Every enterprise approaching agentic AI faces a fundamental choice: how to deploy and manage the large language models that power their agents. There isn’t one right answer, but there is a right framework for deciding.
The Three Deployment Models
1. Bring Your Own LLM (BYOL)
Organizations with specific performance, compliance, or cost requirements often choose to deploy their own models, whether fine-tuned open-source models or commercial API subscriptions.
Best for: Enterprises with strong ML engineering teams, specific compliance requirements, or high-volume use cases where cost optimization matters.
2. Enterprise LLM Platform
Many organizations prefer a managed AI platform, leveraging their cloud provider’s enterprise AI services (Azure OpenAI, AWS Bedrock, Google Vertex AI).
Best for: Enterprises that want managed infrastructure, enterprise agreements, and SLA-backed AI services without building ML operations in-house.
3. Privacy-First Local Models
For highly regulated industries or sensitive data scenarios, running models entirely within the enterprise boundary is the only option.
Best for: Financial services, healthcare, defense, and any organization where data cannot leave the enterprise perimeter.
Choosing Between the Three
The choice usually comes down to three questions:
- Where can the data go? If data cannot leave the enterprise perimeter, local models are the starting point
- Who runs the infrastructure? Strong ML engineering teams can operate their own models; others gain more from a managed platform
- What does the volume look like? High-volume use cases shift the cost balance toward models the enterprise controls
Many enterprises end up on more than one path: a managed platform for general tasks, and a local model for sensitive data.
Where Symphony Fits
Symphony serves as the orchestration and policy layer that works across all three models. Regardless of where the LLM runs, Symphony provides:
- Consistent governance: Same approval workflows, audit trails, and guardrails regardless of the underlying model
- Model-agnostic orchestration: Business processes don’t change when a model is switched or upgraded
- Policy enforcement: Define what agents can and cannot do, independent of model capabilities
- Multi-model coordination: Use different models for different tasks within the same business process
In practice, Symphony works with models from OpenAI, Azure OpenAI, AWS Bedrock, Google Gemini, and SAP BTP, as well as local models run through Ollama. Whichever model is in use, it never executes directly: actions are bounded, reversible, and recorded in the audit trail.
The Strategic Advantage
The enterprises that will succeed with agentic AI are not the ones that pick the “best” model; they’re the ones that build a robust orchestration layer that makes the model choice a pluggable decision rather than an architectural commitment.
Symphony keeps an agentic AI strategy governed and model-independent. The governance side of scaling agents is covered in escaping pilot purgatory with governance and the purpose of an enterprise orchestrator agent, and the agent layer under isAI agentic IT operations.
Frequently asked questions
What are the three ways to deploy LLMs for enterprise agentic AI?
The three models are BYOL, where the enterprise deploys its own fine-tuned or commercial models; an enterprise LLM platform such as Azure OpenAI, AWS Bedrock or Google Vertex AI; and privacy-first local models that run entirely inside the enterprise boundary.
When should an enterprise run LLMs locally?
Local models fit when data cannot leave the enterprise perimeter, which is common in financial services, healthcare, defense and other regulated sectors. Running models inside the boundary keeps sensitive data under enterprise control, at the cost of operating the model infrastructure in-house.
Why should agent governance be independent of the model?
Models change faster than business processes. If approvals, audit trails and guardrails live inside one model's setup, every switch or upgrade forces the governance to be rebuilt. Keeping policy in an orchestration layer makes the model a pluggable decision instead of an architectural commitment.
How does Symphony work across different LLMs?
Symphony acts as the orchestration and policy layer above the model. It works with OpenAI, Azure OpenAI, AWS Bedrock, Google Gemini, SAP BTP and local models through Ollama, applying the same approval workflows, audit trails and guardrails whichever model runs underneath.
Can an enterprise use more than one LLM in the same process?
Yes. Different tasks within one business process can use different models, for example a managed platform for general reasoning and a local model for steps that touch sensitive data, with one orchestration layer applying consistent policy across them.