Model-Agnostic Architecture: Avoiding LLM Lock-In
Decouple business logic from the language model. Agents, Decision Layer, and rule sets stay unchanged when models switch. No vendor lock-in.
The Risk: One Model, One Vendor
Many companies build their AI strategy on a single model. “We use ChatGPT” or “We run on Claude.” Prompts are optimized for that model. Integrations are built for that vendor’s API. Workflows are tailored to that model’s characteristics.
At a Glance - Model-Agnostic Architecture
- Building on a single LLM vendor creates dangerous dependency: prices shift, APIs change, models get deprecated on short notice.
- A model-agnostic architecture decouples business logic from the language model - agents, Decision Layer, and workflows remain untouched during model switches.
- Multi-model routing assigns budget models to simple tasks and flagships to complex reasoning, saving 40-60% on token costs.
- Self-hosted models handle sensitive data while cloud APIs serve non-critical requests - governed by the Decision Layer.
- Forrester (2024) reports that organizations with model-agnostic architectures reduce LLM migration costs by up to 70% compared to single-vendor setups.
Then one of three things happens: The vendor raises prices. The vendor changes the API. A new model appears that is significantly better or cheaper. In every case: The entire implementation needs to be adapted.
In the LLM market, this happens fast. Over the past 18 months, prices have halved, new vendors have entered the market, open-source models have surpassed proprietary models in benchmarks. Anyone locked into a single vendor cannot capitalize on these developments.
Model-Agnostic as an Architectural Principle
In the Gosign reference architecture, the Model Layer is an interchangeable layer. Business logic, including rule sets, decision logic, and workflows, is implemented in the Decision Layer and Agent Layer, not in the model.
When a new model becomes available, it can be integrated without changing the layers above it. The agent does not know which model it is using. It sends a request to the Model Layer and receives a response. Which model delivers the response is irrelevant to the agent.
Multi-Model Routing
An agent can use multiple models simultaneously. The routing is rule-based:
Cost optimization: Simple tasks (document classification, data extraction) run on a cost-efficient model. Complex tasks (decision support, rule interpretation) run on a more powerful model.
Data residency: Sensitive data goes to self-hosted models (Llama, Mistral, DeepSeek). Non-critical data can be routed through cloud APIs.
Failover: If a model provider goes down, the system can automatically switch to an alternative model.
The routing rules are configured in the Decision Layer and fully traceable.
Supported Models
The Gosign architecture currently supports:
- Claude (Anthropic) - Cloud API
- ChatGPT (OpenAI) - Cloud API
- Gemini (Google) - Cloud API
- Llama (Meta) - Self-Hosted or Cloud
- Mistral - Self-Hosted or Cloud
- DeepSeek - Self-Hosted or Cloud
- gpt-oss (OpenAI) - Self-Hosted or Cloud
New models can be integrated as soon as they are accessible via a standard API.
| Aspect | Single-Vendor Setup | Model-Agnostic Architecture |
|---|---|---|
| Model Switch | Full rebuild of prompts, integrations, workflows | Routing rule change, no rebuild |
| Cost Control | Locked to one pricing model | Budget models for simple tasks, flagships for complex |
| Data Sovereignty | Depends on vendor | Self-hosted for sensitive, cloud for non-critical |
| Failover | No alternative if provider is down | Automatic switch to alternative model |
| Migration Cost | High (Forrester: up to 70% higher) | Low (configuration change only) |
| Future-Proofing | Risk of deprecation | New models slot in without changes |
More on this: AI Infrastructure
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Bert Gogolin
CEO & Founder, Gosign
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