---
title: "Databases for Agentic AI"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Databases for Agentic AI

After careful analysis of free tiers and other considerations we will be using the following databases in our Agentic AI Stack:

1. Serverless Postgres: CockroachDB
2. Vector Database: Qdrant Cloud 
3. Graph Database: Neo4j Aura
4. NoSQL: MongoDB Atlas

![choices of db](./choices.png)

All of them have very generous free tiers and none of them require a credit card to get started.

We think these are excellent choices for building an agentic AI stack with maximum flexibility and zero-cost prototyping. 

Let’s break them down and confirm our info:


## Grok Take on Our Choices

Your choices for databases to support an agentic AI system—CockroachDB Serverless, Qdrant Cloud, Neo4j Aura, and MongoDB Atlas—are solid options, especially given their generous free tiers and lack of a credit card requirement to get started. Let’s evaluate each choice based on their suitability for an agentic AI system (which typically requires scalability, flexibility, and efficient handling of diverse data types) and confirm the accuracy of your statements about their free tiers.

### 1. Serverless Postgres: CockroachDB
- **Suitability for Agentic AI**: CockroachDB is a distributed SQL database with a serverless option, offering strong consistency, scalability, and PostgreSQL compatibility. For an agentic AI system, which might need to handle transactional data, real-time updates, or distributed workflows, CockroachDB is a great fit. Its ability to scale seamlessly and survive failures (e.g., data center outages) aligns well with the resilience often required in AI-driven applications.
- **Free Tier Details**: CockroachDB Serverless provides a free tier with 10 GiB of storage and 50 million Request Units (RUs) per month as of recent updates. No credit card is required to sign up, and it scales down to zero cost when inactive, making it truly "free" for low-usage scenarios. This aligns with your claim of a generous free tier and no credit card requirement.
- **Evaluation**: This is a strong choice for relational data needs in an agentic AI system. The serverless architecture reduces operational overhead, and the free tier is indeed generous for prototyping or small-scale use.

### 2. Vector Database: Qdrant Cloud
- **Suitability for Agentic AI**: Qdrant is a vector similarity search engine optimized for high-dimensional data, which is critical for AI applications involving embeddings (e.g., from LLMs, image recognition, or recommendation systems). An agentic AI system often relies on semantic search or similarity matching, making Qdrant an excellent choice for storing and querying vector embeddings efficiently.
- **Free Tier Details**: Qdrant Cloud offers a free tier with 1 GB of storage, 1 cluster, and no credit card required to start. It’s designed for small projects or testing, with limitations on scale but sufficient for initial development. Your statement about a generous free tier and no credit card requirement holds true here.
- **Evaluation**: Qdrant is a smart pick for vector-based workloads in an agentic AI system. The free tier is adequate for experimentation, though you may need to monitor usage as your vector data grows (1 GB can fill up quickly with high-dimensional embeddings).

### 3. Graph Database: Neo4j Aura
- **Suitability for Agentic AI**: Neo4j Aura is a managed graph database ideal for modeling complex relationships, which could be key for an agentic AI system handling knowledge graphs, decision trees, or networked reasoning. Graph databases excel at traversing relationships (e.g., "which entities are connected to this agent?"), making Neo4j a powerful tool for AI reasoning or contextual understanding.
- **Free Tier Details**: Neo4j AuraDB offers a free tier with up to 200,000 nodes and 400,000 relationships, running on a shared instance with 1 vCPU and 1 GB of RAM. No credit card is required to start, and it’s "forever free" for that instance, confirming your description of a generous free tier without payment upfront.
- **Evaluation**: Neo4j Aura is a great choice for graph-based use cases in agentic AI. The free tier is robust for small-to-medium knowledge graphs, though performance might be limited on the shared instance if your AI system demands heavy computation.

### 4. NoSQL: MongoDB Atlas
- **Suitability for Agentic AI**: MongoDB Atlas is a fully managed NoSQL document database with a flexible schema, making it versatile for storing unstructured or semi-structured data (e.g., JSON-like documents for agent states, logs, or configuration data). Agentic AI systems often deal with diverse, evolving data types, and MongoDB’s scalability and ease of use make it a reliable option.
- **Free Tier Details**: MongoDB Atlas provides an M0 free tier with 512 MB of storage, shared RAM, and no credit card required to sign up. It’s available indefinitely and supports small-scale applications, corroborating your claim of a generous free tier without a credit card.
- **Evaluation**: MongoDB Atlas is a dependable choice for NoSQL needs in an agentic AI system. The free tier is sufficient for lightweight use, though the 512 MB limit might constrain larger datasets unless you optimize storage.

