Vector databases (DBs) were once specialized research instruments, but have become widely used infrastructure in just a few years. These power today’s semantic search, recommendation engines, anti-fraud, and Gen AI applications across industries. Options include PostgreSQL with pgvector, MySQL HeatWave, DuckDB VSS, SQLite VSS, Pinecone, Weaviate, Milvus, and several others.
The abundance of choices seems like a boon for businesses. But just beneath that lies the serious problem of stack instability. New Vector DBs appear every quarter, with different APIs, indexing schemes, and performance tradeoffs. Today’s ideal choice may seem outdated or restrictive tomorrow.
For business AI teams, volatility leads to lock-in risk and migration hell. Most projects start with a lightweight engine like DuckDB or SQLite for prototyping, and then move to Postgres, MySQL, or cloud-native services for production. Each switch involves rewriting queries, reshaping pipelines, and slowing down deployments.
This merry-go-round of reengineering undermines the very speed and agility that AI adoption is supposed to bring.
Why is portability important now?
Companies have to strike a difficult balance.
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We want to experiment quickly with minimal overhead and expect to see value early.
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Scale securely on stable, production-quality infrastructure without requiring months of refactoring.
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Be agile in a world where new and better backends emerge almost every month.
Without portability, organizations stagnate. They have technical debt due to recursive code paths, are hesitant to adopt new technologies, and are unable to move prototypes into production quickly. In fact, the database is the bottleneck, not the accelerator.
For companies deploying AI at scale, portability, or the ability to move the underlying infrastructure without re-encoding applications, is more than ever a strategic requirement.
Abstraction as infrastructure
The solution is not to choose "perfection" A vector database (which doesn’t exist), but to change the way companies think about problems.
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.
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ODBC/JDBC provides businesses with 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 standardizes columnar data formats, allowing data systems to work well together.
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ONNX has integrated TensorFlow, PyTorch, and more to create a vendor-neutral format for machine learning (ML) models.
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Kubernetes abstracts infrastructure details so workloads can run the same anywhere on the cloud.
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any-llm (Mozilla AI) makes using AI more secure by allowing one API to be used across many large language model (LLM) vendors.
All these abstractions reduce switching costs and enable deployment. They turned a broken ecosystem into a solid enterprise-grade infrastructure.
Vector databases are at the same tipping point.
Adapter approach to vectors
Instead of directly binding application code to a specific vector backend, enterprises can compile it against an abstraction layer that normalizes operations such as inserts, queries, and filtering.
This doesn’t necessarily eliminate the need to choose a backend. This makes the selection less rigid. Development teams can start with DuckDB or SQLite in the lab, scale up to Postgres or MySQL in production, and eventually adopt special-purpose cloud Vector DB without rebuilding their applications.
Open source efforts like Vectorwrap are early examples of this approach, providing a single Python API for Postgres, MySQL, DuckDB, and SQLite. They demonstrate the power of abstraction to accelerate prototyping, reduce the risk of lock-in, and support hybrid architectures with many backends.
Why businesses need to pay attention
Abstraction has three benefits for data infrastructure leaders and AI decision makers.
Speed from prototype to production
Teams can prototype in a lightweight local environment and scale without costly rewrites.
Reduce vendor risk
By decoupling app code from specific databases, organizations can adopt new backends as they emerge without the need for lengthy migration projects.
Hybrid flexibility
Enterprises can mix transactional DBs, analytical DBs, and specialized vector DBs under one architecture, all behind a centralized interface.
The result is data layer agility that will increasingly differentiate the fast from the slow.
A broader movement in open source
What’s happening in vector space is an example of a larger trend of open source abstraction as critical infrastructure.
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Data format: Apache Arrow
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For ML models: ONNX
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Orchestration: Kubernetes
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AI API: Any-LLM and other similar frameworks
These projects succeed not by adding new features, but by removing friction. These enable businesses to move faster, avoid risk, and evolve with the ecosystem.
Vector DB adapters continue this tradition, transforming high-speed, fragmented spaces into infrastructure that enterprises can truly trust.
The future of Vector DB portability
The situation with Vector DB will not end soon. Instead, the number of options will increase, with each vendor tailored to different use cases, scale, latency, hybrid search, compliance, or cloud platform integration.
In this case, abstraction is the strategy. Companies that take a portable approach can:
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bold prototyping
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Flexible deployment
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Rapid expansion to new technologies
eventually, "JDBC for vectors," A universal standard that codifies queries and operations across backends. Until then, open source abstractions lay the foundation.
conclusion
Companies implementing AI cannot afford to be slowed down by database lock-in. As the vector ecosystem evolves, the winners will be the companies that treat abstractions as infrastructure and build against portable interfaces rather than tying themselves to a single backend.
The lesson of software engineering over the decades is simple. Standards and abstraction lead to adoption. With Vector DB, that revolution has already begun.
Mihir Ahuja is an AI/ML engineer and open source contributor based in San Francisco.
