When to Run AI Locally — a practical decision guide

Local AI and cloud AI are not competing approaches — they are tools for different situations. The right choice depends on your specific constraints around data, budget, scale, and team capability. This guide provides a structured framework to make that decision.

The decision framework

Answer these six questions honestly. Your answers will point clearly toward local AI, cloud AI, or a hybrid approach.

6 key questions

1. How much data will you process?

Volume is the single biggest factor in the local-vs-cloud decision.

• Low volume (<1M tokens/month): Cloud AI is cheaper. No hardware investment needed.
• Medium volume (1-50M tokens/month): It depends on your hardware and model choices. A used RTX 3090 ($700) can break even in 6-18 months.
• High volume (>50M tokens/month): Local AI is almost certainly cheaper. The marginal cost of electricity is negligible compared to per-token cloud pricing.

Use our self-hosting breakeven calculator for an exact comparison.

2. How sensitive is your data?

• Public or low-sensitivity (marketing copy, public data): Cloud AI is fine. Privacy risk is minimal.
• Internal business data (emails, internal docs, strategy): Cloud AI can work with a trusted provider, but local AI is safer.
• Regulated data (PII, PHI, financial records, legal documents): Local AI is the only option that guarantees zero data exposure. Many regulations explicitly prohibit sending certain data to third-party AI services.

3. What scale do you need?

• Single user: Local AI handles this easily.
• Small team (2-10): Local AI works if you have a shared GPU server. Otherwise, cloud AI is simpler.
• Organisation-wide (50-1000+): Local AI requires significant infrastructure. Cloud AI is usually more practical unless privacy mandates local deployment.

4. What latency do you need?

• Real-time (<500ms, e.g., chat, coding autocomplete): Local AI excels. No network latency.
• Interactive (1-5s, e.g., search, analysis): Both work well. Cloud AI is acceptable.
• Batch (minutes to hours): Both work. Cost is the deciding factor.

5. What is your budget?

• Zero upfront, pay as you go: Cloud AI. No hardware purchase.
• Can invest upfront for long-term savings: Local AI if your volume justifies it.
• Predictable monthly spending: Both work. Plugsky offers flat-rate plans for predictable cloud spending.

6. What is your technical capability?

• Non-technical: Cloud AI. No GPU drivers, no model downloads, no troubleshooting.
• Developer: Both. Ollama makes local AI easy. Cloud API is even easier.
• Infrastructure team: Local AI is feasible. You have the skills to manage GPU servers, model deployments, and monitoring.

When local AI wins

Choose local AI when any of these describe your situation:

• Privacy-critical. Your data is regulated, confidential, or proprietary. No exceptions — local AI is the only option.
• High volume, predictable workload. You process millions of tokens per day with steady demand. The upfront GPU cost pays for itself in months.
• Offline required. You need AI in environments without reliable internet — remote sites, air-gapped facilities, field operations.
• Learning and experimentation. You want to understand how models work, try different quantisation levels, fine-tune, or build custom tooling around AI.
• Development phase. During active development, local AI gives instant feedback, zero cost for iterations, and complete privacy for test data.

When cloud AI wins

Choose cloud AI when any of these describe your situation:

• Low volume. You use AI occasionally. Buying a GPU would never pay for itself.
• Need the largest models. Your use case requires GPT-4o, Claude 3.5, or other frontier models that have no local equivalent.
• Variable scale. Your traffic spikes unpredictably. Cloud auto-scaling handles this; local capacity planning cannot.
• Team access. Multiple people in your organisation need AI access from different locations. A shared cloud account is simpler than a shared GPU server.
• No GPU. You do not own a compatible GPU and are not ready to invest. Modern laptops can only run the smallest models.
• Quick start. You need AI working today, not after hardware arrives and drivers are configured.

The hybrid scenario

Many teams discover that the answer is not "either/or" but "both." A hybrid approach works like this:

1. Develop locally. Use Ollama during development and testing. No API costs, no rate limits, fast iteration, complete privacy for test data.
2. Deploy to the cloud. When you go to production, switch to Plugsky or another cloud API. Get access to larger models, auto-scaling, team management, and reliability.
3. Switch with one line. Both Ollama and Plugsky use the OpenAI-compatible API. The change is literally: base_url: "http://localhost:11434/v1" → base_url: "https://api.plugsky.com/v1".

This gives you the best of both worlds: local development velocity with cloud production scale.

Cost breakeven analysis reference

Monthly tokens	Cloud cost (Plugsky flat)	Cloud cost (per-token API)	Local AI cost (amortised)	Winner
1M	$5	$0.15-3	$50-100	Cloud
10M	$29	$1.50-30	$80-150	Cloud
50M	$99	$7.50-150	$120-250	Tie (depends on HW)
100M	$199	$15-300	$150-350	Local (with used GPU)
500M	$999	$75-1500	$300-700	Local

Local AI cost assumes a $1,500 GPU amortised over 24 months + $0.15/hour electricity at 10 hours/day. Cloud per-token cost assumes $0.15/1M input tokens (mid-range API pricing). Plugsky flat rate based on current published plans. Run your exact numbers in the breakeven calculator.

Frequently asked questions

When should I run AI locally?

Run AI locally when you need maximum privacy, have predictable high-volume workloads, operate offline, or want to learn how models work. Local AI is also ideal for development and testing before deploying to production.

When should I use cloud AI instead of local AI?

Use cloud AI when you need the largest models (GPT-4o, Claude 3.5), have variable or unpredictable traffic, need team-wide access, lack GPU hardware, or want zero maintenance. Cloud AI is also better for quick prototyping.

How do I calculate if local AI is cheaper than cloud AI?

Compare your monthly token volume against the amortised cost of GPU hardware plus electricity. At roughly 50M+ tokens per month, local AI is usually cheaper. Use our self-hosting breakeven calculator for an exact comparison based on your hardware and usage.

Can I start with local AI and move to cloud later?

Yes. Because Ollama and Plugsky both use the OpenAI-compatible API, you can develop locally and deploy to the cloud with a one-line change to base_url. This hybrid approach is recommended for teams that want privacy during development and scale in production.

What if I do not have a GPU?

Without a GPU you are limited to small models (1B-3B) running on CPU. For anything beyond basic tasks, cloud AI is the practical choice. Plugsky offers 30+ models with no hardware requirements.