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Beirit Harvey

This blog explores Split-Brain, ScalarLM's intelligent routing layer that automatically determines where each inference request should run. 

Intelligent Model Routing Explained: Inside ScalarLM's Split-Brain

As enterprises adopt more AI models, one question becomes increasingly important: Which model should answer this request?

A customer support chatbot may need access to proprietary company knowledge. A developer assistant might work with confidential source code. Another application may simply need to summarize a public document. While each use case benefits from AI, not every prompt should be handled by the same model.

Many organizations solve this by manually deciding which model to use or by hardcoding routing logic into their applications. That approach works initially, but it becomes increasingly difficult as AI deployments grow. ScalarLM's Split-Brain was built to solve this problem.

Split-Brain is an intelligent routing layer that automatically determines where each inference request should run. Instead of requiring applications to choose between public and private models, Split-Brain analyzes each request and routes it to the most appropriate backend through a single API endpoint.


The Challenge with Multi-Model AI

Enterprise AI is no longer built around a single model.

Organizations often use a combination of:

 * Public frontier models for general reasoning
 * Privately hosted open-source models for sensitive workloads
 * Fine-tuned models for domain-specific tasks

While this provides flexibility, it also creates operational complexity.

Developers now have to answer questions like:

 * Which model should handle this request?
 * Should this prompt stay on private infrastructure?
 * Is this request worth paying premium inference costs?
 * How do we support multiple model providers without rewriting our application?

Embedding those decisions directly into every application quickly becomes difficult to maintain.


What Is Split-Brain?

Split-Brain is ScalarLM's intelligent inference router. Rather than connecting directly to a single model provider, applications send every request to the Split-Brain Router. The router then determines which backend should process the request.

Today, Split-Brain can route requests to:

 * A private ScalarLM deployment
 * Anthropic Claude

From the application's perspective, nothing changes. Developers continue working with a single endpoint while Split-Brain manages the routing automatically.


How Split-Brain Works

Unlike a traditional API gateway or load balancer, Split-Brain doesn't simply distribute traffic. Instead, it evaluates every request using a novelty classifier.

The classifier determines whether a prompt is likely to be:

 * A general request that can be answered by an external foundation model
 * A novel or uncertain request that should remain on private infrastructure

If the classifier is confident that the prompt is general, Split-Brain routes the request to Claude. If the request is novel, uncertain, or better suited for a private deployment, it stays on the organization's ScalarLM infrastructure.

Because this decision happens for every request, routing adapts dynamically without requiring developers to build or maintain custom routing logic.


One API, Multiple Models

One of Split-Brain's biggest advantages is that applications don't need to be rewritten.

The router supports both:

 * OpenAI Chat Completions API (/v1/chat/completions)
 * Anthropic Messages API (/v1/messages)

That means existing SDKs and tools continue working with only a small configuration change. For example, OpenAI-compatible clients simply update their base URL to the Split-Brain router and authenticate using a Split-Brain bearer token.

Claude Code also works natively by pointing its Anthropic base URL to the router and using the Split-Brain authentication token. Because the router speaks both APIs, organizations can adopt Split-Brain without changing how their applications generate prompts or consume responses.


Automatic or Manual Routing

By default, applications use the router-auto model. In this mode, Split-Brain automatically evaluates every request and determines the appropriate backend. For situations where developers need complete control, routing can also be overridden.

Requests can be explicitly sent to:

 * claude to always use Claude
 * scalarlm to always use the private ScalarLM backend

This is particularly useful during testing, benchmarking, or when handling highly sensitive workloads.


Lower AI Inference Costs

One of the biggest benefits of intelligent routing is infrastructure efficiency. Many organizations unknowingly send every request to the same premium model, even when a less expensive option could provide the same result. Split-Brain helps optimize inference spending by selecting the most appropriate backend for each request.

Instead of developers deciding where every prompt should go, routing decisions happen automatically, helping organizations make better use of both private infrastructure and external models.


Greater Control Over Sensitive Data

Cost isn't the only concern.

Enterprise AI applications frequently process:

 * Source code
 * Product roadmaps
 * Internal documentation
 * Customer information
 * Proprietary business knowledge

Split-Brain helps organizations keep sensitive requests on privately hosted ScalarLM infrastructure while allowing more general requests to leverage external models.

Current documentation notes that the novelty classifier evaluates the last user message when making routing decisions. For agentic workflows, where context may also exist in system prompts, tool definitions, or tool outputs, ScalarLM currently recommends pinning sensitive sessions to the private ScalarLM backend until whole-payload classification is introduced.


Built for Enterprise Reliability

Split-Brain includes several capabilities designed for production deployments.

Every response contains metadata identifying:

 * The selected backend
 * The routing decision
 * A unique request ID

Requests are also visible in the Split-Brain Request Explorer, where developers can inspect routing behavior and label prompts to continuously improve classifier performance.

The router is also designed to fail closed.

If the novelty classifier or the private ScalarLM backend becomes unavailable, Split-Brain does not automatically forward requests to an external model. Instead, it returns an error, preventing potentially sensitive prompts from leaving the organization's infrastructure unexpectedly.


Why Intelligent Routing Matters

As enterprises move beyond a single-model strategy, intelligent routing is becoming a core part of AI infrastructure. Applications shouldn't have to know which model is best suited for every request.

By introducing a decision layer between applications and model backends, Split-Brain allows organizations to balance cost, performance, and data privacy without adding complexity for developers.

The result is a simpler architecture, better infrastructure utilization, and greater confidence that every request is handled by the right model.


Final Thoughts

Split-Brain extends ScalarLM beyond training and inference by adding intelligence to the serving layer.

With support for both the OpenAI Chat Completions API and the Anthropic Messages API, automatic routing through a novelty classifier, manual backend controls, built-in observability, and fail-closed behavior, Split-Brain gives organizations a practical way to manage multi-model AI deployments.

As enterprise AI continues to evolve, intelligent routing won't just improve efficiency… it will become a foundational capability for building secure, scalable, and cost-effective AI applications.


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Intelligent Model Routing Explained: Inside ScalarLM's Split-Brain

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