Vector Database for AI Agents: How to Choose a Vector Database Based on Latency, Costs, and Scale

In recent years, anyone seriously involved in artificial intelligence has felt it firsthand: the world is moving too fast for "regular" databases. Suddenly every small startup has an AI agent, or five, trying to answer customers, analyze documents, recommend, summarize, translate, and sometimes even schedule appointments. Everyone talks about models, prompts, RAG, context windows. But behind the scenes, quietly, what keeps all of this from falling apart are actually Vector Databases.

And the real question, which even very experienced companies in Israel find themselves returning to again and again, is not just which model to choose, but: how do you choose a vector database that fits one AI agent, or hundreds of them, without getting stuck on crazy latency, ballooning costs, and scale that stops working exactly when you need it?

Why Do You Even Need a Vector Database for an AI Agent?

Let's start from the basics for a moment. Most AI agents we encounter today – whether it's a chatbot on a customer service website, an internal system in an organization that searches documents, or a smart agent that helps developers – are built on a simple idea: the agent needs to "remember" and understand information that doesn't all fit into its head (i.e., the model's context window), and bring it exactly when needed. For this, it uses storage of vectors – mathematical representations of text, code, images, sometimes also logs – and performs meaning-like searches, not just by keyword.

This search, similarity search, is the beating heart of RAG systems and every AI agent that wants to be a bit smarter than the dumb chatbots of 2018. Here enters the Vector DB: a system that stores millions (and sometimes billions) of vectors, allows inserting, updating, deleting, and mainly – finding very quickly the relevant segments when a new question arrives.

In other words: without a successful vector database, the AI agent remains mainly a nice model without memory. It answers nicely on general questions, but falls when it needs to understand what's written in the company's documents, what was summarized in recent emails, or why this particular feature was built in a certain way two years ago.

It's Not Just About Technology – But User Experience

This might sound technical, but the user on the other side feels it as something much simpler: either it works, or it feels broken. An AI agent with latency that's too high, or with inconsistent results, is simply perceived as unreliable. Anyone working with it day-to-day won't be able to trust it.

And here begins the delicate game between three forces: Latency, Costs, and Scale. You can invest in powerful and crazy servers and get low latency – until the bill arrives. You can do the opposite – choose a "cheap" cloud solution – but discover that in real time, when the AI agents start talking, the system creaks.

Latency: How Slow Is Already "Too Slow" for an AI Agent?

Let's talk about the moment of truth. The user asks a question. The AI agent accesses the Vector DB, performs a search, returns contexts, the model generates an answer. Everything happens in seconds. At least it should.

Where does this break in practice? Usually – in the Round Trip between the model and the vector database. If every search call takes 300–400 milliseconds, and in every answer there are several such calls (because the agent runs several steps, or tries several strategies), suddenly we've easily crossed 5–8 seconds per answer. In a world where a user expects an almost immediate response from the AI agent, this feels like an eternity.

Theoretical Latency vs. Real Latency

In presentations, everyone talks about latency of "under 50ms per query". In reality, especially when dealing with complex cloud environments, this starts to look different:

• The network connection itself (VPC, VPN, Gateway) adds layers of delay.
• The physical distance between the server running the AI agent and the Vector DB matters. Wrong cloud region – and you're in trouble.
• When load increases – some solutions start throttling, suddenly your P95 latency looks much less pretty.

Something else that's not always talked about out loud: a good AI agent, especially those that perform "planning" or multiple steps, don't settle for one search query. Sometimes there are 5–10 interactions with the vector database before the answer goes out to the user. Seemingly small latency accumulates very quickly.

How Do You Check Latency in a Real Way?

Instead of relying on the nice numbers from the Vector DB provider, many organizations in Israel already understand: you need to set up a small pilot, put a real AI agent on it – even if it's an MVP – and measure over days:

• What's the P50, P90, P95 latency under small, medium, and high load?
• How does the system behave under peak loads – for example on campaign publication days or launches?
• What happens when the number of vectors grows 10x? 100x?

