Vector databases (DBs), once specialized research instruments, have become widely used infrastructure in just a few years. They power today’s semantic search, recommendation engines, anti-fraud measures, and AI generation applications across industries. There is an avalanche of options: PostgreSQL with pgvector, MySQL HeatWave, DuckDB VSS, SQLite VSS, Pinecone, Weaviate, Milvus and several others.
The wealth of options sounds like a boon for businesses. But just underneath, a growing problem emerges: stack instability. New vector databases appear every quarter, with disparate APIs, indexing schemes, and performance tradeoffs. Today’s ideal choice may seem outdated or limiting tomorrow.
For enterprise AI teams, volatility translates to lock-in risks and migration hell. Most projects start with lightweight engines like DuckDB or SQLite for prototyping and then move to Postgres, MySQL, or a cloud-native service in production. Each change involves rewriting queries, reshaping pipelines, and slowing down deployments.
This reengineering merry-go-round undermines the speed and agility that AI adoption is supposed to bring.
Why portability is important now
Companies have to do a complicated balancing act:
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Experiment quickly with minimal overhead, hoping to try and get value early;
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Scale safely on stable, production-quality infrastructure without months of refactoring;
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Be agile 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 technologies, and can’t get prototypes into production at a good pace. In effect, the database is a bottleneck rather than an accelerator.
Portability, or the ability to move the underlying infrastructure without recoding the application, is increasingly a strategic requirement for companies deploying AI at scale.
Abstraction as infrastructure
The solution is not to choose the "perfect" vector database (there isn’t one), but to change the way companies think about the problem.
In software engineering, the adapter pattern provides a stable interface while hiding underlying complexity. Historically, we have seen how this principle reshaped entire industries:
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ODBC/JDBC gave businesses a unique way to query relational databases, reducing the risk of being tied to Oracle, MySQL, or SQL Server;
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Apache Arrow standardized columnar data formats, so that data systems could work well together;
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ONNX created a vendor-agnostic format for machine learning (ML) models, bringing together TensorFlow, PyTorch, etc.;
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Kubernetes abstracted away the details of the infrastructure, so that workloads could run the same everywhere in the clouds;
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any-llm (Mozilla AI) now allows you to have an API in 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 inflection point.
The adapter approach to vectors.
Instead of having application code directly tied to some specific vector backend, companies can compile against an abstraction layer that normalizes operations like inserts, queries, and filtering.
This doesn’t necessarily eliminate the need to choose a backend; makes that choice less rigid. Development teams can start with DuckDB or SQLite in the lab, then scale to Postgres or MySQL for production, and ultimately adopt a special-purpose cloud vector database without having to redesign the application.
Open source efforts like Vectorwrap are early examples of this approach, featuring a single Python API for Postgres, MySQL, DuckDB, and SQLite. They demonstrate the power of abstraction to accelerate prototyping, reduce crash risk, and support hybrid architectures that employ numerous backends.
Why companies should care
For data infrastructure leaders and AI decision makers, abstraction offers three benefits:
Speed from prototype to production
Teams can prototype in lightweight on-premises environments and scale without costly rewrites.
Reduced supplier risk
Organizations can adopt new backends as they emerge without lengthy migration projects by decoupling application code from specific databases.
Hybrid flexibility
Enterprises can combine transactional, analytical, and specialized vector databases into a single architecture, all behind an aggregated interface.
The result is data layer agility, and that is increasingly the difference between fast and slow companies.
A broader movement in open source
What’s happening in vector space is an example of a larger trend: open source abstractions as critical infrastructure.
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In data formats: Apache Arrow
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On ML models: ONNX
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In orchestration: Kubernetes
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In AI API: Any-LLM and other similar frameworks
These projects succeed not by adding new capabilities, but by eliminating friction. They allow companies to move faster, hedge bets and evolve along with the ecosystem.
Vector DB adapters continue this legacy, transforming a fragmented, high-speed space into an infrastructure that businesses can truly rely on.
The future of vector database portability
The vector database landscape won’t converge anytime soon. Instead, the number of options will grow and each provider will adapt to different use cases, scale, latency, hybrid search, compliance or cloud platform integration.
Abstraction becomes strategy in this case. Companies that adopt portable approaches will be able to:
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Boldly prototyping
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Flexible implementation
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Rapidly scaling to new technology
We may eventually see a "JDBC for vectors," a universal standard that encodes queries and operations across all backends. Until then, open source abstractions are laying the groundwork.
Conclusion
Companies adopting AI cannot afford to be slowed down by database crashes. As the vector ecosystem evolves, the winners will be those that treat abstraction as infrastructure, building on portable interfaces rather than being tied to a single backend.
The lesson from decades of software engineering is simple: standards and abstractions lead to adoption. For vector databases, that revolution has already begun.
Mihir Ahuja is an AI/ML engineer and open source contributor based in San Francisco.
