Remote inference
- faster to adopt
- access to frontier models
- less infrastructure overhead
- variable usage-based pricing
- reliance on vendor controls and commercial terms
Deployment models
Local vs remote inference
One of the most important AI FinOps decisions is where inference happens. Some workloads are well suited to hosted APIs, while others may justify local or self-hosted models for privacy, control, latency, resilience, or predictable usage patterns.
There is rarely a universal right answer. In many organisations, the most practical path is a mix of remote services for some use cases and local or tightly controlled environments for others.
Local or self-hosted inference
- more control over data and environment
- potentially lower marginal cost at stable volume
- greater operational complexity
- hardware and platform ownership requirements
- more responsibility for security, patching, and performance
When remote inference often makes sense
Remote inference is usually the sensible default for early-stage adoption, fast experimentation, or use cases that depend on leading proprietary models. It reduces setup effort and lets teams focus on the workflow rather than the infrastructure.
- prototyping and early pilots
- general productivity and assistant-style use cases
- teams without in-house GPU or platform capability
- workloads where flexibility matters more than strict cost predictability
When local inference may be worth it
Local or self-hosted inference becomes more attractive when control requirements are higher or usage patterns are stable enough to justify the overhead. The tradeoff is that lower marginal cost can come with much higher operational responsibility.
- sensitive data environments
- strong residency or sovereignty preferences
- high-volume repetitive workloads
- specialised use cases where smaller models are a good fit
Decision factors
- sensitivity of data
- required model capability
- latency and resilience needs
- cost predictability vs flexibility
- internal capability to run and secure infrastructure
- procurement and governance expectations
Australian angle
In Australia, this decision often intersects with data residency preferences, Essential Eight thinking, sovereignty concerns, and practical expectations from security or procurement stakeholders. That means local versus remote inference is not just a technical choice. It is often an operating model decision as well.