The local vs cloud LLM decision isn’t binary anymore. After building systems with both approaches, I’ve found that the best architectures usually combine them strategically. Here’s the framework I use for these decisions.

The Real Question

It’s not “local or cloud?” but rather:

• Which tasks benefit from local inference?
• Which tasks require cloud capabilities?
• How do you route between them intelligently?

Understanding this reframe changes how you approach the decision.

Capability Gap Reality Check

Let’s be honest about current limitations:

What local models do well:

• Code completion and assistance
• Structured data extraction
• Simple classification tasks
• Privacy-sensitive processing
• High-volume, simple queries

What cloud models do better:

• Complex reasoning chains
• Long-context understanding
• Multi-modal processing
• Novel problem solving
• Tasks requiring latest training data

The gap is narrowing but it exists. Pretending otherwise leads to production failures.

Quick Decision Table

Factor	Favors Local	Favors Cloud
Data sensitivity	High PII/proprietary	Public data
Query volume	10,000+ per day	Bursty traffic
Complexity	Simple, structured	Complex reasoning
Latency requirements	Sub-50ms needed	1-3s acceptable
Budget	Predictable preferred	Pay-per-use OK
Uptime requirements	Can’t depend on internet	SLA acceptable
Context length	<4K tokens typical	100K+ tokens needed

Cost Analysis Framework

The math is more nuanced than “local is cheaper for high volume.”

Cloud Cost Calculation

For a typical application (1,000 queries/day):

Input tokens: ~500 average per query Output tokens: ~200 average per query

GPT-4o costs:

• Input: 500K tokens × $2.50/1M = $1.25/day
• Output: 200K tokens × $10/1M = $2.00/day
• Monthly: ~$97.50

Claude Sonnet costs:

• Input: 500K tokens × $3/1M = $1.50/day
• Output: 200K tokens × $15/1M = $3.00/day
• Monthly: ~$135

See the LLM API cost comparison for detailed pricing breakdowns.

Local Cost Calculation

Hardware options:

1. Consumer GPU (RTX 4090, $1,800):

   • Runs 7B-13B models well
   • Power: ~400W under load
   • Electricity: ~$30-50/month at full utilization
   • Amortized hardware: ~$50/month over 3 years

2. Cloud GPU (A100, ~$2/hour):

   • Runs any model
   • On-demand: ~$1,440/month at 24/7
   • Spot instances: ~$500-800/month

Break-even analysis:

Local consumer hardware beats cloud API at roughly 5,000+ complex queries per day OR 50,000+ simple queries per day.

But this ignores opportunity cost, maintenance, and capability differences.

Privacy and Compliance Considerations

When local is mandatory:

• Healthcare data under HIPAA without BAA
• Financial data with strict data residency
• Government contracts with data sovereignty requirements
• Any “data must not leave premises” policy

When cloud is acceptable:

• Public information processing
• Enterprise API agreements with SOC2/HIPAA compliance
• Anonymized or synthetic data
• User-consented processing

The AI security implementation guide covers data protection patterns.

Latency Comparison

Local inference latency:

• First token: 50-200ms (depends on model size)
• Per token: 20-50ms for 7B models
• Total for 200 tokens: ~4-10 seconds

Cloud API latency:

• Network round-trip: 50-200ms
• First token: 200-500ms (queue + inference)
• Per token: 10-30ms (faster hardware)
• Total for 200 tokens: ~3-8 seconds

Counterintuitively, cloud can be faster for generation due to better hardware. But local wins if you need guaranteed latency without network variability.

Hybrid Architecture Patterns

Pattern 1: Complexity-Based Routing

Route simple queries locally, complex queries to cloud:

Local handling:

• Classification (spam, sentiment, intent)
• Entity extraction
• Format conversion
• Simple Q&A with provided context

Cloud handling:

• Multi-step reasoning
• Creative generation
• Queries requiring broad knowledge
• Tasks where quality is critical

Pattern 2: Privacy-Based Routing

Route based on data sensitivity:

Local processing:

• Any query containing PII
• Proprietary code or documents
• Internal communications
• Customer data

Cloud processing:

• Public information
• Anonymized aggregations
• Generic assistance
• Research queries

Pattern 3: Cost-Based Routing

Route based on budget optimization:

Local for high-volume:

• Embedding generation
• Bulk classification
• Repetitive formatting tasks
• Cache-miss handling for common queries

Cloud for high-value:

• User-facing chat
• Quality-critical outputs
• Complex analysis
• Features that drive revenue

The AI cost management architecture guide covers implementation details.

Implementation Considerations

Local Infrastructure Requirements

Minimum viable:

• 16GB RAM
• GPU with 8GB+ VRAM
• 100GB+ SSD for models
• Stable power

Recommended:

• 32GB+ RAM
• 24GB+ VRAM (RTX 3090/4090 or better)
• NVMe storage
• UPS for uptime

See the VRAM requirements guide for detailed specs.

Cloud Provider Considerations

OpenAI:

• Best overall capability
• Most expensive tier
• Good reliability

Anthropic (Claude):

• Strong reasoning
• Better long-context
• Growing reliability

Google (Gemini):

• Competitive pricing
• Good multimodal
• Flash model very fast

Open providers (Together, Fireworks):

• Open model access
• Lower cost
• Variable quality

Migration Strategies

Starting Local, Adding Cloud

1. Build with local first for cost control
2. Identify tasks where local falls short
3. Add cloud routing for those specific tasks
4. Monitor and adjust routing thresholds

Starting Cloud, Adding Local

1. Build with cloud for capability
2. Identify high-volume/simple tasks
3. Deploy local for those workloads
4. Gradually shift traffic as confidence grows

Decision Framework Summary

Go local-first when:

• Privacy is non-negotiable
• Volume is predictably high
• Tasks are well-defined and simple
• Budget predictability matters
• You have infrastructure expertise

Go cloud-first when:

• Quality is paramount
• Requirements are evolving
• Traffic is unpredictable
• Multimodal needed
• Team is small/time-constrained

Go hybrid when:

• Both cost and quality matter
• Privacy requirements vary by data type
• You have engineering capacity to manage complexity

My Recommendation

Most production systems should plan for hybrid from day one. Design your abstraction layer to support multiple backends, even if you start with just one.

This gives you:

• Flexibility to optimize later
• Fallback options during outages
• Ability to A/B test providers
• Future-proofing as the landscape changes

The build vs framework decision guide covers abstraction strategies.

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Want deeper analysis on local vs cloud trade-offs?

I cover real implementation patterns on the AI Engineering YouTube channel.

Discuss architecture decisions with experienced engineers in the AI Engineer community on Skool.