Abstract or die: Why AI enterprises can't afford rigid vector stacks

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

The wealth of choices seems like a boon for businesses. But just beneath that, a growing problem looms: stack instability. New vector databases appear every quarter, with varying APIs, indexing schemes, and performance trade-offs. The perfect choice today may seem outdated or limited tomorrow.

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

This re-engineering spiral undermines the speed and agility that adopting AI is supposed to bring.

Why portability matters now

Companies have a difficult balance:

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

• Scaling securely through stable, production-quality infrastructure without the need for months of refactoring;

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

Without portability, organizations stagnate. They have technical debt from repetitive code paths, are reluctant to adopt new technology and can’t move prototypes to production as quickly. In fact, the database is a bottleneck, not an accelerator.

Portability, or the ability to move the underlying infrastructure without recoding the application, is more of a strategic requirement than ever for organizations rolling out AI at scale.

Abstraction as infrastructure

The solution is not a choice "excellent" A vector database (there isn’t one), but to change the way organizations think about the problem.

In software engineering, the adapter pattern provides a stable interface while hiding the underlying complexity. Historically, we’ve seen how this principle has reshaped entire industries:

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

• Apache Arrow is a unified columnar data format, so that data systems can work well together;

• ONNX created a vendor-agnostic format for machine learning (ML) models, bringing TensorFlow, PyTorch, etc. together;

• Kubernetes has abstracted away the details of infrastructure, so workloads can run the same way everywhere on the cloud;

• Any-llm (Mozilla AI) now allows for a single API across many large language model (LLM) vendors, so playing with AI is safer.

All these abstractions led to their adoption by reducing switching costs. They turn broken ecosystems into powerful enterprise-grade infrastructure.

Vector databases are also at the same turning point.

Transformer approach to vectors

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

This does not necessarily eliminate the need to choose a backend; It makes this choice less stringent. Development teams can start with DuckDB or SQLite in the lab, then scale up to Postgres or MySQL for production and eventually adopt a special-purpose cloud vector database without having to re-engineer the application.

Open source efforts like Vectorwrap are early examples of this approach, offering a single Python API to Postgres, MySQL, DuckDB, and SQLite. It demonstrates the power of abstraction to speed up the prototyping process, reduce the risk of lock-in, and support hybrid architectures that use multiple backends.

Why should companies care?

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

Speed ​​from prototype to production

Teams can create prototypes in lightweight, local environments and scale without expensive rewrites.

Reduce seller risk

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

Hybrid flexibility

Businesses can mix transactional, analytics, and specialized vector databases within a single architecture, all behind a consolidated interface.

The result is data layer flexibility, which is the difference between fast and slow companies.

A broader movement in open source

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 format: Kubernetes

• In AI APIs: Any-LLM and other frameworks

These projects succeed not by adding new capabilities, but by removing friction. It enables companies to move more quickly, hedge, and evolve alongside the ecosystem.

Vector DB switches continue this legacy, turning a fragmented, high-speed space into infrastructure that organizations can truly rely on.

The future of vector-oriented database portability

The landscape of vector databases will not be converging any time soon. Instead, the number of options will grow, and each vendor will adjust for different use cases, scale, response time, hybrid search, compliance, or cloud platform integration.

Abstraction becomes a strategy in this case. Businesses that embrace mobile approaches will be able to:

• Prototype boldly

• Flexible publishing

• Rapidly expanding into new technology

It is possible that we will eventually see a "JDBC for vectors," A global standard that regulates queries and operations across backends. Until then, open source abstractions lay the foundation.

conclusion

Companies adopting AI cannot afford to slow down due to database lock-up. As the vector ecosystem evolves, the winners will be those who treat abstraction as infrastructure, building on portable interfaces rather than tying themselves to any single backend.

The decades-old lesson of software engineering is simple: standards and abstractions lead to 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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2025-10-18 09:00:00