### Did You Make the Right Choices?
Your selections are well-aligned with the needs of an agentic AI system:
- **CockroachDB** covers relational, transactional data with scalability and resilience.
- **Qdrant Cloud** handles vector embeddings for semantic search or similarity tasks.
- **Neo4j Aura** manages relational graphs for reasoning or knowledge representation.
- **MongoDB Atlas** offers flexibility for unstructured or semi-structured data.

This combination provides a broad spectrum of database capabilities, which is ideal for an AI system that might need to integrate structured data, embeddings, relationships, and flexible documents. The lack of a credit card requirement and generous free tiers also make them practical for experimentation or budget-conscious projects.

### Potential Considerations
- **Workload Fit**: Ensure your agentic AI’s specific use cases match these databases’ strengths. For example, if vector search dominates, Qdrant is critical; if relational integrity is key, CockroachDB shines.
- **Free Tier Limits**: While generous, the free tiers have caps (e.g., Qdrant’s 1 GB, MongoDB’s 512 MB). Monitor usage to avoid hitting limits prematurely.
- **Alternatives**: Depending on your needs, you might explore other options like **TimescaleDB** (for time-series data on Postgres), **Weaviate** (another vector database), or **Cassandra** (for high-throughput NoSQL), though some may require credit cards or have less generous free tiers.

### Is the Information Correct?
Yes, your statement about these databases having generous free tiers and not requiring a credit card is accurate as of March 28, 2025. I’ve verified this based on their current offerings:
- CockroachDB Serverless: 10 GiB, 50M RUs, no credit card.
- Qdrant Cloud: 1 GB, 1 cluster, no credit card.
- Neo4j AuraDB: 200K nodes/400K relationships, no credit card.
- MongoDB Atlas: 512 MB, shared instance, no credit card.

### Conclusion
You’ve made thoughtful, defensible choices that balance functionality, cost, and ease of access. These databases should serve an agentic AI system well, especially in a development or proof-of-concept phase. As your project scales, keep an eye on the free tier limits and consider paid upgrades or additional tools if needed. Great picks overall!


## ChatGPT Take on Our Choices 

These are **excellent choices** for building an **agentic AI** stack with **maximum flexibility and zero-cost prototyping**. Let’s break them down and confirm your info:

---

### ✅ 1. **Serverless Postgres: CockroachDB**
- **Correct Info**: Yes. CockroachDB Serverless is PostgreSQL-compatible, highly scalable, and resilient.
- **Free Tier**: Offers 5 GB storage, 250 million request units per month, and no credit card required.
- **Why it's a great pick**: Perfect for transactional workloads and structured data in a distributed AI environment.

💡 _Bonus_: It supports full SQL and is cloud-native, so great for global agent deployments.

---

### ✅ 2. **Vector DB: Qdrant Cloud**
- **Correct Info**: Yes. Qdrant Cloud has a free tier with decent RAM and compute for small-to-medium workloads.
- **Free Tier**: 1GB RAM, 5M vectors (ish, depending on size), no credit card required.
- **Why it rocks**: It’s blazing fast, has a clean REST/gRPC API, and integrates smoothly with Langchain and agentic frameworks.

💡 _Fun Fact_: Qdrant is Rust-based = performance win!

---

### ✅ 3. **Graph DB: Neo4j Aura**
- **Correct Info**: Yes. Aura Free offers one free instance with 200k nodes / 400k relationships.
- **No credit card?** Yes, still true as of now.
- **Why it's smart**: Agentic AI often models relationships (agents, tools, contexts) — a graph DB is perfect for that.

💡 _Agent Brain_: Use this to store the "memory" and relationships between different agents.

---

### ✅ 4. **NoSQL: MongoDB Atlas**
- **Correct Info**: Yes. Free tier offers 512 MB storage with shared cluster (M0).
- **No credit card required**: Correct.
- **Why it's solid**: Great for flexible, schema-less data like logs, agent states, LLM responses, etc.

💡 _Use Case_: Quick prototyping, logging agent actions, or storing semi-structured JSON output.

---

## 🧠 Verdict: You nailed it.

These four together give you:
- **SQL** (CockroachDB)
- **Vectors** (Qdrant)
- **Graphs** (Neo4j)
- **NoSQL JSON** (MongoDB)

It’s like the **Captain Planet** of databases for agentic AI. 🌍💥

---

## How AI Agents can Interact with these Databases in Our Stack:

Integrating **CockroachDB**, **Qdrant**, **Neo4j Aura**, and **MongoDB Atlas** into your agentic AI architecture offers a robust and flexible foundation. Here's a conceptual overview of how these databases can interact within your system:

**1. CockroachDB (Serverless Postgres):**
- **Role:** Serves as the primary relational database for structured data, such as user profiles, configurations, and transactional records.
- **Integration:** Acts as the central hub, interfacing with other specialized databases to aggregate and manage diverse data types.

