How to run LLMs on Google Cloud — what SMEs should plan for

According to iTWire, Google Cloud has expanded its model support and is bringing OpenAI models to customers. The change widens where businesses can access large language models and how teams choose model providers.

That move means small and mid-sized enterprises (SMEs) will have more deployment options for LLMs — from directly calling OpenAI through Google Cloud’s platform to mixing models from different vendors under one cloud contract. Below I walk through what changes for business owners, who is affected, the main risks, and a concrete checklist you can use for pilots and migrations.

How to run LLMs on Google Cloud affects your business

More model choice inside a cloud provider simplifies procurement and integration in some ways: your cloud bill can now include calls to OpenAI models (according to iTWire), and your teams can manage compute, networking and monitoring in one place. For SMEs this lowers friction for testing generative AI workflows, but it also shifts the decision from purely technical (which model performs best) to commercial (which provider, latency and pricing fit your operations).

Who this matters for

• Product teams building customer-facing features (chat, summarisation, search).
• Operations and back-office teams wanting automation in ERPs and SaaS tools.
• CTOs and procurement teams balancing cost, compliance and vendor risk.

Cost and vendor choice — new trade-offs to model

Opening another route to OpenAI models changes the economics and negotiation surface. You should expect new variables to consider:

• Pricing structure: model choice and cloud packaging may change per-call costs and how egress, networking or proxying is billed.
• Consolidated billing vs. best price: one bill with Google Cloud can simplify accounting but might not always be cheapest for heavy usage.
• Vendor lock-in vs. multi-model flexibility: easier access to multiple models encourages experimentation, but integration patterns (APIs, SDKs, auth) can still create operational coupling.

Practical implication: run a cost-per-query and latency model for representative workloads before committing. Test both native cloud model access and direct vendor integration to see which is cheaper and faster for your use case.

Integration with existing ERPs and SaaS — don’t treat LLMs as a bolt-on

If you plan to add LLM-driven features to accounting, inventory, HR or customer systems, think end-to-end: data flows, security, and error handling. Key considerations:

• Data connectivity: build secure connectors between your ERP and the model endpoint. Avoid manual exports — use API-driven pipelines.
• Context and grounding: give the model the right subset of operational data (not full production dumps) and keep auditable logs of model outputs that influence decisions.
• Latency and availability: high SLA business processes need guarantees; measure round-trip times and plan for fallback logic.

If you don’t have these building blocks, consider rebuilding the integration layer — for example custom middleware or modules that sit between your ERP and the model. We build integrations like this for clients who want to add AI to core operations (see our work on /services/custom-erp-development and /services/ai-development).

Risks and controls to put in place now

• Data leakage and compliance: ensure PII and regulated data are handled according to local laws and your contracts. Treat model providers like any third-party processor.
• Unexpected outputs: generative models can hallucinate. Add verification steps before any model-generated content affects financials, legal text or customer-facing statements.
• Cost runaway: set quotas, alerts and budget caps on model calls to avoid surprise invoices.
• Monitoring and observability: log prompts, responses, latencies and errors. This helps debugging and shows model drift over time.

Practical checklist for pilots and migrations

• Define the business outcome: pick a measurable KPI (time saved, response accuracy, conversion lift).
• Prepare a representative dataset: sanitize and sample the real data the model will use.
• Test both routes: direct vendor API vs. model access through your cloud provider. Measure cost, latency and throughput.
• Build minimal integration: API-driven connector, auth, and a sandboxed environment.
• Add safety layers: response validators, PII filters, and human-in-the-loop where decisions matter.
• Run load and failure tests: simulate peak traffic and provider outages.
• Set budgets and alerts: enforce quotas and billing notifications.
• Plan rollback and vendor exit: keep contracts and connectors modular so you can switch providers without rearchitecting core systems.

Next steps for teams ready to pilot

Start with a scoped proof-of-concept tied to a single business goal. Treat the pilot as both a technical and procurement experiment: compare price/performance and integration effort side-by-side. Use the pilot to standardise a pattern — connectors, monitoring dashboards, and safety checks — that you can reuse as you scale.

If you want help defining the pilot, building the ERP connectors or adding automation safely, we do end-to-end projects from POC to production. We can help integrate LLMs with your business systems and build the protections and monitoring you need.

Talk to the team to discuss a pilot or migration plan — book a call at /contact.