How to design a vector database like Pinecone?

Modern AI apps thrive on a vector database. Learn how it works:

Jul 23, 2025

An anime character (boy) in 4k who is coding and thinking about vectors on a day with bright sunset, and hyper realistic feeling with orange and teal filter.

The rapid mass adoption of AI has led to a massive amount of unstructured data exploding at an exponential rate. In simpler terms, when we use LLM interfaces like ChatGPT, they generate unstructured data rather than structured data.

Traditional databases like MySQL, Postgres, designed primarily for structured data and exact keyword matching, are proving inadequate for the surging demands of modern AI applications.

What is a vector database?

A vector database is a specialized database management system meticulously engineered to store, manage, and query high-dimensional numerical representations of data, known as "vector embeddings".

These embeddings are numerical arrays that contain the semantic meaning and inherent characteristics of various data types, including non-mathematical forms such as words, images, and audio. By transforming data into these multidimensional arrays, different ML models can effectively process and compare information based on its underlying meaning rather than superficial keywords.

Here is an example of what a vector embedding would look like:

"The cat sat on the mat"     → [0.2, -0.1, 0.8, 0.3, -0.5, ...]
"A dog ran in the park"      → [0.1, 0.4, -0.2, 0.7, 0.1, ...]
"The feline rested on the rug" → [0.3, -0.2, 0.7, 0.2, -0.4, ...]

Vector Space Model: The core concept underpinning this approach is often the "vector space model," which posits that data points can be represented as vectors in a multidimensional space, and the "distance" or "angle" between these vectors directly correlates with their relevance or similarity.

Why do we even need a vector database?

The need for a vector database stems directly from the limitation of a traditional database—while excellent for structured data and exact keyword-based lookups, it lags when confronted with the vast unstructured data that dominates today's AI-first world. Here are a few of the limitations—these are adoption factors for a vector database:

Unlocking Unstructured Data: Traditional DBs struggle to store and query unstructured data in a manner that preserves its semantic meaning or contextual relationships.
Efficient Similarity Search: AI applications demand the ability to find "closest" or "semantically similar" items, a task for which traditional databases are not optimized, and the cost of computing a `cosine similarity` is high here.
Foundation for AI/ML Applications: Vector DBs serve as a foundational layer for a wide array of AI-powered applications such as large language models (LLMs), Retrieval Augmented Generation (RAG) systems, and conversational agents requiring long-term memory.

2022 is long gone, my friend. There should be a new unit of time because things are changing at light speed. What should we call the new unit?

Types of Vector DBs

Vector DBs can be broadly categorized based on their deployment model and underlying storage strategy.

Deployment Models

Self-Hosted: This model offers the highest degree of control over the vector database software, allowing for granular optimization of performance, scaling parameters, and data security.
Cloud-Managed (SaaS): Managed services simplify setup and maintenance by offloading operational tasks such as automated patching, backups, and infrastructure provisioning to the cloud provider.
Serverless: Representing the highest level of abstraction, serverless vector databases virtually eliminate the need for infrastructure management.

Storage Models

In-Memory: This model prioritizes raw speed and throughput by storing the majority of data directly in Random Access Memory (RAM).
Disk-Based (or Hybrid): Although a purely "disk-based vector database" is a distinct category, many vector databases utilize disk storage for handling larger datasets.

Functional Requirements - What?

A functional requirement represents what the system must do— this is the core. You would have guessed the first one right.

1. CRUD Operations

Like any DB, a vector DB must support the fundamental Create, Read, Update, and Delete (CRUD) operations for its data, although the CRUD of a traditional DB and the vector DB are not the same because of its representation in higher dimensions and indexed data.

Create/Insert: This involves adding new vector embeddings, along with their associated metadata, into the database. The process often begins with the vectorization of raw data before insertion.
Read/Query: This refers to retrieving specific vectors or sets of vectors, typically based on similarity to a query vector or through filtering by associated metadata.
Update: Modifying existing vector embeddings or their metadata is a critical capability. Internally, updates are frequently handled as a "delete followed by an insert" operation.
Delete: Removing vector embeddings from the database. Some systems employ "soft deletion," where vectors are merely marked as inactive, with the actual space reclamation occurring during background compaction processes

2. Similarity Search Capabilities

The core utility lies in its ability to perform highly efficient similarity searches, identifying data points, aka vectors, that are "closest" or most semantically similar to a given query vector.

