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Multi-Model AI with OOS

Use any AI model. Local or cloud. From a single system.

Most AI platforms are built around a single model. You choose a provider, integrate their API, and build your system around it. If you want to switch providers or add a second model, you rewrite code, change integrations, and hope everything still works.

OOS takes a different approach. It supports multiple AI models at the same time, from a single system. Local models and cloud models. Open source and commercial. All available, all controlled by the same defined behaviors.

One System, Many Models

OOS can connect to multiple AI model providers simultaneously. A local model running on your own hardware. OpenAI. Anthropic. Google. Meta. Ollama. ONNX. Others. They are all available from the same system at the same time.

You do not need separate integrations for each provider. You do not need different code paths. You do not need to rebuild anything when you add a new model or remove an old one. OOS handles the connection. You choose which model to use.

You can also set a default model. If no specific model is requested, the system uses the default. Simple for everyday operations. Flexible when you need it.

Choose the Right Model for the Task

Not every task needs the same model. A simple classification might need a fast, lightweight local model. A complex analysis might need a powerful cloud model. Sensitive data might require a model that runs entirely on your own hardware, never leaving your network.

With OOS, you can make that choice per request. Use a local model for tasks involving private data. Use a cloud model for tasks that need more capability. Use a fast model when speed matters. Use a powerful model when accuracy matters. The choice is yours, and you can change it at any time.

The Model Changes. The Behavior Does Not.

This is what makes OOS different from other multi-model approaches.

In most systems, switching from one model to another means different behavior. Different response formats. Different quality. Different failure modes. The application has to account for each model's differences.

In OOS, the defined behavior of the object stays the same regardless of which model is behind it. The rules do not change. The response logic does not change. Whether the response comes from a local model or a cloud model, OOS maintains the same defined behavior.

The model provides the intelligence. OOS provides the consistency.

No Vendor Lock-In

Vendor lock-in is one of the biggest risks in AI deployment. If your system is built around one provider, you are dependent on their pricing, their availability, their decisions. If they raise prices, you pay more. If they change their model, your system changes with it. If they go down, you go down.

OOS eliminates this risk. Because you can connect to any model provider, you are never dependent on a single one. If one provider raises prices, switch to another. If one model performs better for your use case, use it. If a provider experiences downtime, fall back to a local model. Your system continues. Your behaviors stay the same.

Keep Sensitive Data Local

For many organizations, sending data to a cloud model is not an option. Healthcare data, financial records, classified information, proprietary business logic. These cannot leave the network.

With OOS, you run a local model on your own hardware for sensitive tasks. The data never leaves your environment. For non-sensitive tasks, you use a cloud model for additional capability. Both operate under the same unified identity with the same defined behaviors. The security boundary is clear and enforceable.

Bring Your Own Model

Many organizations build their own AI models. These models are trained on proprietary data and represent significant investment. Sharing them with a third-party platform is not an option.

OOS provides a standard integration path for custom models. Build your connector, and OOS loads it alongside the built-in providers. Your model stays on your hardware, behind your firewall, under your control. OOS never touches the model itself. It only communicates through the connector you build. Your proprietary model operates under the same defined behaviors as any other model in the system.

Use What You Need

OOS is modular by design. While OOS is built to manage objects and enforce defined behaviors, you do not have to use every part of it. You can start with multi-model support alone, and add object capabilities later if needed. One connection point for local models, cloud models, and custom models. Use what you need. Add more when you are ready.

Why This Matters

AI is evolving fast. New models appear every month. Prices change. Capabilities improve. The model that is best today may not be best tomorrow.

Organizations that lock themselves into a single model provider are betting their infrastructure on one company's roadmap. Organizations that use OOS are free to choose the best model for each task, switch when something better comes along, and keep sensitive data under their own control.

The AI model is a component. It can be replaced. What cannot be replaced is the defined behavior, the rules, and the consistency that OOS provides. Models come and go. The system stays the same.

Combined with OOS Unified Identity, this becomes even more powerful. Multiple machines across different networks and architectures, all operating as one logical system, all connecting to any combination of local and cloud models, all following the same defined behaviors. One unified system. Any model. Consistent responses everywhere.

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