| Cost |
Typically pay-per-token or subscription ($20-200/month); zero upfront compute cost; scales automatically with usage. |
High upfront cost for GPU hardware ($2K-8K) or local server setup; ongoing electricity ($20-100/month), maintenance (2-10 hours/month), and model updates; low marginal cost per query once running. |
| Accuracy / Capability |
Access to frontier models (GPT-4/5, Gemini 1.5, Claude 3.5) trained on massive corpora; consistent and state-of-the-art reasoning; regular capability improvements. |
Quality limited by open-weight model performance (Llama-3, Mistral, etc.); generally 6-18 months behind frontier models; dependent on local fine-tuning and compute constraints; may require different models for different tasks. |
| Latency |
Network round-trip delays; variable depending on load and region. |
Millisecond-level response when local compute is sufficient; can degrade under large context or constrained hardware. |
| Security / Privacy |
Data leaves device; governed by provider's TOS, logs, and compliance regime. |
Data stays local; user controls memory, logs, and encryption. Vulnerable to endpoint compromise and poor key management. |
| Update Cycle |
Automatic access to newest model iterations and safety patches. |
Manual model upgrades; risk of version drift, dependency conflicts, or stale weights. |
| Integration / Orchestration |
Easy API and plugin ecosystem; managed scaling; extensive documentation and community support. |
Requires manual orchestration (RAG, vector DB, embedding pipelines); higher setup and maintenance burden; limited tooling ecosystem; significant technical expertise required for optimization. |
| Compliance / Auditability |
Vendor certifications (SOC-2, GDPR, HIPAA) but limited transparency into inference data flow. |
Full visibility and control of logs and storage; compliance responsibility shifts entirely to user. |
| Collaboration / Scalability |
Multi-user environments, shared workspaces, and cloud state synchronization. |
Difficult to scale; requires local synchronization, peer-to-peer federation, or on-prem orchestration. |
| Energy / Environmental Cost |
Centralized data center efficiency; opaque carbon footprint. |
Decentralized compute; potentially higher per-query energy use on consumer hardware. |