A key value proposition for vertical AI applications is being model-agnostic. They act as a strategic layer for enterprises, allowing them to route tasks to the best available LLM at any given time. This de-risks enterprise AI strategy from being locked into a single model provider whose performance may be surpassed.

As major AI players like SpaceX/Cursor and Anthropic build closed ecosystems and change pricing, companies face significant vendor lock-in risk. An open IDE layer that supports multiple AI models becomes a strategic asset, allowing teams to avoid price hikes and switch to better models without overhauling workflows.

The "AI wrapper" concern is mitigated by a multi-model strategy. A startup can integrate the best models from various providers for different tasks, creating a superior product. A platform like OpenAI is incentivized to only use its own models, creating a durable advantage for the startup.

For most startups, training a custom foundation model is a waste of capital. The winning strategy is to focus on workflow and proprietary data, building a "headless" product that uses a model router to switch between the cheapest, most effective LLMs for any given task.

Instead of relying on a single AI provider, Genspark built its application on 70+ models. This 'mixture of agents' architecture orchestrates the best model for any task, providing superior results and preventing vendor lock-in for enterprise clients who fear dependency on one provider.

n8n positions itself as the orchestration layer, not the engine (LLM). Users bring their own API keys and can switch between models like OpenAI or Anthropic with minimal effort. This flexibility de-risks adoption for users who are concerned about being locked into a single LLM provider's ecosystem.

Like Kayak for flights, being a model aggregator provides superior value to users who want access to the best tool for a specific job. Big tech companies are restricted to their own models, creating an opportunity for startups to win by offering a 'single pane of glass' across all available models.

Rather than competing to build a single foundation model, Perplexity's strategy is to be an 'aggregator orchestrator' that intelligently selects the best specialized model for any given task. This allows them to always offer the best performance without owning the underlying models, similar to how Kayak aggregates flights.

The Anthropic shutdown shows the danger of relying on one AI model. A robust strategy is to build a proprietary front-end "harness" that controls memory, skills, and data, while being able to dynamically route requests to various backend models.