Only then, suddenly you discover that certain solutions shine on a hundred thousand vectors, but start making disturbing creaks in the first millions.

Costs: Where Does the Money Go When an AI Agent Meets a Vector DB?

The money topic, as usual, comes a bit too late in conversations with developers. They've already chosen technology, already started measuring latency, and then someone from finance quietly asks: "Can we get a cost estimate for a year?". And there, sometimes, comes the shock.

When talking about Vector Database for AI agents, costs usually divide into several components:

• Storage: How much does it cost to store a million, ten million, a hundred million vectors? And does the price increase linearly or exponentially?
• Queries: There are payment models of "per request" or "per RU" (Request Unit). An AI agent with many steps can burn a lot of such units.
• Network: Sometimes ignored, but if the AI agent and Vector DB don't sit in the same cloud region, data traffic can increase the bill.
• Management and Maintenance: When choosing a self-hosted solution, you need to invest DevOps time, monitoring, updates, security. This is money, even if it doesn't appear directly on the cloud bill.

The Common Mistake: Thinking Only About Storage Price

Many organizations look at the price per GB per month, say "totally fine", and ignore query costs. But an AI agent that takes 5–10 embedding lookups per conversation, and works with hundreds or thousands of users in parallel, can raise that part of the bill to unexpected numbers.

One trick that works not bad in reality: try to simulate real usage and divide the total cost into "cost per conversation" or "cost per active user per month". When asking "how much does it cost us to allow one user to work a full day with the AI agent?", the financial conversation suddenly becomes more concrete.

On-prem, Self-hosted, or SaaS?

In this arena there are three main directions, and each has a different price – not just in money:

• SaaS Vector DB – convenient, quick to set up, usually with good latency (if sitting in the same cloud region). The price is usually per query/storage, with pricing models that aren't always transparent to the end.
• Self-hosted in the cloud – you run solutions like Qdrant, Weaviate, Milvus, Vespa, or even Elastic with vector search yourself. Requires DevOps, but allows control over cloud costs and configuration.
• On-prem – mainly in conservative or data-sensitive organizations (banks, insurance, government). Here hardware costs, storage, and operations personnel enter the picture heavily.

In Israeli reality, quite a few companies choose a hybrid model: start with SaaS to move fast, and only when AI agent usage stabilizes and intensifies – do a gradual migration to Self-hosted, to better control costs and latency.

Scale: When AI Agents Multiply

Another point that's not always thought about at the start: usually you don't stay with one AI agent. There's a support agent, there's an internal knowledge agent, an agent that analyzes logs, and another one dealing with finance. Each of them generates more vectors, and more load on the Vector DB.

If at the start you worked with half a million vectors, and suddenly you're on the way to ten million, the system's behavior can change. What worked well in the lab doesn't always survive when scale becomes real.

The Index Question: ANN, HNSW, and What's Between

Behind the scenes, most Vector DBs use data structures of type Approximate Nearest Neighbor (ANN) – with algorithms like HNSW, IVF, and more acronyms. Seemingly these are internal product details, but in practice this affects user experience and scaling ability:

• Some indexes are very fast for Query, but heavy to build and update.
• Others are more flexible for real-time updates, but lose a bit of accuracy or latency.
• When the number of vectors grows dramatically, some indexes "swell" in memory.

Anyone building dynamic AI agent systems – those that generate more and more knowledge, not just read static knowledge – needs to pay very close attention to how the Vector DB handles continuous writing (ingestion) alongside fast reads.

Multi-tenant and Namespaces

Especially in startups that sell AI agent solutions to customers, another question arises: does the Vector Database know how to manage separate "data spaces" (namespaces, collections) for different customers, without harming performance?

Some solutions tend to behave very nicely when there's one large collection, but start getting complicated when there are hundreds of small collections. Others, on the contrary, were built from the start for multi-tenant scenarios and allow separating each customer's data well, both in terms of performance and security.

