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 initiatives and general AI applications across industries. There are a flood of options: PGVector, SQL HatView, DuckDBVSS, Skylite VSS, Pincon, Vivit, Mulvis, and PostgreSQL along with many others.
The wealth of choice seems like a boon for companies. But at the very bottom, a growing problem boils down: stack instability. A new Vector DBS appears every quarter, with different APIs, indexing schemes and performance trade-offs. Today’s ideal choice may seem dated or limited tomorrow.
For business AI teams, volatility translates into lock-in risks and migration hell. Most projects start life with lightweight engines such as DuckDB or Sqllet for Squat, then move to production in Postgres, SQL or a cloud-native service. Each switch involves rewriting queries, reshaping pipelines, and slowing down deployments.
This round of re-engineering undermines the very speed and agility that AI has to bring to adoption.
Why Portability Matters Now
Companies have a tricky balancing act:
Experiment quickly with minimal overhead in hopes of trying and getting an initial price.
Scale securely on stable, production-quality infrastructure without months of refactoring.
Stay agile in a world where new and better backends arrive every month.
Without portability, organizations become stagnant. They have technical debt from iterative code paths, are reluctant to adopt new technology and cannot 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 another strategic requirement for enterprises to deploy AI at scale.
Abstract as infrastructure
The solution is not to choose "complete" Vector database (not one), but to change how businesses think about this problem.
In software engineering, the adapter pattern provides a stable interface while hiding the underlying complexity. Historically, we’ve seen how this principle changed entire industries:
ODBC/JDBC gave enterprises a single way to query relational databases, reducing the risk of connecting to Oracle, SQL or SQL Server.
Apache Arrow standardizes columnar data formats, so data systems can play well together.
ONNX created a vendor-agnostic format for machine learning (ML) models, bringing together TensorfFlow, Pytorch, etc.
Kubernetes abstracts infrastructure specifications, so workloads can run the same everywhere on clouds.
Any LLM (Mozilla AI) now makes it possible to have an API across many major language model (LLM) vendors, so it’s safer to play with AI.
All these abstractions are adopted by reducing the switching cost as a result. They turned a broken ecosystem into a solid, enterprise-level infrastructure.
Vector databases are also at the same tipping point.
Adapter approach to vectors
Instead of having application code bound directly to some specific vector backend, companies can compile against an abstraction layer that automates tasks like admissions, queries, and filtering.
This does not necessarily eliminate the need for backselection. This makes the choice less difficult. Development teams can start with DuckDB or SQLite in the lab, then scale to Postgres or SQL for production, and eventually adopt a special-purpose cloud VectorDB without re-evaluating the application.
Open source efforts like Vectorrope are early examples of this approach, offering a single Python API across Postgres, SQL, DuckDB, and SQLite. They demonstrate the power of abstraction to accelerate prototyping, reduce lock-in risk, and support hybrid architectures that utilize multiple backends.
Why Businesses Should Care
For leaders of data infrastructure and decision makers for AI, abstraction offers three benefits:
Speed from prototype to production
Teams can prototype in a lightweight native environment and at scale without expensive write-ups.
Low vendor risk
Organizations emerge without lengthy migration projects by decoupling app code from specific databases.
Hybrid flexibility
Companies can combine transactional, analytical and special vector DBS under one architecture, all behind a unified interface.
The result is the agility of the data layer, and it’s the biggest 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.
In data formats: Apache Arrow
In ML models: ONX
In Orchestration: Kubernetes
In AI APIs: Any LLM and other such frameworks
These projects succeed not by adding new capabilities, but by eliminating friction. They enable businesses to move more quickly, hedge bets and evolve alongside ecosystems.
Vector DB adapters continue that legacy, turning a high-speed, fragmented space into an infrastructure that businesses can truly depend on.
The Future of Vector DB Portability
The Vector DBS landscape isn’t going to change anytime soon. Instead, the number of options will increase, and each vendor will be tuning for different use cases, scale, latency, hybrid search, compliance or cloud platform integration.
Abstraction becomes strategy in this case. Companies adopting a portable approach will be able to:
Prototyping boldly
Deploy flexibly
Scaling new tech faster
It is possible that we will eventually see "JDBC for vector," A universal standard that codifies queries and operations in the backend. Until then, open source abstracts are laying the groundwork.
The result
Enterprises can’t be slowed down by database lock-in to adopt AI. As the Vector ecosystem evolves, the winners will be those who treat abstraction as infrastructure, building against portable interfaces rather than binding themselves to any backend.
The decades-long lesson of software engineering is simple: standards and abstractions lead to adoption. For VectorDBS, that revolution has already begun.
Mihir Ahuja is an AI/ML engineer and open source contributor based in San Francisco.