Orchestrating many LLMs & pipelines to simplify AI workflows

Mid-to-large software teams building AI features are increasingly composing multi-model pipelines that mix LLM calls, tool invocations, and retrieval-augmented generation; these teams routinely face brittle orchestration problems—state management, model routing, latency spikes, cost attribution, testing, and compliance—across a growing set of model and vector-store providers. The operational burden slows iteration and creates hidden costs when engineers resort to ad hoc scripts and point solutions. You could build an orchestration platform that provides a declarative pipeline language, a model-agnostic runtime, prebuilt connectors to OpenAI/Anthropic/Hugging Face and major vector DBs, automatic cost- and latency-aware model routing, plus first-class observability, testing harnesses, and policy enforcement. Offer both a managed control plane with SLO-backed runtimes and an embeddable SDK so teams can develop locally, integrate with CI, and enforce governance centrally. This is a timely opportunity because composable AI patterns, stable API-driven LLMs, and standardized vector/RAG workflows have made multi-model pipelines both common and economically meaningful; I estimate an addressable market of roughly 500,000 mid+ software teams at ~$50K ACV each, or about $25B, and market indicators are strong (market score 90/100, revenue potential 82/100). Competition is medium today—many libraries and early platforms exist, but few provide the enterprise-grade integration of runtime, observability, cost control, and governance together. To win you must be candid about the hard engineering work ahead—ensuring low-latency, portable runtimes, deep integrations, and keeping up with rapidly evolving model APIs—while doubling down on differentiators: an opinionated, model-agnostic runtime with robust observability, cost governance, extensible connectors, and a product-led approach with close early customers; these trade-offs make the opportunity worth pursuing but not trivial to execute.