The Israeli Reality: Cloud, Regulation, and AI Agents in Hebrew

Like in many technological fields, in Israel there's a small addition that complicates the picture. On one hand, Israeli startups run fast, choose advanced cloud tools, and integrate AI agents in almost every new product. On the other hand, there are banks, insurance companies, public bodies – where they don't approve so quickly to take sensitive data out of the country or from the closed VPC.

In these places, the conversation about vector database also becomes a conversation about data security, regulation, and physical location of data. Not every SaaS provider is ready to run cloud instances in Israel, not every solution meets ISO/PCI/local regulation requirements.

In addition, an AI agent that works in Hebrew – and this is an interesting point – sometimes generates embeddings that are a bit different (and noisier) from languages like English. The meaning is that the quality of semantic search in the Vector DB – and how it handles the mess of grammatical gender, acronyms, slang – becomes even more critical.

Not always the solution is in the Vector DB itself, but when planning the architecture, you need to take into account that sometimes we'll want pre-processing processes, additional filters, or even embedding models adapted for Hebrew – and all of this sits above (or alongside) the search steps in the vector database.

How Do You Even Approach the Choice? Not a "Checklist", But Some Insights

We could try to give a rigid checklist here: if you have so many users, choose solution X. But the world of AI agents changes too fast for rigid recipes. Instead, let's talk about some principles that help make a reasonable decision, even if it's not "perfect".

1. Start with the Problem, Not the Technology

Before you choose a vector database, try to formulate for yourselves: what AI agent are you building? Who is it intended for? How many interactions are expected per day? How much knowledge does it need to digest? Does it mainly read static documents, or constantly generate new knowledge (logs, events, summaries)?

An AI agent intended to help a lawyer search contracts, with a few hundred large documents, doesn't look like an AI agent that sits inside a SaaS system with thousands of users generating text every minute. It's not the same latency, not the same cost emphasis, not the same scale.

2. Think About Latency as User Experience, Not a Number on a Dashboard

Many times latency is measured at the Vector DB API level. But the user feels total Response Time – from the moment they press Enter until the full answer returns. Within this there is:

• Time to create Embedding (if done in real time).
• Search time in Vector DB.
• Time to retrieve additional data (metadata, full documents).
• LLM model runtime.
• All the internal "deliberations" of the AI agent (if it has planning).

When examining a vector database solution, it's worth measuring some conversations end-to-end, not just the single search call. Sometimes a small improvement in Vector DB latency is very felt in user experience, and sometimes the bottleneck is actually somewhere else entirely.

3. Understand Usage Patterns: Burst vs. Steady-state

If your AI agents work mainly in strong but short loads (for example, during a webinar, or after sending a newsletter), it's important that the Vector DB knows how to handle burst traffic without crashing. Some solutions know how to absorb short peaks well, others are built more for stable and continuous use.

On the other hand, if your organization operates an internal AI agent that works in the background all day, performing indexing, scanning, analysis – maybe you'll want a solution that's better with steady-state capacity, and not just with momentary peaks.

4. Be Honest About DevOps Capabilities

Sounds trivial, but happens quite a bit: developers get excited about a Self-hosted solution, choose an advanced Vector DB, open it in a Kubernetes cluster – and then discover there aren't really resources in the organization to maintain it. Updates get delayed, monitoring is problematic, no one knows exactly what's happening when there's a failure at 3 AM.

In such a situation, sometimes it's better to pay a bit more for managed SaaS, and be quiet in terms of availability and maintenance. Only in organizations that have serious DevOps – and not "one person who's already overloaded" – is there logic to quickly jump to Self-hosted Vector DB for critical AI agents.

5. Think Ahead: Where Are Your AI Agents Evolving?

A question worth asking honestly: where do you see your AI agents in a year? If it's a one-time project, POC, or limited internal tool – it might simply be better to go for the fastest solution to set up, even if it's more expensive in the long run.

