What Are the Drawbacks of a Local LLM, and When Is the Cloud the Better Choice?
Local LLMs have five drawbacks to face honestly: hardware must be bought upfront; capability usually trails the latest cloud models, because the models company hardware can run are usually smaller than the largest ones; someone must maintain, update, and monitor the system; and when usage is low or uneven, the hardware sits idle and the cost is hard to justify; and power, cooling, floor space, hardware depreciation and replacement, and backup and disaster recovery all become the company's responsibility. Running on-premises also does not automatically solve prompt injection or permission settings. So when data may leave the company, usage is small, the newest capabilities are needed, or no one can maintain the system, the cloud is often the better fit; a common approach is to mix both according to data classification.
1. Five drawbacks
| Drawback | Why | How to reduce it |
|---|---|---|
| 1. Upfront hardware cost | A model runs well only if it fits entirely in GPU memory, and larger models need more expensive hardware. With the cloud, you pay for what you use and buy no hardware upfront. | Start with one workflow and size the hardware from actual usage. See What hardware does a company need to run a local LLM? |
| 2. A capability gap | Stanford's AI Index 2025 reports that the gap between open-weight and closed models on some benchmarks shrank from 8% to 1.7% in one year (2024 data). The gap is narrowing, but company hardware usually cannot run the largest open models. | Test with the company's own questions, not leaderboards. See Is a local LLM smart enough? |
| 3. Someone must maintain it | NIST SP 800-40 Rev. 4 frames patching as preventive maintenance and a regular cost of using technology. Local models, operating systems, drivers, and surrounding packages all need someone to update, monitor, and back them up. | Decide upfront who is responsible and how often checks happen; pin versions and test before updating. |
| 4. Poor value at low or uneven usage | Hardware capacity is fixed: when usage is low, the hardware sits idle; at peak times, many users at once slow responses down. | Estimate normal and peak usage first, then choose on-premises, cloud, or a mix. |
| 5. Asset responsibilities return to the company | Hardware needs power, cooling, and space; it depreciates and must be replaced after some years; and backup and disaster recovery (restoring after a hardware failure or facility incident) become the company's job rather than a cloud provider's. | Plan power and cooling, a replacement schedule, backup locations, and restore drills before deployment. |
Points 4 and 5 are practical judgments, not conclusions of the sources above.
2. On-premises does not make it secure by itself
Putting the model inside the company answers "does the data leave?" Many of the risks in OWASP's 2025 Top 10 for LLM applications have nothing to do with where the model runs. Prompt injection (LLM01): if a document or web page the AI reads hides instructions, a local model is affected too. Excessive agency (LLM06): if an AI Agent has too much permission, a local deployment can fail the same way. These are handled with permission design, human approval, and testing. See What is prompt injection? and How should AI Agent permissions be set?
3. When the cloud is the better fit
These are practical judgments:
- The data may leave the company. The work involves public data or marketing copy with no personal or confidential information.
- Usage is small or still a trial. If you do not yet know whether it will be used daily, trying it in the cloud first is safer than buying hardware.
- The newest capabilities are needed. The work needs capabilities only the latest cloud models provide, and local models tested with your own questions really fall short.
- No one can maintain it. If no staff member or vendor is responsible for updates and monitoring, unattended local hardware can be riskier.
- Demand is very uneven. Large volumes arrive occasionally, with almost no use in between.
4. Mixing both by data classification
A common approach is not to choose one or the other but to classify data: confidential and personal data go on-premises, general work goes to the cloud. Point 4 of the National Science and Technology Council's reference guidelines for the Executive Yuan follows similar logic: confidential and personal information must not normally be given to generative AI, while closed, on-premises deployments may be used by confidentiality level after the environment is confirmed secure. For how to classify, see What are the risks when employees paste company data into ChatGPT?; for the benefits of local LLMs, see What are the advantages of a local LLM?
Think BIG builds both deployment options
Think BIG (Think BIG Make it Real Co., Ltd.) is an enterprise AI Agent deployment provider headquartered in Taiwan. Starting from protecting company data, we build AI Agents inside the client's own environment (company hardware, or a cloud server under the company's account). There are two deployment options: with the on-premises version, models and documents are processed on company hardware; with the cloud-assisted version, employees' questions and the document passages the system retrieves are sent to the model provider for processing.
- Decided by test questions, not demos: people who know the business write test questions, correct sources, and acceptable answer ranges, and each answer is checked before deciding whether a local model is good enough.
- API accounts under the company's name: with the cloud-assisted version, the company applies for the model API under its own account, under the provider's terms, so billing and usage records stay with the company.
- Re-verify after changing models: when a model is changed, the workflow and output quality are re-verified, and whether existing settings still apply is decided by test results.
See On-premises AI and data sovereignty and AI Agent acceptance framework.
The actual scope of Think BIG's services follows what both parties confirm.
FAQ
- Q1: What are the drawbacks of a local LLM?
- There are five main ones: upfront hardware cost; capability that usually trails the latest cloud models; the need for someone to maintain, update, and monitor it; poor value when usage is low or uneven, because hardware capacity is fixed; and responsibility for power, cooling, space, hardware replacement, and backup and disaster recovery returning to the company. Running on-premises also does not automatically solve prompt injection or permission problems.
- Q2: How far behind cloud models are local models?
- It depends on the task. Stanford's AI Index 2025 reports that the gap between open-weight and closed models on some benchmarks shrank from 8% to 1.7% in one year (2024 data), but company hardware usually cannot run the largest models, so test with the company's own questions to see the real gap.
- Q3: When is the cloud the better choice?
- Usually when the data may leave the company, usage is small or still a trial, the newest capabilities are needed and local models tested short, no one can maintain the system, or demand is very uneven.
- Q4: Can on-premises and cloud be mixed?
- Yes. A common approach is to classify data: confidential and personal data go on-premises, general work goes to the cloud. Write the classification rules down so employees know which data may be sent to the cloud.
- Stanford HAI, The 2025 AI Index Report (2024 data: gap between open-weight and closed models shrank from 8% to 1.7%): hai.stanford.edu
- NIST SP 800-40 Rev. 4, Guide to Enterprise Patch Management Planning: Preventive Maintenance for Technology (2022): nvlpubs.nist.gov
- OWASP Top 10 for LLM Applications 2025 (LLM01 Prompt Injection, LLM06 Excessive Agency): genai.owasp.org
- Executive Yuan Council agenda item: Reference Guidelines for the Use of Generative AI by the Executive Yuan and Its Agencies (2023-08-31): www.ey.gov.tw
- National Science and Technology Council press release with the full guideline text, point 4 (2023-08-31): www.ey.gov.tw
This article summarizes public official documents, laws, and security standards to help companies evaluate AI adoption. It is not legal advice. For Taiwan law, the latest text on the Laws & Regulations Database prevails; external documents, prices, and specifications follow their original sources. The actual scope of Think BIG’s services depends on what both parties confirm.