Part 3: Multi-Model, Always Your Choice—the Power of LLM Agnosticism

Why True Model Agnosticism Matters

When Kochava set out to build StationOne, we immediately recognized a reality: No single AI model fits every task, budget, or compliance scenario. This is even more stark in large-scale, regulated sectors like advertising, where billions of data points move across silos and the cost and risk of a suboptimal LLM can be immense.

A rigid, one-size-fits-all AI stack leads to teams hitting dead ends:

• Privacy-focused use cases get blocked by cloud-only models.
• Speed and experimentation are limited by vendor APIs.
• Costs spiral out of control when every workflow relies on a single commercial endpoint.
• Most critically: Innovative new models—from startups, cloud providers, or open source—can’t be adopted without a year-long integration cycle.

StationOne solves this by making model choice not merely an option, but a core architectural feature.

How StationOne Delivers Multi-LLM Freedom

Kochava engineered StationOne’s multi-LLM layer to abstract away model-specific quirks while maximizing configurability.

• Plug in any LLM, switch at will: Our engine abstraction layer lets you register and select from multiple LLM providers, including OpenAI, Anthropic, Gemini, local private models, specialized market analysis engines, and more.
  
• Task-based routing: Automation and workflow recipes can be built within Agent Forge, specifying the best model for a given job: fast and cheap for routine classification, specialized for regulatory or vertical tasks, high-end cloud for generative content.
• Bring your own model: As the LLM ecosystem evolves, StationOne stays future-proof. Want to use a new open-source model for in-house audience segmentation? Just register it—no upstream dependency, no roadmap delay.
• Policy control for enterprise: Admins can set constraints (e.g., “Sensitive ad spend data must never hit an external API”), and StationOne enforces this across workflows.