Repeat LLM setup pain — centralized config, templates, and orchestration

Executive Summary

Large and mid-size enterprise AI teams—platform engineers, ML/infra orgs, and LLM product owners—regularly spend repeated weeks integrating new models, wiring connectors, copying prompt templates, and re-implementing safety and observability for each provider and use case. That results in duplicated engineering effort, brittle agent flows, and inconsistent governance across deployments, which is especially painful as teams adopt multiple models and agentized workflows. You could build a centralized LLM configuration and orchestration platform offering reusable templates, policy-as-code, multi-model routing, an adapter library for hosted and self-hosted weights, and an agent runtime with safety hooks, retries, and lineage/observability. Deliver it as a SaaS control plane with on-prem connectors and SDKs so teams can move from weeks of one-off integration to days of standardized onboarding and enforceable governance. This market is attractive now: the addressable opportunity is roughly $40B (200,000 large+mid enterprises × $200K/year), with a market score of 88/100 and revenue potential rated 90/100, driven by multi-model deployment, agentization, and commoditization of inference. Cloud vendors are optimizing hosting and inference, not orchestration and policy, so there’s a timing window for a focused orchestration and governance layer that reduces operational drag. To stand out, be multi-model neutral, optimize developer DX with prebuilt enterprise templates and local dev loops, and bake in policy-as-code and measurable ROI metrics (time-to-deploy, incident reduction), while building strategic connectors to major providers and open-weight hosts. Honest challenges are long enterprise sales cycles, integration friction with incumbent MLOps and observability stacks, and the ongoing maintenance burden of keeping adapters and safety controls up to date as APIs and models evolve.