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Use a cloud AI service or run your own?

A cloud service can be easier to start with; running AI yourself requires equipment and maintenance. Compare the options using your data rules, number of users and budget. Different tasks can use different approaches.

Start with the workload and data, then choose private, cloud or hybrid delivery

A cloud API connects your systems to an external AI provider. Private deployment runs the model on your equipment or in an environment you choose. Both need clear data access rules and a cost comparison.

Four questions first

Can data leave the company? How quickly must you launch? How many people will use it at once, and how long can they wait? Who will operate the service?

What each option involves

Private deployment offers clearer data boundaries, customisation and cost control. Cloud APIs launch faster with lower initial effort. Hybrid routing can use each path for the tasks it handles best. Results depend on model, load, interfaces and compliance requirements.

Workloads that favour private delivery

Sensitive contracts, research, internal policies, knowledge-base Q&A and fixed permission boundaries are good candidates for private or private-cloud deployment.

Workloads that may stay on cloud APIs

Public information, low-sensitivity experiments and rapid prototypes may be simpler on a cloud API.

Try your actual work before deciding

Remove sensitive details from real questions, then check answer quality, document search, waiting times with several users, and cost.

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