You made decisions about your data architecture three years ago that worked well. You were using data pipelines for analytics and structured data by sending it to the warehouse. Dashboards used queries to display information.
Fast forward to today, your team is also building AI features such as Semantic Search, Recommender Engines and Large Language Models.
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Your team is now facing issues with existing AI data architecture to support those use cases. The performance of queries is low, the relevancy of the results from your queries is low and your engineering team is debating:
"Is a vector database necessary for our team?"
For Staff and Principal Engineers understand the following:
- What is a vector database (without all of the hype)?
- How does a vector database fit into today’s data architecture for AI?
- Am I using a vector database properly?
As stated above, not every AI system requires a vector database; however, if a vector database is required it will be heavily used.
Straight-Forward Definition of AI Data Architecture
To understand the vector database, let us first have a basic understanding of data architecture for AI.
A Basic Definition of Data architecture for AI:
Data architecture for AI defines how an organization collects, processes, stores, and serves data to machine learning models and AI solutions.
Key Components of Data Infrastructure
| Component | Definition |
|---|---|
| Data Ingestion | Receive raw data |
| Storage | Save structured and unstructured data |
| Processing | Clean and modify data |
| Orchestration | Administer multiple processes |
| Observability | Verify reliability of a particular process |
How Traditional Systems are Insufficient
Traditional systems are geared towards:
- Structured Queries
- Exact Match
- Relational Data
AI Systems typically require:
- Semantic Understanding
- Similarity
- High Dimensional Data Handling
This is where Vector Databases Come into Play.
Key Insight
Vector Databases are not a replacement for your data platform but are an extension of your platform for certain AI use cases.
Why AI Data Infrastructure is More Important than Ever by 2026
The need for specialized infrastructure will continue to grow.
1. Growth of LLM and Semantic Based Systems
The following applications are now possible:
- Semantic Search
- Chatbots
- Recommendation Engines
They Require:
- Embedding
- Similarity Matching
2. Unstructured Data Growth
Today’s systems can manage:
- Text
- Images
- Audio
Traditional databases cannot.
3. Customer Expectations have evolved
Customers Expect:
- Relevant Data
- Contextual Responses
- Real-time Performance
The Gap
Most Data Pipelines are still designed for:
- Batch process
- Structured Queries
ALL of the above is not inclusive of:
- Vector Similarity
- Embedding Based Retrieval
Key Insight
AI Workloads require an entirely NEW pattern of data access, not just MORE data.
Core Components of your AI Data Infrastructure; What are you building
To understand how and where Vector Databases fit within your infrastructure, break down your system.
1. Ingestion Layer
Receive the following types of data:
- Raw Text
- User Action
- External Data
2. Processing Layer
Convert Data into:
- Features
- Embedding (your AI use cases)
3. Storage Layer
- Data Warehouses
- Data Lakes
- Vector Database (speciality)
4. Serving Layer
Handle the following:
- Query
- Inference for Models
5.Observability Layer
Observability Layers Are A Way To Observe Data Quality, Pipeline Performance And Where Vector Databases Participate In The Overall Processing Layer.
Vector Databases Are Not Core Infrastructure, But Rather A Supplement To Existing Processing Infrastructure, Which Are Based On Use Cases.
Data Infrastructure For AI - Walkthrough Example Of How Data Infrastructure Works In The Real World.
Let Us Walk-Through A Common AI Use Case, Semantic Search System, Step 1 Data Ingest.
Data Ingesting Will Include Document Collections And User Queries.
Data Will Be Embedded To Create New Vectors From Text, And Then Stored In A Vector Database While The Original Document Data Will Go In Traditional Storage Systems.
Once The User Submits Their Query, The Query Is Created By Converting The Query To A Vector Format And Then Comparing The Vector To The Vectors That Were Previously Created And Are Stored In The Vector Database.
The Most Similar Results Will Be Returned Back To The User By The System Based On The Rank Of That Result As Compared To The Other Stored Vectors.
Traditional Systems Fail Because The Query Will Be Based On Keyword Matching, And Consequently This Will Not Be A Matching Item.
