Lesson 532 of 2116
When Local LLMs Make Sense vs Cloud: The Decision Framework
A clear framework for deciding, per workload, whether local or cloud is the right answer — and when a hybrid is best.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1Stop comparing models. Compare workloads.
- 2decision framework
- 3total cost of ownership
- 4data sensitivity
Concept cluster
Terms to connect while reading
Section 1
Stop comparing models. Compare workloads.
The 'is local better than cloud' debate is the wrong frame. The right frame is: per workload, which fits better? The same team can run cloud-frontier coding assistants and a local PII redactor and a hybrid RAG, all in production, all justified by the workload. Decide one workload at a time.
Five questions per workload
- 1Sensitivity: would I be uncomfortable handing this data to a third party even with a contract?
- 2Capability: does the task require frontier-level reasoning that local models cannot match?
- 3Volume: am I running enough queries that per-token cloud cost dominates hardware cost?
- 4Latency: do I need sub-100ms time-to-first-token at the office?
- 5Operational maturity: do I have the people to run a model server like a real service?
Compare the options
| Workload | Recommendation | Why |
|---|---|---|
| Customer-facing chatbot, frontier reasoning needed | Cloud | Capability dominates |
| Internal PII-redaction microservice | Local | Sensitivity dominates |
| Coding assistant for individual developer | Cloud or hybrid | Capability matters; data is mixed |
| Healthcare chart summarization | Local or trusted private cloud | Compliance dominates |
| Ad-hoc analyst exploration | Cloud | Capability + low volume |
| Logging-pipeline classifier | Local | High volume, simple task |
| Real-time game NPC dialogue | Local | Latency and cost dominate |
Operational realities people forget
- Local models do not auto-update — you decide when to upgrade. Pro and con
- Local servers need monitoring, restarts, GPU drivers, and security patches
- Models that work fine on a developer laptop fail under real load — load test before launch
- Vendor outages happen; so do GPU failures. Both need a runbook
A short worksheet
- 1List your team's top five LLM-using workloads
- 2Score each on the five questions (1-5)
- 3Recommend cloud, local, or hybrid for each — with one sentence of reasoning
- 4Identify the one workload where switching to local would make the biggest difference
Apply this
- Run the worksheet for your real workloads
- Build the hybrid prototype for whichever one had the strongest local case
- Decide one rollback criterion in writing before launching
Key terms in this lesson
The big idea: local vs cloud is a workload question, not a worldview. Score the workloads, build the hybrid, and let the architecture follow the data.
End-of-lesson quiz
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