When does private Ai deployment actually make sense and when is SaaS Ai enough?
Posted by Fit_Shopping2563@reddit | LocalLLaMA | View on Reddit | 3 comments
I keep seeing companies jump straight from we should use Ai to we need a private Ai deployment.
But in practice,those are two very different decisions.
From what I have seen,private AI starts to make sense when at least one of these becomes a real constraint:
- sensitive internal data cannot leave the network
- permission boundaries are complex
- auditability matters
-Ai has to connect to real internal workflows
- the company operates in a regulated environment
Where I think people get it wrong is treating private Ai like an advanced version of using Ai tools.
It is not just a model or deployment choice.
It changes the operating model:
Data boundary ,permission design,logging,workflow integration,cost control,and long-term ownership.
At the same time,I also think a lot of companies overbuild too early.
If the team is small, use case is still basic, and internal workflow are not even standardized yet,SaaS Ai is often the smarter first step.
Curious how others here think about the boundary:
What is the moment when private Ai becomes justified in a real company?
AlwaysLateToThaParty@reddit
If security, maintenance, reproducability, and privacy are important to your organization, local deployment matters.
ambient_temp_xeno@reddit
You missed out: risk of getting banned from a private ai service.
Decent-Occasion-2720@reddit
Je pense que la seule chose qu'une entreprise devrait externaliser ce sont les fournisseurs d'inférences car le coût du matériel est très élever pour le moment.
Le reste devrait être privée et uniquement sélectionner le fournisseur d'inférence en fonction de la sensibilité des données.
Cela dépend du niveau de maîtrise de vos infrastructure que vous voulez avoir, mais faire autrement c'est s'exposer a des forte dépendance a des solutions externe. Aujourd'hui parier sur pérennité d'un service externe pour des activités importante me semble fortement risqué.
Reste à savoir ce que l'on peut considérer ou pas comme sensible, qui sait ce que vont raconter les employer à aux LLM...