Open-source LLMs can be self-hosted for control, customization, and data residency; proprietary LLMs are accessed via a managed API for frontier capability with less operational burden. Regulated firms weigh data control and cost against capability and speed — and increasingly run a mix, routing sensitive workloads to self-hosted models and others to hosted APIs.
By Evgeny Aleksandrov, Founder, BlackGrid ·
At a glance
Dimension
Open-source LLM
Proprietary LLM
Hosting
Self-hosted (your infra)
Vendor API
Data control
Stays in your environment
Sent to the provider
Capability
Strong, closing the gap
Typically frontier
Cost model
Infrastructure + ops
Per-token / subscription
Customization
Full (weights available)
Limited to provided controls
Ops burden
You run inference
Managed for you
When to choose Open-source LLM
Data residency or no-data-egress is required
You need full control and customization
Per-token cost at scale must be minimized
You can run and secure your own inference
When to choose Proprietary LLM
You want frontier capability now
Time-to-value beats infrastructure control
You prefer managed scaling and updates
You lack the ML-ops capacity to self-host
Can you use both?
Many enterprises run a portfolio: a self-hosted open model for sensitive, high-volume, or data-resident workloads, and a proprietary API where frontier capability matters most. A standard interface such as the Model Context Protocol makes swapping models far easier.
Are open-source LLMs good enough for the enterprise?
Increasingly, yes — the capability gap has narrowed. The deciding factors are usually data control, customization, cost at scale, and whether you can operate inference securely.
Why would a regulated firm self-host an LLM?
Data residency and control: sensitive data never leaves the environment, customization is unrestricted, and per-token cost at scale can be lower. The trade-off is operational burden.
Does model choice affect governance?
Yes. Either way you need model provenance, evaluation, and an audit trail. The NIST AI RMF frames these as ongoing risk-management functions regardless of where the model runs.