
The 100-Billion-Vector Database: Lessons for Enterprise AI Search
Explore IBM’s 100-billion-vector database research and the practical architecture, relevance, permission and cost lessons for enterprise AI search.
Practical guidance for buyers, operators and product teams. 11 articles in this topic.

Explore IBM’s 100-billion-vector database research and the practical architecture, relevance, permission and cost lessons for enterprise AI search.

Learn how quantum-centric supercomputing combines quantum and classical resources, where it may help and what businesses should realistically do now.

Design event streams, fresh retrieval, controlled tool APIs and transaction checks so AI agents act on current business state rather than stale context.

Reduce cloud and SaaS lock-in through portable data, clear interfaces, account ownership, exit testing and deliberate use of managed services.

Plan edge AI for sensors, equipment and field operations with device identity, offline behaviour, model updates and cloud integration.

Control AI cost using unit economics, allocation, budgets, model routing, caching and outcome-based value measures.

Plan legacy data migration with ownership, profiling, cleansing, mapping, trial runs, reconciliation, cut-over, rollback and archive access.

Extend internal platforms for models, GPUs, agents, evaluation, policy, observability and cost without rebuilding every delivery control.

Understand the business controls behind CI/CD: versioned change, automated tests, approvals, database safety, observability, rollback and ownership.

Assess cloud migration readiness across business goals, applications, data, security, connectivity, cost, skills, recovery and migration sequencing.

Identify structural database problems—weak keys, duplicate meanings, missing history and unclear ownership—before they undermine reporting and automation.
Tell us what you are building, replacing or connecting.