If Vector Databases Hadn’t Been Used, Then Results Would Have Been Returned As If They Were Semantically Related. Vector Databases Create A Means To Allow For Meaning Based Retrieval, As Opposed To Keyword Based Retrieval.
Common Mistakes That Teams Make With AI Data Infrastructure
Even the most experienced teams will make common mistakes that could have been avoided.
Many Teams Have Not Fully Validated The Need For Vector Databases Before They Begin The Process Of Implementing Of Them.
Using Vector Databases Prematurely Without A Clear Use Case.
There Are Use Cases Where You May Need To Use A Vector Database For Example Semantic Search.
Over Engineering The Implementation Of A Vector Database Before Understanding The Trade-Offs Of The Implementation To Determine If A Vector Database Is The Right Solution.
Ignoring The Data Pipeline Requirements For Vector Database Implementation.
Vector Databases Need An Embedding Generation Data Pipeline, For Ongoing Updates To The Vector Database.
Underestimating The Operational Complexity Of Vector Databases
As The Implementation Of A Vector Database Introduces New Scaling Challenges And Increased Monitoring Challenges.
Key Insight
Vector Databases Solve Specific Problems.When you use a vector database without needing it makes things harder than they should be.
AI Infrastructure Data Best Practices: What High-Performing Teams Do
Great teams create their own rules.
1. Start With Your Use Case
Use vector databases when you have a reason to, such as:
- Semantic Search
- Recommendations
- LLM Retrieval
2. Connect With What You Already Have
Do not change:
- Warehouses
- Data Lakes
Instead:
- Extend Current Architecture
3. Have a Good Pipeline
Make sure you have:
- Reliable Embedding Generation
- Consistent Data Updates
4. Monitor Your System
Measure:
- Response Time
- Accurate Results
- Reliable System
5. Use All-in-one Products
Use all-in-one products like Logiciel, to:
- Cover observability and lineage
- Provide infrastructure that can support an AI
- Simplify operations
Key Insight
The best infrastructure is as simple as possible, and as complex as necessary.
Do You Actually Need a Vector Database?
Here Is The Practical Way:
You Probably Do If:
- You are building semantic search
- You are using embeddings
- You are using LLMs
You Probably Don't If:
- You have structured queries
- You need a simple keyword search
- You don't have multiple dimensions
Decision-Making Framework
Your Questions:
- Do we need semantic understanding?
- Are we using embeddings as an important part of our systems?
- Do traditional databases meet our current needs?
If you answered yes to the above, you may want to explore using a vector database.
Key Insight
Technology should be adopted according to your business model-not based on trends.
Conclusion
Although vector databases are extremely useful, they cannot be the sole databases used in every application.
Here Are 3 Key Takeaways:
- Vector databases solve specific AI issues
- Not all applications need vector databases
- Vector databases will complement existing infrastructure
Your overall data infrastructure should continue to be a core component of AI just as before.
When done right, vector databases allow you to create:
- Better Search Relevancy
- Better Recommendations
- Stronger AI
RAG & Vector Database Guide
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Call To Action
If you’re looking at your data stack for AI:
Start with:
- Design And Scale Your Data Infrastructure
- Real-time Data Infrastructure
Or Go To The Next Step
👉 Go To Logiciel’s Site To See What We Do For Teams That Need An AI Compliant Data Infrastructure.
At Logiciel Solutions, we work with engineering teams to create systems that are as:
- Simple
- Scalable
- Performing
To Get You The Right Tools-At The Right Time.
Frequently Asked Questions
What Is A Vector Database?
Vector databases are the tools to store and retrieve vectors of many dimensions, so you can search for similarities, rather than for an exact match.
How Is A Vector Database Different From A Traditional Database?
Traditional databases only use structured queries and find exact matches, while vector databases search for items based on the similarity of their embeddings.
Do All AI Systems Use Vector Databases?
No, vector databases are only for systems that use embeddings or semantic search.
What Are Common Uses?
- Semantic Searching - Recommendations - Retrieving LLMs
How Do You Decide If You Need A Vector Database?
You should examine if your system has any semantic understanding, or if you will be using any form of embedding retrieval.
