Self Hosted AI vs Managed Private AI for Business

If you want private AI and you are choosing between self hosting an open source model and buying a managed private AI deployment, the honest answer for most companies of 10 to 200 people is managed. Self hosting gives you the most control and the lowest cost per token, but it turns AI into a standing engineering project: servers, security, access controls, and maintenance that never stops. Managed private AI gives you the same private result, your data in your own environment, without the build and the upkeep. This guide breaks down both paths so you can choose with your eyes open.

What self hosting AI actually involves

Self hosting means your company owns the entire stack. On paper it looks cheap because open source models are free to download. In practice the model is the smallest part of the job. You are signing up to build and run a production system that your business will come to depend on.

• Compute: renting and sizing GPU instances, then paying for them whether usage is high or idle
• Serving stack: standing up inference servers, load balancing, and failover so the assistant stays up
• Data pipeline: connecting your documents, chunking and indexing them, and keeping that index fresh
• Access and identity: wiring single sign on, role based permissions, and audit logging yourself
• Security: patching the stack, managing secrets, and passing the security reviews your clients send you
• Maintenance: model upgrades, dependency updates, and someone on call when it breaks

None of this is exotic for a company with a platform engineering team and the scale to justify it. For a professional services firm, an agency, or an accounting practice, it is a headcount and a risk you probably do not want on your books.

What managed private AI involves

Managed private AI flips the ownership. A provider stands up a dedicated cloud environment for your company, connects it to your data, secures it, and then operates it as a service. Your data still lives in your own environment, so you keep the privacy benefit, but you do not carry the build or the maintenance. Your side of the work is deciding which data sources to connect and who gets access.

• The provider provisions a dedicated environment and applies your branding
• Your documents and systems are connected and indexed inside your environment
• Role based access, encryption, audit trails, and a SOC2 friendly architecture are configured for you
• Model calls run under enterprise agreements with zero data retention, so nothing trains a public model
• Hosting, monitoring, updates, and user support are handled on an ongoing basis

Cost: the comparison that matters

Self hosting looks cheaper per token and gets more expensive once you count the people. GPU bills are only the start. The real cost is the engineering time to build the system and the ongoing salary to keep it running, plus the opportunity cost of that team not working on your actual business.

Managed private AI is priced like infrastructure plus seats: a monthly platform fee that covers your dedicated environment, hosting, and maintenance, starting around $500 per month, plus a per seat rate in the $20 to $25 range. There is no engineer to hire and no build to fund. For a full breakdown and a comparison against public tools, see the pricing page.

Control and customization

This is the one area where self hosting genuinely wins. If you need to fine tune a model on proprietary data, run fully air gapped with no external calls, or customize the serving layer, owning the stack gives you that freedom. Most companies do not need that level of control. They need a private, branded assistant that answers from their documents and respects their access rules, which a managed deployment delivers without the engineering commitment.

Security and compliance

Both paths can be secure. The difference is who is responsible for making them secure and keeping them that way. Self hosting means your team owns patching, access controls, and the answers on every client security questionnaire. A managed deployment ships with role based access, encryption at rest and in transit, audit trails, and a SOC2 friendly architecture already in place, and the provider maintains them. If a client has ever asked where your data lives, having a documented, managed answer is worth a great deal.

Which one fits your company

Self host if you have a platform or machine learning team, you are operating at a scale where per token savings outweigh salaries, or you have a hard requirement that only a fully owned stack can meet. Choose managed private AI if you are a company of 10 to 200 people, you handle other people's sensitive information, and you want a private assistant running in weeks rather than a project that runs for quarters.

The middle path most companies pick

There is a reason managed private AI is the common choice in this size range. It is the only option that gives you the privacy of a self hosted system with the speed and simplicity of a subscription. HummingAgent AI runs exactly this model through its managed private AI deployment services: a dedicated Azure environment for your company, your documents indexed inside it, access to 30 plus models through one branded interface, and everything hosted, updated, and supported for you. The practical first step is a 30 minute demo where you see a live private environment, map it to your data sources, and leave with a fixed quote.