But if you're building an entire platform based on AI agents, a SaaS product, or a capability that will become critical to the organization – it's very worth investing a bit more thought in choosing a vector database that can perform gradual scaling, be flexible in pricing, and integrate well with the rest of your architecture components.

Frequently Asked Questions About Vector DB and AI Agents

Do You Always Need a Vector Database for an AI Agent?

Not necessarily. There are simple agents that can make do with short-term memory, or regular textual storage. But once it's about semantic search on a medium amount of information and above – contracts, documents, code, logs – Vector DB gives a clear jump in capabilities. Every AI agent that needs to "understand" an information archive will benefit significantly from organized vector storage.

What's More Important: Latency or Search Quality (recall/precision)?

It depends on usage. In customer support chat, latency is usually decisive – the user won't wait 10 seconds per answer. On the other hand, in an AI agent that helps a lawyer find a clause in a contract, you can tolerate an additional second of search, if the result will be much more accurate. In practice, we try to reach a balance point – low enough latency, with search quality good enough for use.

How Many Vectors Is Considered "A Lot" for a Vector DB?

The numbers are a bit misleading. There are systems that handle tens of millions of vectors beautifully, and there are those that already at a few million start showing signs of strain. But as a rule of thumb: up to a million vectors – almost any reasonable solution will work. Between a million and ten million – you already need to seriously check performance. Above ten million – a real pilot with load similar to the production environment is mandatory.

Does Vector Size (dimension) Matter?

Yes. The higher the vector dimensionality, the heavier ANN searches can be. Most common models today work around 384–1536 dimensions. When choosing an embedding model for an AI agent, there's consideration not just of quality, but also of weight – how much does it cost to search within a large vector.

Is It Worth Combining Several Vector DBs in Parallel?

In some advanced systems, yes. There are architectures where an AI agent uses one Vector DB for hot data – active documents, recent conversations – and another Vector DB for archive data. There are also solutions that combine built-in vector search in a regular relational database (Postgres, for example) with a dedicated Vector DB. This slightly increases complexity, but sometimes creates a good balance between cost, latency, and scale.

Summary Table: Choosing a Vector Database for AI Agents

Criterion	What to Actually Check	Impact on AI Agent
Latency	P50/P95 measurement under load, geographic location, indexing time	Directly affects response time and "flow" feeling of the conversation
Storage Costs	Price per GB, data growth rate, hot/cold storage	Determines ability to grow to millions of vectors without financial collapse
Query Costs	Price per Query/RU, daily usage forecast, Burst	Affects cost per conversation/user, especially in multi-step agents
Scale	Performance on millions of vectors, parallel writes, indexes	Determines if you can add AI agents and customers without harming performance
Deployment Model	SaaS vs. Self-hosted/On-prem, DevOps requirements	Defines setup time, operational flexibility, and control level
Multi-tenant	Collections/Namespaces, data isolation, permissions	Critical for products with multiple customers or different teams
Security and Regulation	Cloud regions, encryption, standards (ISO, SOC2, etc.)	Affects ability to work with financial, health, public sectors
Ecosystem Integration	SDKs, programming language support, integration with existing LLMs	Shortens AI agent development and reduces unnecessary code "glue"

A Final Word: How Not to Get Lost in All of This

If you've reached here, you're probably either building a serious AI agent right now, or at least thinking about how to introduce one into your product or organization. You might also be feeling that feeling – that there are too many options, too many tools, and too many nice words in presentations.

The key, in the end, is not to choose "the perfect Vector DB" – there probably isn't one anyway – but to choose a tool that fits your current situation, and has a logical growth path with you. Start small, measure, understand where the real pains are – latency, cost, scale – and then tune.

And if there's something to learn from recent years in the AI world, it's that the combination of good models with the right data infrastructure can turn an AI agent from a gimmick tool into a real team member. One that produces value, not just noise.

If you're hesitating between solutions, worried about costs, or simply want to hear from someone who's already seen several projects burn on wrong infrastructure choices – we'd be happy to help with an initial consultation at no cost, help make a bit of order before jumping into the water.