Vector databases (DBs), which were once specialist 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 general AI applications across industries. There are plenty of options: PostgreSQL with pgvector, MySQL Heatwave, DuckDB VSS, SQLite VSS, Pinecone, Vivinet, Milvus and many others.
The richness of options seems like a boon for companies. But just below, a growing problem looms: stack instability. New vector DBs appear every quarter with different APIs, indexing schemes, and performance trade-offs. Today’s ideal option may seem dated or limited tomorrow.
For business AI teams, instability translates into lock-in risks 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, reshaping pipelines, and slowing down deployments.
This re-engineering carousel undermines the speed and agility that will come from AI adoption.
Why does portability matter now?
Companies have a difficult balancing act:
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Experiment quickly, with minimal overhead, in the hopes of trying and achieving value quickly;
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Securely scale 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 become stagnant. They have technical debt from recursive code paths, are hesitant to adopt new technology and can’t move prototypes to production at speed. In fact, the database is a bottleneck rather than an accelerator.
Portability, or the ability to move the underlying infrastructure without re-encoding the application, is a strategic requirement for enterprises looking to introduce AI at scale.
Abstraction as infrastructure
The solution is not to choose "Excellent" Vector database (there isn’t one), but to change the way enterprises think about the problem.
In software engineering, the adapter pattern provides a stable interface while hiding the underlying complexity. Historically, we have seen how this principle has reshaped entire industries:
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ODBC/JDBC gave enterprises a single way to query relational databases, reducing the risk of being tied to Oracle, MySQL or SQL Server;
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Apache Arrow standardized column data formats, so data systems could play nice 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 abstracts the infrastructure details, so that workloads can run uniformly across clouds;
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Ani-LLM (Mozilla AI) now makes it possible for many large language model (LLM) vendors to have one API, so it is safer to play with AI.
All these findings led to its adoption by reducing switching costs. They transformed broken ecosystems into solid, enterprise-level infrastructure.
Vector databases are also at the same critical point.
Adapter approach for vectors
Instead of tying application code directly to some specific vector backend, companies can compile against an abstraction layer that generalizes operations like inserts, queries, and filtering.
This does not necessarily eliminate the need to choose a backend; This makes that choice less drastic. 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 DB without re-architecting the application.
Open source efforts like VectorWrap are early examples of this approach, offering a single Python API for Postgres, MySQL, DuckDB, and SQLite. They demonstrate the power of abstraction to accelerate prototyping, reduce lock-in risk, and support hybrid architectures that employ multiple backends.
Why should businesses care
For data infrastructure leaders and AI decision makers, abstraction offers three benefits:
Speed from prototype to production
Teams are able to create lightweight local environments and prototypes at scale without expensive rewrites.
Seller’s risk reduced
Organizations can adopt new backends as they emerge without lengthy migration projects by separating app code from specific databases.
hybrid flexibility
Companies can blend transactional, analytical, and special vector DBs under one architecture, behind a single composite interface.
The result is the agility of the data layer, and it is the difference between fast and slow companies.
A broader movement in open source
What’s happening in the vector space is an example of a larger trend: open-source abstraction as critical infrastructure.
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Data formats: Apache Arrow
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In ML Model: ONNX
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In Orchestration: Kubernetes
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In AI API: Any-LLM and other such frameworks
These projects succeed not by adding new capacity, but by removing friction. They enable enterprises to move more quickly, make bets, and evolve with the ecosystem.
Vector DB adapters continue this legacy, turning a high-speed, fragmented space into an infrastructure that enterprises can truly depend on.
The future of Vector DB portability
The vector DB scenario won’t come together any time soon. Instead, the number of options will grow, and each vendor will tune for different use cases, scale, latency, hybrid search, compliance, or cloud platform integration.
In this case abstraction becomes the strategy. Companies adopting a portable approach will be able to:
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prototyping boldly
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flexible deployment
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moving rapidly towards new technology
It’s possible we’ll eventually see one "JDBC for vectors," A universal standard that codifies queries and operations in the backend. Until then, open-source abstractions are laying the groundwork.
conclusion
Enterprises adopting AI cannot afford to be slowed down 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 tying themselves to any one backend.
The decades-old lesson of software engineering is simple: Standards and abstraction lead to adoption. For VectorDB, that revolution has already begun.
Mihir Ahuja is an AI/ML engineer and open-source contributor based in San Francisco.
