The local AI vs cloud AI decision is one of the most consequential infrastructure choices for AI products. Cloud APIs (OpenAI, Anthropic, Google) are easy to start with but expensive at scale. Local models (Llama, Mistral, Gemma) require infrastructure investment but are cheaper at scale. The right choice depends on your volume, privacy requirements, and operational capabilities.
The Cost Crossover
Cloud APIs charge per token: $0.01-$0.06 per 1K tokens depending on model and provider. Local models charge per GPU hour: $1-$10 per hour depending on instance type. The crossover point — where local becomes cheaper than cloud — depends on volume. At 10K requests/day (each with 1K tokens), cloud costs ~$300-$1,800/month. A local GPU instance costs $700-$3,000/month. At this volume, cloud is cheaper. At 100K requests/day, cloud costs $3,000-$18,000/month. A local GPU cluster costs $2,000-$5,000/month. At this volume, local is significantly cheaper.
Privacy and Compliance
Cloud APIs require sending your data to a third-party server. For non-sensitive data, this is fine. For regulated data (healthcare, finance, legal), this may violate compliance requirements. Local models keep all data on your infrastructure — no data leaves your network. If your data is regulated, local is not just preferred — it is required. The compliance cost of cloud AI (DPAs, data processing agreements, audit trails) can add 20-40% to the effective cost.
Operational Complexity
Cloud APIs are operationally simple: make an HTTP request, get a response. No infrastructure to manage, no models to deploy, no GPUs to provision. Local models are operationally complex: you need GPU infrastructure, model deployment and serving (vLLM, TGI), model updates and versioning, monitoring and alerting, and incident response. The operational cost of local AI is significant: budget 0.5-1 FTE for model operations at scale. Cloud APIs require no dedicated model operations.
Note
The operational cost of local AI is often underestimated. Budget 0.5-1 FTE for model operations — this is a real cost that affects the crossover calculation.
Conclusion
Local AI is cheaper at scale (100K+ requests/day) and required for regulated data. Cloud AI is cheaper at low volume and operationally simpler. The crossover point depends on volume, privacy requirements, and operational maturity. Start with cloud, evaluate local when volume justifies the operational investment.
Key Takeaways
- Cost crossover: cloud cheaper below ~50K requests/day; local cheaper above ~100K requests/day
- Cloud APIs: $0.01-$0.06/1K tokens. Local GPU: $1-$10/hour. Crossover depends on volume.
- Regulated data requires local models — cloud APIs may violate compliance
- Operational complexity: local AI requires 0.5-1 FTE for model operations
- Start with cloud, evaluate local when volume justifies the operational investment