In this guide
Key differences
Cloud AI sends requests to a provider-operated service. The provider operates inference infrastructure and you pay under its subscription or usage model. Local AI runs on hardware you control; your team manages hardware, runtime, access, updates, monitoring and capacity.
| Factor | Cloud API | Local inference |
|---|---|---|
| Initial cost | Usually low | Hardware and setup investment |
| Scaling | Provider capacity, quotas and rate limits | Add hardware or tune concurrency |
| Operations | Provider manages serving infrastructure | You manage drivers, runtime and availability |
| Data path | Prompts leave your environment for the service | Can remain on-premises if the whole workflow is local |
| Model choice | Provider catalogue and service terms | Compatible open-weight models and licences |
| Performance | Network and provider dependent | GPU, memory bandwidth, runtime and context dependent |
Compare total cost—not just token price
Cloud spend includes input/output tokens, cache eligibility, batch discounts, tool calls, embeddings, retries, service tiers and fixed fees. Local cost includes hardware, electricity, cooling, support, maintenance, spare capacity, deployment time and staff effort. Idle local hardware still costs money; highly utilised hardware may make more economic sense.
Quality matters too. If a smaller local model needs more retries, human review or a second model, the apparent saving may disappear. Use the AI API Cost Calculator to compare provider quotes and growth, then use the Local AI Hardware Calculator for memory requirements and an illustrative cost comparison. Enter current provider prices yourself.
Privacy and governance
Local hosting can reduce external data transfers, but it is not a complete security control. Restrict model endpoint access, protect logs and stored prompts, patch the host, segment the service, manage model files and review connected tools and retrieval sources.
For hosted services, review data processing terms, retention, training use, region, subprocessors, access controls, audit features and deletion. Classify data before choosing a platform; neither deployment choice replaces a required data-protection assessment or organisational policy.
Hardware and performance
Model weights are only part of memory use. Context length and concurrent sequences add key-value (KV) cache; the runtime needs temporary buffers and overhead. Quantisation reduces weight memory but can affect quality and adds metadata. A model fitting in VRAM is not necessarily fast enough: benchmark realistic prompts and concurrency on target hardware.
CPU offload can make a larger model load but may create significant latency. Multi-GPU VRAM is not always fully additive. Use the Local AI Hardware Calculator as a planning estimate, not a compatibility or tokens-per-second guarantee.
Decision framework
| Priority | Starting point | Validate |
|---|---|---|
| Fast proof of concept | Cloud API | Data terms, quotas, quality and spend |
| Variable workload | Cloud API | Rate limits, availability and cost controls |
| Offline or isolated operation | Local inference | Licence, updates, hardware and support |
| Strict data locality | Local or approved private deployment | Full data path, logs and integrations |
| High predictable volume | Compare both | Utilisation, amortisation, throughput and quality |
| Complex high-stakes tasks | Evaluate multiple candidates | Representative accuracy and failure tests |
Practical rollout for IT teams
- Define the task, users, acceptable error rate, latency and success criteria.
- Classify data and decide what may be sent to a provider.
- Create a representative evaluation set and measure output quality.
- Model token volume, discounts, workload growth, hardware and operating costs.
- Test prompt injection, data leakage, access control, logging and outages.
- Pilot with human oversight and monitor quality, latency, cost and feedback.
- Review prices, model versions, licences, patches and whether the deployment still meets the need.