What is InferenceOps? Defining the Function Behind Scalable AI

This is blog 2 of a 3 part series, you can read part one here.

In my last blog, I explained why enterprises won’t succeed if they try to run AI inference the same way they run ML. The old ML playbook made sense when models were cheap to serve, highly bespoke, and managed by individual use case teams. But with AI, inference has become the bottleneck: expensive, complex, and central to delivering value.

That shift is why enterprises need a new operational model: InferenceOps.

But what exactly is InferenceOps? Is it just a new name for MLOps, or something fundamentally different? In this blog, I’ll define what InferenceOps is, what responsibilities sit under this function, and why it’s becoming the backbone of enterprise AI adoption.

What is InferenceOps?

InferenceOps is the enterprise function responsible for running AI inference at scale. It centralizes infrastructure, ensures governance, and delivers reliable, efficient APIs to downstream use case teams.

Some describe InferenceOps primarily as a set of tools or a technical platform for distributed inference. That view is useful, but it doesn’t go far enough.

InferenceOps is not just a toolkit - it’s an enterprise function. Tools alone don’t solve the challenges of cost, governance, or risk. What enterprises need is a clear operating model: a dedicated capability inside the platform team, accountable to use case teams, the wider platform organization, and the company as a whole.

This broader definition includes the technical foundations - infrastructure optimization, observability, multi-model management - but also the organizational responsibilities: compliance, risk management, chargeback, and enabling downstream teams to move faster. In other words, InferenceOps is not just about how you run inference, it’s about who owns it and what it delivers to the enterprise.

The Core Responsibilities of InferenceOps

The responsibilities of InferenceOps can be defined relative to the groups they serve:

Responsibilities to the Use Case Teams

• Deliver low-latency, scalable AI APIs for the models needed to power their use cases.
• Provide tooling to monitor, manage, and optimize application performance (including feedback loops).
• Offer shared tooling to accelerate development, such as MCP servers, structured generation constrainers, and SDKs.
• Optimize deployments for use case specific needs (e.g., batched vs. real-time, data residency).
• Back commitments with clear SLAs and an on-call schedule.

This leads to: Faster time to market, reliable model performance, focus on innovation not infrastructure

Responsibilities to the Platform Team

• Optimize and manage inference infrastructure for efficiency and reliability.
• Operate across complex multi-cloud and on-premise compute environments.
• Own model lifecycle management, including deployment, versioning, and rollback processes.

This leads to: Higher GPU utilization, Operational consistency, and reduced platform overhead

Responsibilities to the Company

• Ensure governance and compliance: audit readiness, access controls, guardrails, regulatory adherence, and jurisdictional control.
• Manage enterprise-wide risk by preventing fragmented or unsanctioned inference deployments.
• Control costs and establish chargeback models to align usage with spend.
• Create a strategic advantage by establishing a central backbone for faster adoption of new models, supported by benchmarks and guidance for use case teams.
• Manage both self-hosted and cloud-hosted AI APIs under a unified framework to minimise vendor risk.

This leads to: predictable costs, regulatory readiness, and strategic AI capability at scale

Why InferenceOps Matters

Enterprises don’t invest in new functions lightly. InferenceOps matters because it directly advances four enterprise priorities:

• Efficiency: Reduce redundant deployments, maximise GPU utilization, and keep spend predictable.
• Reliability: Provide enterprise-grade SLAs for latency, throughput, and uptime.
• Governance: Apply consistent controls, guardrails, and audit trails across every use case.
• Speed: Free application teams to innovate quickly without being held back by infrastructure management.

I write about this more in my last blog (here)

Where InferenceOps Lives in the Organization

InferenceOps is not a standalone function - we’ve seen it be most successful when it sits inside the enterprise platform team. That’s the natural place for it to collaborate with use case teams, security, compliance, and infrastructure.

Its role is to act as the center of excellence for inference: the team that owns the standards, infrastructure, and practices that allow every other team to consume inference as a reliable, governed service.

InferenceOps is the backbone of sustainable enterprise AI adoption. Without it, enterprises risk spiralling costs, inconsistent governance, and fragile deployments. With it, they unlock a foundation that makes AI scalable, reliable, and compliant across the organisation.

In my next blog, I’ll explore how enterprises can actually stand up an InferenceOps function - the people, processes, and platforms required to make it real.

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