There are a few types of searches that we can perform:

k-Nearest Neighbors (k-NN): This fundamental search type aims to retrieve the 'k' most similar vectors to a query vector, as measured by a chosen similarity metric. While this approach ensures perfect accuracy, its computational intensity (O(n) time complexity) makes it impractical for large datasets. It is best suited for smaller datasets (e.g., under 10,000 vectors).
Approximate Nearest Neighbor (ANN): To overcome the scalability limitations of exact k-NN, most vector databases employ ANN algorithms. ANN sacrifices a slight degree of accuracy for massive gains in speed and scalability, achieving sublinear time complexity (e.g., O(log n)). These algorithms utilize various techniques such as hashing, quantization, or graph-based methods—to intelligently narrow down the search space, making them ideal for large-scale, real-time AI applications.
Exact Nearest Neighbor (ENN): This term is often used to emphasize the guarantee of 100% accuracy through an exhaustive search, similar to exact k-NN. ENN is crucial for applications where precision is paramount, such as in legal or financial systems.

Range Search (or Radius Search): This type of query retrieves all vectors that fall within a specified distance (radius) from a given query vector. This is particularly useful for applications where a threshold of similarity is more important than a fixed number of results, for example, finding all items "similar enough" to a user's preference.

3. Scalar Data Management

Beyond calculating the raw vector similarity, they also provide robust capabilities for managing and filtering by associated metadata. Each vector embedding can have descriptive metadata (e.g., title, category, timestamp, author, price) that further characterizes the data it represents. This metadata is indispensable for:

Refining Search Results: Users can combine semantic similarity searches with precise filters based on metadata, thereby narrowing down results to a highly relevant subset. This functionality operates much like a
WHERE clause in SQL, significantly enhancing both the accuracy and efficiency of searches.
Types of Filtering:
- Post-query Filtering: This approach applies filter conditions to the top 'K' results obtained after a similarity search is completed. While simple to implement, it can lead to an unpredictable number of final results, potentially returning too few or even no matches if the initial 'K' results do not contain enough items that satisfy the filter criteria.
- In-query Filtering: This strategy executes similarity search and metadata filtering concurrently. By reducing the search space before extensive similarity calculations, it is generally more efficient. However, this method can be resource-intensive, as it requires both vector and scalar (metadata) data to be loaded into memory simultaneously, potentially leading to Out-Of-Memory (OOM) issues if not carefully managed.
- Fuzzy Filtering: A specialized filtering type designed to handle variations like typos, misspellings, or international spelling differences in metadata.

4. Query Language

A robust vector database must offer flexible and intuitive interfaces to enable developers to interact with it seamlessly. These interfaces are critical for enhancing developer experience and accelerating application development.

Key aspects include:

SDKs and APIs: Most vector databases should provide Software Development Kits (SDKs) in popular programming languages (e.g., Python, Node, Go, Java) and well-defined REST APIs. These SDKs and APIs abstract away the underlying complexities of vector operations, simplifying integration for developers and making application development more productive.
Query Language Flexibility: Beyond basic vector similarity search, modern vector databases support advanced queries that combine vector similarity with rich metadata filtering.

Non-functional requirements

Vector database performs its functions, impacting its overall success, stability, and operational viability in production.

Performance and Latency:
- Vector databases must deliver low-latency queries and high throughput, essential for real-time AI applications. This is achieved through specialized indexing techniques like Approximate Nearest Neighbor (ANN) algorithms, which significantly reduce search space compared to exhaustive linear scans.
- A key consideration is the trade-off between search accuracy and speed; ANN sacrifices slight accuracy for massive speed gains, while Exact Nearest Neighbor (ENN) guarantees 100% accuracy but is slower for large datasets.
Scalability:
- To handle growing datasets and increasing query loads, vector databases primarily rely on horizontal scaling, distributing data across multiple nodes.
- Sharding, where datasets are partitioned into smaller units, is the core mechanism, often optimized by grouping semantically similar vectors to enhance search efficiency.
- Challenges include managing "hot shards" and increased administrative burden.
Reliability and Durability:
- Reliability ensures consistent system performance without failure, often enhanced through redundancy and service replicas.
- Durability concerns data persistence and recovery from failures without loss. For in-memory systems, extensive replication is vital, while non-volatile storage (disk) and mechanisms like write-ahead logs or snapshots ensure long-term data safety.
- Fault tolerance is achieved through both sharding and replication, with multiple data copies across nodes ensuring continuous availability
Security:
- Security is paramount for vector data because we might be handling highly sensitive data that has to be compliant (HIPAA for healthcare, SOC 2, PCI DSS)
- Encryption:
  - Data in Transit: Secure communication typically mandates the use of Transport Layer Security (TLS) to encrypt data moving between clients and the database, safeguarding it from eavesdropping and tampering.
  - Data at Rest: Protecting sensitive information stored within the database is critical. A significant innovation in this area is "property-preserving vector encryption." This method is superior to simpler approaches like redaction or tokenization of Personally Identifiable Information (PII),

There are additional important non-functional requirements that go into designing a vector DB — like cost-effectiveness (dealing with higher dimension means more resources), consistency (eventual consistency, session consistency)

High-level design

Let’s start with the components needed for a vector database to handle the entire lifecycle from data ingestion to querying:

Core Components:

Ingestion Pipeline:
1. It is responsible for connecting diverse raw data sources (say text, images, video)—to preprocess the unstructured data, such as sanitizing the data, and transforming it into vector embeddings using appropriate ML models.
2. The output will be fed into the vector database (to store)
Storage Layer:
1. Now that we have the vector embeddings ready to store, we decide to store them either in-memory for hot data or disk-based for cold data, or a hybrid approach.
2. Vectors are typically organized into collections or indices where all the vectors within a given collection share the same dimensionality.
Indexing Layer:
1. This is a critical part for the efficient performance of the similarity search when you are dealing with billions of data—Imagine opening a dictionary without indexes; this makes it horrible to find similar words.
2. Common Indexing Techniques: tree-based, hashing-based, graph-based (e.g., HNSW), and quantization-based methods.
3. This layer is also responsible for handling incremental updates and deletions to the index.
Metadata Storage Layer:
1. This component stores all associated metadata for each vector.
2. This enables rich filtering capabilities, allowing queries to be narrowed down based on structured attributes (e.g., timestamps, categories, authors) in addition to vector similarity
Query Engine:
1. This component processes incoming similarity search queries. It takes a query vector (or raw text/image that is subsequently vectorized) and interacts with both the indexing layer and the metadata store to identify and retrieve the most relevant vectors.
2. It supports various similarity metrics (e.g., cosine, Euclidean) and search types (k-NN, ANN, range search).
API Layer: This layer provides programmatic access to the database through comprehensive SDKs in popular programming languages, REST APIs, GraphQL endpoints, or even SQL interfaces, simplifying integration for developers.

Data Flow

The data flow within a vector database system is a continuous process, designed to handle dynamic data and support real-time AI applications.

Raw Data Ingestion: Unstructured data (text, images, audio, video) from various sources (e.g., cloud storage buckets, internal enterprise systems) is fed into the ingestion pipeline.
Preprocessing & Sanitization: The raw data undergoes type-specific preprocessing—tokenization for text, resizing for images, and optional sanitization (e.g., masking, redaction) to prepare it for vectorization.
Embedding Generation (Vectorization): A pre-trained or custom machine learning embedding model transforms the preprocessed data into high-dimensional numerical vector embeddings.
Vector & Metadata Storage: These newly generated vector embeddings, along with their associated metadata (e.g., original text, image URL, timestamps, categories), are stored in the vector database's storage layer.
Indexing: As vectors are stored, the indexing layer concurrently builds or updates its specialized data structures like HNSW graphs to optimize for rapid similarity search. This process is continuous to accommodate incremental updates.
Query Reception: A user or an application sends a query (e.g., a natural language phrase, an image) to the vector database's API/query engine.
Query Vectorization: If the query is not already in vector format, it is transformed into a query vector using the same embedding model that generated the stored data, ensuring consistency in the vector space.
Similarity Search & Filtering: The query engine performs a similarity search against the indexed vectors, often applying metadata filters to narrow down the search space. This typically involves ANN algorithms for speed.
Result Retrieval & Ranking: The database returns the 'k' most similar vectors (and their associated metadata) based on the chosen similarity metric and ranking. Hybrid relevancy scoring, which combines vector scores with traditional lexical scores, can be applied to enhance results.
Application Consumption: The application processes these results, for instance, by displaying recommended products, generating a contextual answer for an LLM, or identifying visually similar content.

fig: High-level architecture of a vector DB

Bonus: Tools and Languages

Building and interacting with vector databases often involves a diverse ecosystem of programming languages, specialized database solutions, indexing libraries, and integration tools. Understanding these options is crucial for successful implementation.

Programming Languages

The choice of programming language often dictates the available SDKs and community support for interacting with vector databases.

Python: The undisputed champion in the AI/ML landscape, Python offers extensive libraries and SDKs for nearly all major vector databases. Its rich ecosystem for data science (NumPy, SciPy, Pandas) and machine learning (TensorFlow, PyTorch) makes it ideal for generating embeddings and orchestrating AI applications.
Java, Go, Node.js: Many enterprise-grade vector databases provide official SDKs for these languages, enabling their seamless integration into large-scale backend services, microservices, and web applications.
Rust/C++: For highly performant, low-level implementations and core database engines (e.g., Qdrant's core in Rust, Faiss in C++), these languages offer unparalleled speed and memory control.
Julia: While less common, Julia is gaining traction in scientific computing and could be a compelling choice for specific high-performance, custom algorithm development.

Conclusion

Vector databases are no longer a niche technology; they're a cornerstone for building intelligent, AI-powered applications that understand context and meaning, not just keywords.

By diving into their design principles, from the core math of embeddings to the complexities of distributed systems, you're now equipped to tackle the next generation of data challenges and build truly smart systems. The future of search and recommendations is here, and it's powered by vectors!