ChatGPT vs Local LLMs: When to Use Each

Businesses adopting AI face a fundamental choice: use a cloud service like ChatGPT or run open-source models on their own infrastructure. Each path has real trade-offs in cost, data privacy, capability, and operational complexity. This guide breaks down when each approach makes sense so you can make the right call for your team.

Quick Verdict

ChatGPT: Best for teams that need fast deployment, a polished interface, built-in plugins, image generation, and the highest-quality reasoning out of the box.
Local LLMs: Best for organizations that require full data control, long-term cost savings at scale, fine-tuning on proprietary data, or operate in regulated industries.
Our recommendation: Most Canadian businesses benefit from a hybrid approach — ChatGPT for general productivity, local models for sensitive data workflows.

ChatGPT vs Local LLMs at a Glance

Feature	ChatGPT (Cloud)	Local LLMs (Self-Hosted)
Monthly Cost	~$27 CAD/user/mo (Plus) or ~$34 CAD (Team)	$220–$420 CAD/mo amortized hardware; near-zero per query
Data Privacy	Data sent to US servers; opt-out available	Data never leaves your network — full sovereignty
Output Quality	GPT-4o is best-in-class for reasoning and nuance	70B models reach ~80–90% of GPT-4o on most tasks
Setup Complexity	Sign up and start — zero configuration	Requires GPU hardware, model download, and serving software
Maintenance	Managed by OpenAI — automatic updates	Self-managed; requires IT staff for updates and monitoring
Customization	Custom GPTs, system prompts, limited fine-tuning	Full fine-tuning, LoRA adapters, custom training on your data
Internet Connectivity	Required — cloud-based service	Runs fully offline; ideal for air-gapped environments
Break-Even Point	Cheaper for teams under ~15 users	Cheaper for teams over ~15–25 users

What Are Local LLMs?

“Local LLMs” refers to open-source large language models that you download and run on your own hardware or private cloud infrastructure. Instead of sending prompts to OpenAI’s servers, the model runs entirely within your network. No data leaves your control.

Popular Open-Source Models

• Llama 3.1 (Meta): Available in 8B, 70B, and 405B sizes. Strong general performance
• Mistral / Mixtral (Mistral AI): Excellent reasoning, strong French-English bilingual support
• Qwen 2.5 (Alibaba): Competitive coding and math capabilities
• DeepSeek-V3: Strong reasoning at lower resource requirements

How You Run Them

• Ollama: One-command install, runs models locally with a simple API
• vLLM: High-throughput serving for production workloads
• llama.cpp: CPU-friendly inference for smaller models
• Text Generation WebUI: Browser-based chat interface for local models

Key point: Local LLMs are not a single product — they are an ecosystem. You choose the model, the serving software, and the hardware. This gives you maximum flexibility but requires more technical effort than signing up for ChatGPT.

Head-to-Head Comparison

The table below compares ChatGPT (Plus/Team tier) against self-hosted local LLMs across the dimensions that matter most for business adoption.

Dimension	ChatGPT	Local LLMs
Setup Ease	Sign up and start — zero configuration	Requires hardware, model download, and serving software
Output Quality	GPT-4o is best-in-class for reasoning and nuance	70B models reach ~80-90% of GPT-4o on most tasks
Cost Structure	~$27 CAD/user/month (Plus) or pay-per-token (API)	$8K-$15K CAD upfront hardware, near-zero per query
Data Privacy	Data sent to US servers; opt-out training available	Data never leaves your network — full sovereignty
Customization	Custom GPTs, system prompts, limited fine-tuning via API	Full fine-tuning, LoRA adapters, custom training on your data
Internet Access	Built-in web browsing and real-time search	No internet by default; requires RAG or plugin setup
Image Generation	DALL-E 3 built in	Stable Diffusion or FLUX (separate setup required)
Speed	Fast, optimized infrastructure	Depends on hardware; can match or exceed with good GPUs
Support	OpenAI support, extensive documentation	Community forums, self-managed; no vendor SLA

When ChatGPT Is the Right Choice

ChatGPT remains the strongest option for teams that value convenience, broad capabilities, and minimal setup time. Here are the scenarios where it wins clearly.

