j.caldwellBy Jeff Caldwell, AI Feasibility Engineer
A client asked me a deceptively simple question: “Could SQLite replace FAISS for our early-stage vector search?”
Two days later, I had built a complete vector database implementation, run comprehensive benchmarks, and delivered the answer: Yes, up to ~200k vectors. Switch to FAISS for sub-100ms performance at million-vector scale.
This is exactly the kind of rapid AI validation that can save teams months of development time and thousands in infrastructure costs. Let me show you how I approached this problem—and share the surprisingly capable SQLite vector database that emerged from it.
The Challenge: Vector Search Without the Complexity
Modern AI applications increasingly rely on vector similarity search for everything from RAG (Retrieval-Augmented Generation) to recommendation systems. The default solution is often reaching for heavyweight tools like Pinecone, Weaviate, or FAISS—powerful systems that come with operational overhead, learning curves, and infrastructure requirements.
But what if you’re just getting started? What if you need to validate an AI concept quickly without spinning up vector database infrastructure?
This is where my SQLite-based approach shines.
The Solution: Radical Simplicity
I built a complete vector database in a single Python file with just one dependency: NumPy. No setup, no configuration files, no Docker containers. Just SQLite doing what it does best—being reliable, fast enough, and universally available.
Key Design Principles
• Zero infrastructure — Uses SQLite’s built-in capabilities with performance-optimized PRAGMAs
• Single-file — The entire implementation fits in one readable Python script
• Batteries included — CRUD operations, batch inserts, metadata filtering, context windows, and database statistics
• Targeted optimization — Custom cosine similarity function with NumPy acceleration
Performance Results: When SQLite Surprises
Here’s what the benchmarks revealed on desktop hardware (Intel i7-14700KF + 32GB RAM, CPU-only run):
| Vectors | Insert (ms) | SQL Query (ms) | Fast NumPy (ms) |
|---|---|---|---|
| 1,000 | 100 | 27 | 8 |
| 10,000 | 1,400 | 2,940 | 875 |
| 100,000 | 16,300 | 29,456 | 9,447 |
Insert is one-time ingest; 16.3s for 100k rows = 6k rows/second.
The “Fast NumPy” method uses a thin Python loop instead of SQL’s ORDER BY, delivering 3-4x performance improvements at scale.
When to Use SQLite vs When to Migrate
SQLite shines for:
- ✅ Early-stage prototypes and MVPs
- ✅ Applications with < 200k vectors
- ✅ Batch processing workloads
- ✅ Local development and testing
- ✅ Embedded applications
Time to migrate when you need:
- ⚠️ Sub-100ms query latency at scale
- ⚠️ Million+ vector collections
- ⚠️ Distributed search capabilities
My 72-Hour Feasibility Playbook
This SQLite vector database isn’t just a technical artifact—it’s a demonstration of how rapid AI validation should work. In 72 hours, I took a client from uncertainty to clarity:
- Prototype: Built a working implementation
- Measure: Ran comprehensive benchmarks
- Decide: Delivered clear go/pivot/drop recommendations
This approach has saved clients months of development time and prevented costly architectural mistakes.
Quick Start (60 seconds)
The complete implementation is available on GitHub. You can run a demo in under a minute:
git clone https://github.com/fox4snce/storyvectordb \
&& cd storyvectordb \
&& pip install numpy \
&& python benchmarks/benchmark_sqlite.py
Runs the 10-minute benchmark and prints the same numbers you see above.
The codebase includes:
- Complete vector database implementation
- Benchmark suite with visualization
- Example usage patterns
- Performance optimization techniques
When Simple Wins
Not every AI project needs enterprise-grade vector infrastructure from day one. Sometimes the best solution is the one that gets you from idea to validated prototype in the shortest time possible.
SQLite VectorDB proves that with thoughtful design and focused optimization, you can build surprisingly capable AI infrastructure using tools you already have.
Behind the Build
This project was inspired by a Microsoft blog post that walked through building a vector database with SQL Server. The original implementation had some gaps, so I worked with GPT to fill them in and then adapted the approach for SQLite. After losing my corporate role, I realized how many teams struggle with the same “Could we just…?” questions around AI feasibility.
Need rapid AI feasibility testing for your next project?
I help teams turn “Could we just…?” questions into actionable data in 72 hours. My fixed-price feasibility sprints include working prototypes, benchmark data, and clear go/pivot/drop recommendations.
Fixed price: US $2,500 | Typical turnaround: 3 business days
Contact: mailto:j.caldwell@simplychaos.org | Response time: < 24 hours (Pacific)
The SQLite VectorDB project is open source under MIT license. Use it, modify it, learn from it—and let me know what you build.
