Vector Databases Turn AI Solutions into Business Value
Selecting the Vector Database Solution That’s Right for You
By: Dr. Sai Mali Ananthanarayanan, Chief Data Scientist; Tom Thayyil Thomas, AI Engineering Intern; Naveen Suresh, AI/ML Engineer; Ojas Sawant, Staff Engineer
The future of business and securing competitive advantages requires artificial intelligence (AI) and leveraging it effectively to drive innovation, enhance customer experiences, and optimize operations. The real challenge for most companies resides in transforming AI from a buzzword into tangible business value. This is where vector databases (Vector DBs) come into play. By enabling advanced AI models to store, search, and process vast amounts of unstructured data—such as text, images, and videos—Vector DBs unlock the potential for businesses to harness deep insights and make smarter decisions in real time.
How Do Vector Databases Work?
Vector DBs transform diverse data points into numerical sequences that serve as unique “digital fingerprints.” For instance, items like “Apple,” “Mango,” and “Microsoft” are mapped close to each other in a multi-dimensional space because they share overlapping contexts—”Apple” and “Mango” are both fruits, while “Apple” and “Microsoft” are technology brands. However, “Dog” is far from these because its context, associated with animals, has little overlap with fruits or technology. From Netflix’s content recommendations to Amazon’s product suggestions, this clustering system underpins many of today’s leading technologies, enabling businesses to process vast amounts of data with unprecedented efficiency and insight.
The “Right” Vector Database Depends on Your Use Case
Understanding and evaluating Vector DBs is essential because these systems are foundational to AI-driven innovation, yet the landscape of vector databases is evolving rapidly. Driven by the surging demand for high-dimensional data processing, the Vector DB field has experienced significant growth in recent years. As of today, there are over a dozen notable Vector DBs, including Milvus, Pinecone, Qdrant, PGVector, and ChromaDB, among others. Historically dominated by a few key players, new entrants with unique capabilities have reshaped the competitive landscape. Such a diverse ecosystem makes evaluating these technologies critical to understanding which aligns best with an organization’s strategic objectives.
Selecting and implementing the right Vector DB to transform your business data is a complex and critical task in today’s AI landscape. Teragonia has evaluated leading Vector DBs with key business metrics. Our experts—AI engineers, technologists, investors, seasoned executives, and M&A advisors—help organizations align, build, implement, and ensure that their AI solutions use the most appropriate vector database to achieve their strategic business goals.
Key Metrics for Vector Database Evaluation
Teragonia’s experts evaluated Vector DBs using the metrics of Cost and LIMITS (Latency, Indexing Speed, Memory Usage, Infrastructure Throughput, Total Storage, Scalability and Ease of Use):
- Cost: Refers to the total cost of ownership, including infrastructure expenses and licensing fees. Balancing costs with performance ensures sustainable investment for long-term AI success.
- Latency: The time it takes to retrieve results. Lower latency translates directly into faster, more responsive applications—a critical factor in scenarios like real-time recommendations or fraud detection in transactions.
- Indexing Speed: The time required to index or update data. Faster indexing means quicker integration of new data into the system, a key advantage for industries like financial trading or dynamic content delivery.
- Memory Usage: Measures efficiency in utilizing RAM during operations. Lower memory usage reduces infrastructure costs while ensuring consistent performance.
- Infrastructure Throughput: The number of queries handled per second. High throughput is essential for scaling operations efficiently, such as processing millions of queries during peak sales events in e-commerce.
- Storage Footprint: The amount of space needed for storing vector data. Optimizing storage not only saves costs but also allows for handling larger datasets without performance degradation.
- Scalability and Ease of Use: Refers to developer-friendliness and the quality of documentation. A user-friendly solution accelerates adoption, lowers the learning curve, and scales with your business.
Evaluating Vector Databases with Classic Novels
To provide actionable insights, we evaluated five popular Vector DBs—Milvus, Qdrant, PGVector, Pinecone, and ChromaDB—across three datasets using the classic novels “Alice in Wonderland,” “Pride and Prejudice,” and “Frankenstein.” These novels were chosen because they represent unstructured data, which is a common challenge for Vector DBs to process and analyze effectively.
Each database was assessed based on the Cost and LIMITS metrics outlined above with performance categorized into:
- Best: Exemplary performance and industry-leading
- Intermediate: Solid performance meeting most expectations
- Emerging: Trailing and significant room for improvement
To evaluate scalability, we used datasets of varying sizes:
- “Alice in Wonderland”: ~30,000 words (small dataset)
- “Pride and Prejudice”: ~120,000 words (medium dataset)
- “Frankenstein”: ~75,000 words (medium-to-large dataset)
Each dataset was divided into smaller sections of 500 words to see how well the databases could handle storing and searching the information:
- Storing Data: These sections were converted into a special format called vector embeddings and saved in the database.
- Searching Data: The databases were tested to find the most relevant matches for specific queries.
- Speed Testing: Multiple searches were run at the same time to check how quickly the database could provide results and handle a large number of requests.
Evaluation Results
Performance Highlights
- PGVector demonstrated best scalability with consistently low latency and high throughput across all datasets.
- Milvus ranked high due to efficient memory usage and throughput.
- Qdrant showed moderate scalability with steady performance but higher memory usage.
- Pinecone had moderate scalability but was hindered by higher costs.
- ChromaDB exhibited the least scalability with significant performance drops on larger datasets.
Recommendations for Executives Investing in Vector Databases
When choosing the right Vector DB, there is no one-size-fits-all solution. The best option depends on your specific needs, such as performance requirements, budget, and application goals. To help you make an informed decision, we summarize the top vector databases according to your primary goal.
- High-Performance: If scalability and low latency are crucial, Milvus and PGVector stand out as top contenders.
- Affordable and Lightweight: ChromaDB offers affordability but at the expense of performance, making it suitable for small dataset applications, such as extracting key financial metrics from 250 financial documents.
- Best Support and Ease of Use: For minimal operational overhead, Pinecone provides robust support but at a premium cost.
- Flexibility and Real-Time Use: Qdrant’s deployment options make it suitable for hybrid environments and real-time applications.
Getting Started with Vector Databases
Vector DBs bridge complex AI algorithms and actionable intelligence, making them indispensable tools for business growth and competitive advantages. Organizations that prioritize vector databases today position themselves to lead in innovation and decision-making, transforming data into a strategic asset that drives measurable outcomes.
Teragonia accelerates business scaling with advanced data solutions. The company’s Decision Intelligence Solution delivers real-time, system level insights to enhance EBITDA and facilitates an effective and efficient transition from conventional business methods to data-driven strategies. To speak with one of our experts about Vector DBs or our suite of AI-based, value creation, BizOps solutions, contact us for more information.
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