Choose ChatGPT when...

Small teams needing quick deployment

If your team is under 20 people and you need AI working today, ChatGPT is the fastest path. No hardware to buy, no models to configure, no IT overhead. Sign up, distribute seats, and start.

You need the plugin and integration ecosystem

ChatGPT’s Custom GPTs, code interpreter, web browsing, file analysis, and third-party plugins provide capabilities that local models cannot replicate without significant development work.

Image generation is a requirement

DALL-E 3 is built into ChatGPT Plus. While you can run Stable Diffusion or FLUX locally, the setup effort and GPU requirements are substantial. For teams generating marketing visuals or product mockups, ChatGPT’s integrated approach is far simpler.

Budget allows per-user subscriptions

At ~$27 CAD/user/month for Plus or ~$34 CAD for Team, the math works for small groups. A 10-person team spends $270-$340 CAD/month — predictable and tax-deductible with no surprise infrastructure bills.

Keep in mind: ChatGPT Plus standard accounts do not guarantee data exclusion from training. If your team handles sensitive data, upgrade to ChatGPT Team or Enterprise, which include data privacy controls and admin management.

When Local LLMs Are the Right Choice

Self-hosted models shine in scenarios where data control, regulatory compliance, or economics at scale make cloud services impractical. These are the use cases where local wins.

Choose local LLMs when...

Sensitive data must stay on-premises

Legal firms processing privileged documents, healthcare organizations handling patient records, and government agencies with classified information cannot risk data leaving their network. Local models keep every byte within your perimeter.

Large teams make per-seat pricing expensive

At 50+ users, ChatGPT Plus costs $1,350+ CAD/month. A local model deployment amortized over 36 months runs approximately $300-$400 CAD/month regardless of user count. The break-even point for most teams is around 15-25 users.

You need to fine-tune on proprietary data

ChatGPT offers limited fine-tuning through the API. Local models let you run full fine-tuning or LoRA adapters on your proprietary datasets — medical terminology, legal precedents, internal product knowledge — for significantly better domain-specific performance.

Regulatory requirements demand data residency

Some Canadian organizations must keep data on Canadian soil. Provincial health information acts (PHIPA in Ontario, HIA in Alberta) and certain federal contracts require data residency that ChatGPT cannot guarantee. Self-hosted models on Canadian servers satisfy these requirements.

Offline or air-gapped environments

Military, critical infrastructure, and certain research facilities operate without internet access. Local models are the only AI option for air-gapped networks. Once downloaded, they run entirely offline.

The Hybrid Approach: Best of Both Worlds

In practice, most organizations do not choose exclusively one or the other. The strongest deployments we see use both — routing different types of work to the best tool for the job.

ChatGPT for general productivity

Give team members ChatGPT access for brainstorming, drafting, research, and ad-hoc analysis. The polished interface and plugin ecosystem make it ideal for interactive, non-sensitive work.

Local models for sensitive workflows

Route tasks involving personal data, privileged documents, or proprietary IP through a self-hosted model. Build internal tools on top of Ollama or vLLM that enforce data boundaries automatically.

A routing layer that decides

Advanced teams build a middleware layer that classifies incoming requests and routes them to the right model. Sensitive queries go local, everything else goes to the cloud API. This maximizes both quality and compliance.

Real example: A 30-person law firm in Vancouver we consulted uses ChatGPT Team for general legal research and marketing content ($34 × 8 seats = $272/mo). For document review involving client privileged information, they run Llama 3.1 70B on a local server ($180/mo amortized). Total: ~$452/mo with full privilege protection.

Canadian Perspective: PIPEDA, Data Residency, and Compliance

Canadian businesses face specific regulatory considerations that make this choice more consequential than in many other markets.

