To cut through the noise, we spoke with Yoann Veny, Data-Science Manager at Agilytic. Below is a condensed playbook that skips the generic AI primer and goes straight to what matters: open vs. closed source, plus when a hybrid stack makes sense.
1. What exactly counts as an “open-source” LLM?
Level of openness | What you get |
|---|---|
Fully Open (Rarest) | Weights + code + detailed dataset list or raw data. Rare for modern, frontier-scale LLMs due to copyright. |
Open Model | Weights and training recipes. Data often scrubbed but documented. I.e. Mistral |
Open Weights (Most common) | Downloadable model weights + inference code. Training data may be redacted or partially released. I.e. DeepSeek R1, Llama |
For this article, “open source” means at least open weights plus a license that permits commercial self-hosting. Anything that hides the weights (e.g., GPT-4o, Claude 3) we label closed source.
2. Why teams pick open-source LLMs
“If the vendor decides one day to tweak price or performance, you find out once your bill comes in. Self-hosting avoids unexpected fluctuations.” -Yoann
Key advantages
Full Customization & Bias Control: Fine-tune on internal data and inspect layers when results look odd.
Data Sovereignty: No text leaves your firewall, easing GDPR, HIPAA, or bank-secrecy audits.
Predictable costs: Once the hardware amortized, high-volume usage is often cheaper than per-token APIs.
Case in point: (hypothetical but commonplace)
A boutique law firm must summarize thousands of highly-confidential PDFs. They can fine-tune an open-source model on-prem, add a retrieval layer, and achieve sufficiently accurate clause extraction, without a single document touching a provider’s cloud.
Why open wins: Data never leaves site · Legal jargon fine-tuning · Stable hardware costs
3. Why teams pick closed-source LLMs
Closed APIs shine when speed and staff capacity outweigh control concerns.
Key advantages
Days-to-Prod: No hardware or model training required.
Lower cost R&D: Vendors iterate frequently; you inherit the gains.
Elastic Pricing: Pay only for what you call (handy for spikes, iterating or testing).
→ Case in point: Automated Invoice Processing
A logistics company suffered late fees and compliance issues from manual invoice handling. By adopting Azure’s closed-source LLMs, we automated their data extraction and validations. The solution reduced labor costs by €100k/year and cut processing time by 70%.
Why Closed Source Won Here:
Fast Deployment: Existing APIs integrated directly with the company’s systems.
Enterprise Support & SLAs: Microsoft’s reliability and security provisions were critical to the client.
Scalability: Azure cloud handled traffic spikes without needing on-premise hardware upgrades.
4. Side-by-side cheat-sheet
Factor | Open-Source | Closed-Source |
|---|---|---|
Customization | Full fine-tune, weight surgery | Prompt-only or limited adapters |
Up-front effort | GPU infra, Training and Dev | Minimal |
Ongoing cost | Flat (hardware + power) | Variable (per-token) |
Compliance | You control locality & logs | Rely on vendor attestations |
Road-map risk | DIY upgrades | Vendor lock-in / price shifts |
5. Making the call
As Yoann neatly sums it up: “It’s always case by case”.
Go open if data can never leave, you have data science muscle, and workloads are steady.
Go closed for rapid pilots, spiky demand, or when best-in-class accuracy trumps transparency.