Abstract or Die: Why AI Enterprises Can’t Afford Rigid Vector Stacks

Vector databases (DBs), once specialized research tools, have become a widely used infrastructure in just a few years. They power today’s semantic search, recommendation engines, anti-fraud measures, and next-generation AI applications across industries. There are multiple options: PostgreSQL with pgvector, MySQL HeatWave, DuckDB VSS, SQLite VSS, Pinecone, Weaviate, Milvus, and several others.

A wealth of choices sounds like a boon for companies. But just underneath looms a growing problem: stack instability. New vector DBs appear every quarter with different APIs, indexing schemes, and performance trade-offs. An ideal choice today may seem outdated or limiting tomorrow.

For AI business teams, volatility translates into lock-in risks and migration hell. Most projects start life with lightweight engines like DuckDB or SQLite for prototyping, then move to Postgres, MySQL, or a cloud service in production. Any switch involves rewriting queries, reshaping pipelines, and delaying deployments.

This renewed merry-go-round undermines the very speed and flexibility that AI adoption is supposed to bring.

Why portability matters now

Companies have a difficult balance:

• Experiment quickly with minimal cost, hoping to try and get early value;

• Scale safely on stable, production-quality infrastructure without months of refactoring;

• Be nimble in a world where new and better backends arrive almost every month.

Without portability, organizations stagnate. They have technical debt from recursive code paths, are hesitant to adopt new technology, and can’t move prototypes into production at pace. In practice, the database is more of a bottleneck than an accelerator.

Portability, or the ability to move the underlying infrastructure without recoding the application, is an increasingly strategic requirement for enterprises deploying AI at scale.

Abstraction as infrastructure

The solution is not to choose "perfect" vector database (there isn’t one), but to change the way businesses think about the problem.

In software engineering, the adapter model provides a robust interface while hiding the underlying complexity. Historically, we’ve seen this principle change entire industries:

• ODBC/JDBC gave enterprises a single way to query relational databases, reducing the risk of being tied to Oracle, MySQL or SQL Server;

• Apache Arrow standardizes columnar data formats so data systems can work well together;

• ONNX created a vendor-dependent format for machine learning (ML) models, bringing together TensorFlow, PyTorch, and more;

• Kubernetes abstracts away the infrastructure details so workloads can run uniformly across clouds;

• any-llm (Mozilla AI) now makes it possible to have one API for many Large Language Model (LLM) providers, so playing with AI is safer.

All of these abstractions led to adoption by reducing switching costs. They turned broken ecosystems into solid enterprise-level infrastructure.

Vector databases are also at the same turning point.

The adapter approach to vectors

Instead of having application code directly bound to some specific vector backend, companies can compile against an abstraction layer that normalizes operations such as inserts, queries, and filtering.

This does not necessarily eliminate the need for backend selection; makes this choice less rigid. Development teams can start with DuckDB or SQLite in the lab, then expand to Postgres or MySQL for production, and eventually adopt a purpose-built cloud vector database without having to re-architect the application.

Open source efforts like Vectorwrap are early examples of this approach, presenting a single Python API for Postgres, MySQL, DuckDB, and SQLite. They demonstrate the power of abstraction to accelerate prototyping, reduce the risk of deadlock, and support hybrid architectures using multiple backends.

Why businesses should care

For data infrastructure leaders and AI decision makers, abstraction offers three benefits:

Speed ​​from prototype to production

Teams are able to prototype in lightweight on-premises environments and scale without expensive rewrites.

Reduced seller risk

Organizations can adopt new backends as they emerge without lengthy migration projects by decoupling application code from specific databases.

Hybrid flexibility

Companies can mix transactional, analytical and specialized vector databases under one architecture, all behind an aggregated interface.

The result is flexibility at the data layer, and that’s a growing difference between fast and slow companies.

Broader open source movement

What’s happening in the vector space is one example of a larger trend: open source abstractions as critical infrastructure.

• In data formats: Apache Arrow

• In ML models: ONNX

• In orchestration: Kubernetes

• In AI API: Any-LLM and other similar frameworks

These projects succeed not by adding new capabilities, but by removing friction. They allow businesses to move faster, hedge and grow with the ecosystem.

Vector DB adapters continue this legacy, transforming a high-speed, fragmented space into an infrastructure that enterprises can truly rely on.

The future of vector database portability

The vector DB landscape is not converging anytime soon. Instead, the number of options will grow, and each vendor will adjust to different use cases, scale, latency, hybrid demand, compliance, or cloud platform integration.

In this case, abstraction becomes strategy. Companies that adopt portable approaches will be able to:

• Prototype boldly

• Deploy in a flexible manner

• Rapid scaling to new technologies

It is possible that we will eventually see a "JDBC for vectors," a universal standard that codifies back-end queries and operations. Until then, open source abstractions lay the groundwork.

Conclusion

Enterprises embracing AI cannot afford to be held back by database lock-in. As the vector ecosystem evolves, the winners will be those that treat abstraction as infrastructure, building against portable interfaces rather than binding to any single backend.

The decades-long lesson of software engineering is simple: Standards and abstractions drive adoption. For vector databases, this revolution has already begun.

Mihir Ahuja is an AI/ML engineer and open source contributor based in San Francisco.

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