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Is this your company? Claim itWe sell monitoring and evaluation software for machine learning and agent applications, offered as hosted software and as customer-managed installs.
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Running language model workloads across regions is pushing our cloud bill up faster than we can explain it. We need spend attributed by team, feature and region, with hard caps that stop runaway usage. A good solution must show who caused a cost, alert before limits hit, and keep a clear audit trail.
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 run heavy query workloads for our customers, so compute spend sits at the center of our business model. Today our engineers benchmark and tune cost per query by hand, which eats time we cannot spare. A good solution must show cost per workload clearly, fit a small team, use usage-based pricing, and be quick to adopt.
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.
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 resolved conversation by our AI agent adds model spend, yet our plans must stay cheap for small shops. We lack a clear view of cost per customer and per conversation. A good solution must track model usage by customer, flag unprofitable accounts, plug into our current stack simply, and avoid seat-based pricing.
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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