PIPEDA Requirements

Canada’s Personal Information Protection and Electronic Documents Act requires organizations to protect personal information with appropriate safeguards.

• Local LLMs: Full compliance — data stays on your servers
• ChatGPT Enterprise: Compliant with DPA and zero-retention
• ChatGPT Plus: Risk exposure — data crosses to US servers

Data Residency Options

For organizations requiring data to remain in Canada:

• On-premises: Your own hardware in your own facility
• OVH Montreal: GPU cloud instances in Canadian data centres
• AWS ca-central-1: Montreal region with GPU VMs
• Azure Canada East: Toronto region; also offers Azure OpenAI

Provincial Health Acts

Healthcare organizations face additional constraints beyond PIPEDA:

• Ontario PHIPA: Requires health data to remain in Canada
• Alberta HIA: Strict custodian obligations for health info
• Quebec Law 25: Enhanced privacy requirements since 2024
• Local models are the safest path for all three

Government & Defence

Federal and provincial government AI use often mandates:

• Data processed exclusively on Canadian infrastructure
• No third-party access to prompts or responses
• Audit trails for all AI interactions
• Local LLMs satisfy all three; ChatGPT does not

Cost Break-Even Analysis

The economics shift depending on team size. Here is when local models become cheaper than ChatGPT subscriptions.

Team Size	ChatGPT Plus (monthly)	Local LLM (monthly)*	Monthly Savings
5 users	$135	$280	-$145 (ChatGPT wins)
15 users	$405	$350	+$55 (break-even zone)
25 users	$675	$380	+$295 (local wins)
50 users	$1,350	$420	+$930 (local wins)
100 users	$2,700	$500	+$2,200 (local wins)

* Local LLM costs assume a 70B-class model on dedicated hardware ($12,000 CAD) amortized over 36 months, plus $50-$80/mo for electricity, maintenance, and IT time. Larger teams may need additional GPUs, reflected in higher amortized costs at 50+ and 100+ user tiers.

The hidden cost factor

Local LLMs require IT expertise to deploy, maintain, update, and troubleshoot. If you do not have in-house ML or DevOps staff, factor in $5,000-$15,000 CAD for initial setup consulting and ongoing maintenance costs. Our AI integration service includes deployment and training for Canadian teams.

Frequently Asked Questions

Can local LLMs match ChatGPT quality for business tasks?

Top open-source models like Llama 3.1 70B and Mixtral 8x22B handle summarization, drafting, and internal Q&A well. They typically reach 80-90% of GPT-4o quality on routine business tasks. For complex multi-step reasoning or creative writing, ChatGPT still holds an edge. Many teams use both: local models for everyday work and ChatGPT for harder problems.

How much does it cost to run a local LLM for a business?

Hardware for a capable 70B-parameter model costs $8,000-$15,000 CAD upfront (workstation with dual GPUs and 64 GB RAM). Amortized over 36 months, that is roughly $220-$420 CAD/month including electricity. Alternatively, Canadian GPU cloud instances cost $2-6 CAD/hour. For teams of 50+ users the per-user cost drops well below ChatGPT subscriptions.

Is a local LLM better for PIPEDA compliance than ChatGPT?

Yes. With a local LLM, data never leaves your infrastructure, giving you full control over personal information as required by PIPEDA. ChatGPT sends data to US servers by default. For regulated industries like healthcare, legal, and finance, self-hosted models provide the strongest compliance posture. Azure OpenAI with a Canada East region is a middle-ground option.

What hardware do I need to run a local LLM?

For a small 7B-parameter model: a workstation with 16 GB RAM and a consumer GPU (RTX 4070 or better), costing around $2,000 CAD. For a strong 70B model: 64 GB RAM and one or two high-end GPUs (NVIDIA A6000 or dual RTX 4090), costing $8,000-$15,000 CAD. Cloud alternatives include GPU VMs on OVH Montreal or AWS ca-central-1 starting at $2 CAD/hour.