AI Architecture · Explained
Open vs Closed
Two philosophies shaping how artificial intelligence is built, shared, and deployed across the world.
Open Source / Open Weight
Open Models
The model weights — the billions of numerical parameters learned during training — are released publicly. Anyone can download, run, fine-tune, and redistribute the model without restriction (or under a license). The underlying architecture and, often, training code are also visible.
Proprietary / API-Only
Closed Models
The model weights are private and never released. Users interact with the model exclusively through an API or product interface. The developer retains full control over how the model is accessed, modified, and monetized. Internal details are trade secrets.
Side-by-Side Comparison
Dimension
Open
Closed
Access
Download & run locally — no API needed
API-only; access requires account & payment
Cost
Free to use (compute costs only)
Pay-per-token or subscription fees
Privacy
Data never leaves your own hardware
Prompts sent to provider’s servers
Customization
Full fine-tuning, RLHF, quantization, merging
Limited to system prompts & fine-tune APIs (if offered)
Performance
Frontier capability gap; catching up fast
Currently leads on most benchmarks
Safety Controls
Guardrails can be modified or removed
Provider enforces consistent policies
Transparency
Weights & code inspectable by researchers
Internal architecture is opaque
Uptime / Reliability
Self-hosted; you manage infrastructure
Provider manages scaling & reliability
Iteration Speed
Community-driven; rapid experimentation
Controlled release cycle
Notable Models
Open / Open-Weight
Llama 3 / 3.1 / 3.2
Meta AI · Community licence
Mistral / Mixtral
Mistral AI · Apache 2.0
Falcon 180B
TII UAE · Open licence
Qwen 2.5 / Qwen-VL
Alibaba Cloud · Apache 2.0
DeepSeek R1
DeepSeek · MIT licence
Phi-3 / Phi-4
Microsoft Research · MIT
Gemma 2
Google · Gemma licence
Closed / Proprietary
GPT-4o / o1 / o3
OpenAI · API-only
Claude 3 / 4 family
Anthropic · API + claude.ai
Gemini 1.5 / 2.0 Ultra
Google DeepMind · API-only
Grok 2 / 3
xAI · API + X platform
Command R+
Cohere · Enterprise API
Nova / Titan
Amazon Bedrock · API-only
Copilot / Phi (hosted)
Microsoft · Products & API
Key Tradeoffs
Safety & Alignment
OPENResearchers can audit weights and probe for hidden behaviors; but bad actors can also strip safeguards.
CLOSEDProvider consistently enforces safety policies; but users must trust the company’s claims.
Enterprise & Compliance
OPENOn-premise deployment satisfies strict data-residency and air-gap requirements.
CLOSEDRobust SLAs, legal agreements, and SOC 2 certifications; data leaves the building.
Raw Capability
OPENTop open models (Llama 3.1 405B, DeepSeek R1) rival mid-tier closed models but rarely match the frontier.
CLOSEDFrontier closed models still lead on reasoning, coding, and multimodal tasks as of 2025.
Economics
OPENZero API cost; only GPU/cloud compute. Economical at high volumes once infrastructure is set up.
CLOSEDPay-as-you-go with zero setup; becomes expensive at very high token volumes.
Geopolitics & Power
OPENDistributes AI capability globally; reduces dependency on a handful of US tech companies.
CLOSEDConcentrates power; raises concerns about AI access inequality and single points of failure.
Research & Innovation
OPENEnables academic research, reproducibility, and rapid community iteration on top of base models.
CLOSEDResearch done internally; external researchers must rely on API access and papers alone.
The Openness Spectrum
