Solutions
Production-grade solutions that turn our open-source packages into deployable AI systems for specific business problems. Pick one, follow the DIY tutorial to see how it's done, download the examples and deploy them on your own infrastructure — for free — or tell us which ones you want customized and deployed.
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4 solutions
openai-cost-control-for-servicetitan-small-business-agent-spend
SMBs deploying AI-driven scheduling and dispatch agents on ServiceTitan risk runaway costs if one tenant floods the system with requests; without per-tenant budgets, a single bad day can exceed the entire month's budget.Cap and monitor OpenAI tokens per ServiceTitan tenant so field-service AI automation never blows the monthly budget.
vllm-ai-spend-control-for-smb-agent-workflows
Small businesses running agents on self‑hosted vLLM struggle to see aggregated LLM spend per customer, team, or use case. Without built‑in budgeting, a runaway prompt or misconfigured agent can balloon compute costs before anyone notices.Track, cap, and forecast LLM costs across all your agents powered by self‑hosted vLLM models, without slowing down responses.
xai-grok-cost-control-for-smb-customer-support-agents
SMBs deploying AI customer support agents often face unpredictable monthly bills as chat volume spikes, with no built-in controls to limit spending per customer or automatically switch to cheaper models when budgets are exhausted.Prevent runaway AI spending by enforcing per-tenant daily budgets and fallback routing for xAI Grok-powered support agents.
aws-bedrock-cost-control-for-serverless-ai-applications
SMBs adopting AWS Bedrock for AI features often see unpredictable costs because there's no native spend controls. A single mis‑sized prompt or a spike in traffic can burn through a month's budget in hours, with no granular insight into which model or user caused it.A budget‑aware middleware that routes, caps, and tracks every AWS Bedrock LLM call, preventing surprise bills for SMBs running serverless AI features.