020_08
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We sell software that lets enterprises deploy, serve and monitor machine learning models on container infrastructure, alongside a widely used source-available core.
- Industry
- DevTools & Software · AI & ML
- Size
- 50–200 employees
- Region
- Europe
- Tools budget
- $1K–5K per month
- Needs
- Technical hiring. Engineers who know systems languages and container orchestration are hard to find, and our hiring across two continents is slow.Today: slow recruiting of systems-language engineers through agencies and job boards across two regions · Urgency: this yearApplication security & code scanning. We ship many container images and dependencies each release and need scanning plus a clear vulnerability disclosure process.Today: release-time image and dependency scans; open-source repository lacks a published disclosure policy · Urgency: this yearCloud cost optimization. Testing serving workloads across many orchestration clusters drives a cloud bill we struggle to attribute.Today: temporary orchestration clusters for testing billed without clear attribution; inferred · Urgency: later
- Who decides
- vp engineering · team trial
- Expects from suppliers
- Container image and dependency scanning in pipelines; Cost attribution per test cluster; Hiring support across the UK and Asia; Good fit for open-source workflows
- Not a fit
- Agentless scanners with no container support; Recruiters without systems engineering depth; Long multi-year lock-in contracts
- Stack
- Kubernetes, CI/CD pipeline in place, cloud: GCP-class hyperscaler, multi-cloud
- Buying triggers
- cost pressure, security incident risk
Needs
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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.
We are adding AI to claims and customer service, and right now product teams review model behaviour informally before anything ships. That will not scale. A good solution must catch wrong or unsafe answers to members before they land, work with our app backend, handle Nordic languages, and stay transparent on price. A quick team trial matters more than a long rollout.
We run a regulated cloud platform where our engineers and partner teams all need access, and proving who can touch what is harder than it should be. We need auditable, least-privilege access governance that fits our hyperscaler, supports reviews and fast revocation, and can pass a large parent group's security due diligence.
We hold customer traces packed with sensitive context, and our detection and response coverage is still thin. We need cloud-native monitoring tuned for SaaS and Kubernetes logs, alerts that surface real threats instead of noise, and a rollout that keeps pace with our fast hiring. Cloud-delivered only, and affordable for a mid-sized team.
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.