Vector databases and semantic search help enterprises find answers by meaning, not just keywords. In 2026, they have become mission-critical infrastructure behind agentic AI and Retrieval-Augmented Generation (RAG), the technique where an AI model pulls in your trusted data before it answers. Semantic search unlocks the roughly 80% of enterprise data that is unstructured and cuts 'zero-result' queries by 30-50%. Surge helps you adopt, implement, integrate, and govern these technologies so your AI gives accurate, sourced answers.
What vector databases and semantic search are, and why they matter
A vector database stores information as numerical 'embeddings' that capture meaning, so a search can match ideas even when the exact words differ. Semantic search uses this to understand intent, making it the retrieval engine behind RAG. The market reflects this shift: the vector database market is projected to reach $17.91 billion by 2034 (24% CAGR), and the semantic search market is projected to reach $18.03 billion by 2031. Over 65% of large North American enterprises have already integrated semantic capabilities.
- Semantic search reduces 'zero-result' queries by 30-50% and unlocks the ~80% of enterprise data that is unstructured.
- Hybrid search, combining dense vectors, sparse vectors (BM25), and rerankers with Reciprocal Rank Fusion, is now the standard for accurate production RAG.
- Leading vector databases have moved to serverless architectures that separate reads, writes, and storage to handle bursty agentic AI workloads at lower cost.
- General-purpose databases with vector extensions, like PostgreSQL (pgvector), are a common default for new RAG projects, avoiding extra infrastructure.
Choosing the right architecture and controlling cost
Not every team needs a separate, standalone vector database. Production evidence from over 100 enterprise deployments points to pgvector as a strong starting point because it avoids managing an extra component. At scale, standalone managed databases can become expensive, since read-unit pricing compounds quickly with complex metadata filtering. This is driving migrations to self-hosted, integrated, and more predictable-priced options. The right choice depends on your workload, accuracy needs, and budget.
- Match the platform to your scale: integrated extensions for new projects, dedicated systems where volume demands it.
- Design hybrid search for both meaning and exact, domain-specific keyword matches.
- Plan for cost early, since read-heavy and metadata-filtered queries drive spend.
How Surge helps you implement vector databases and semantic search
Surge Software Solutions is a Premier Adobe Experience Cloud Partner that helps enterprises adopt, implement, integrate, and govern digital-experience and AI technologies. We position ourselves as your expert delivery partner: we help you select an architecture, stand up semantic search and RAG pipelines, and connect them to your data and experience stack, including Adobe Experience Platform, Customer Journey Analytics, and AJO where relevant. We support three flexible engagement models so you can scale the way that suits you.
- Implementation Partner: we deliver and integrate your vector search and RAG pipeline end to end.
- Resourcing Partner: we supply skilled specialists to extend your team.
- GCC (Global Capability Center): we help you build a dedicated long-term capability.
- Governance support so your AI retrieval stays accurate, controlled, and aligned to your data.
Reach Surge for help implementing this
Ready to move semantic search and RAG from pilot to production? Contact Surge to plan, implement, and govern the right vector search foundation for your enterprise.
Talk to a Surge Expert