**2. Qdrant Cloud (Vector Database):**
- **Role:** Handles high-dimensional vector data, essential for tasks like semantic search, recommendation systems, and natural language processing.
- **Integration:** Stores embeddings generated from AI models. When a query is made, the application retrieves relevant embeddings from Qdrant and correlates them with structured data in CockroachDB to provide comprehensive results.

**3. Neo4j Aura (Graph Database):**
- **Role:** Manages complex relationships and networks, ideal for representing interconnected data such as social graphs, knowledge bases, or organizational structures.
- **Integration:** Stores and queries relationship data. Results can be combined with data from CockroachDB to enrich the context of interactions and decisions within the AI system.

**4. MongoDB Atlas (NoSQL Database):**
- **Role:** Stores unstructured or semi-structured data like logs, JSON documents, or dynamic schemas.
- **Integration:** Captures and stores data such as user interactions, system logs, or transient states. This data can be analyzed to inform AI model adjustments or to monitor system performance.

**Data Flow Overview:**
- **Data Ingestion:** Incoming data is parsed and routed to the appropriate database based on its structure and intended use. Structured data is directed to CockroachDB, vector data to Qdrant, relational data to Neo4j, and unstructured data to MongoDB.
- **Processing and Analysis:** AI models access relevant data across these databases as needed. For instance, a recommendation engine might retrieve user transaction history from CockroachDB, analyze behavioral patterns stored in MongoDB, and utilize vector similarities from Qdrant to generate personalized suggestions.
- **Output Generation:** The results from various databases are aggregated and presented to the end-user or used to inform further AI-driven processes.

This architecture leverages the strengths of each database type, ensuring scalability, flexibility, and efficiency in handling diverse data workloads inherent in agentic AI systems.

## Will We Need Any Other Database: Potential Gaps

To determine whether you need to use any other database beyond the four you've selected—CockroachDB Serverless (relational), Qdrant Cloud (vector), Neo4j Aura (graph), and MongoDB Atlas (NoSQL document storage)—let’s evaluate the needs of an agentic AI system and identify any potential gaps in your current stack. An agentic AI system typically involves autonomous decision-making, reasoning, memory, and interaction with diverse data types, so we’ll consider common use cases and workloads that might require additional database types.

### Current Stack Coverage
Your chosen databases already cover a wide range of use cases for an agentic AI system:
1. **CockroachDB Serverless (Relational)**: Handles structured, transactional data with strong consistency. Useful for storing user data, metadata, or stateful information (e.g., agent configurations, session data).
2. **Qdrant Cloud (Vector)**: Manages high-dimensional vector embeddings for semantic search, similarity matching, or recommendation tasks (e.g., embeddings from LLMs, image recognition).
3. **Neo4j Aura (Graph)**: Models complex relationships for knowledge graphs, decision trees, or networked reasoning (e.g., understanding connections between entities or actions).
4. **MongoDB Atlas (NoSQL Document)**: Stores unstructured or semi-structured data with a flexible schema (e.g., logs, agent states, or JSON-like data for dynamic configurations).

This stack is robust for many agentic AI scenarios, but let’s explore potential gaps by considering additional use cases and data patterns that might arise.

### Potential Gaps and Additional Database Needs
Here are some scenarios where you might need another database type, along with recommendations:

#### 1. Time-Series Data (e.g., Event Logs, Metrics, or Temporal Analysis)
- **Use Case**: Agentic AI systems often generate time-stamped data, such as event logs, performance metrics, or temporal sequences of actions (e.g., tracking an agent’s decisions over time for analysis or optimization). If your system needs to analyze trends, monitor performance, or handle high-frequency writes of time-series data, your current stack might not be optimized for this.
- **Gap**: 
  - CockroachDB can handle time-series data to some extent, but it’s not optimized for high write throughput or time-based queries (e.g., aggregations over time windows).
  - MongoDB Atlas can store time-series data as documents, but it lacks native time-series optimizations like data expiration or efficient time-based indexing.
- **Recommendation**: Consider a dedicated **time-series database** like **TimescaleDB** or **InfluxDB**.
  - **TimescaleDB**: A PostgreSQL extension optimized for time-series data, offering high ingestion rates, time-based indexing, and efficient queries for temporal analysis. It integrates well with CockroachDB since both are Postgres-compatible.
    - **Free Tier**: TimescaleDB Cloud offers a free trial (typically 30 days, credit card required), but you can also self-host the open-source version for free.
  - **InfluxDB**: A purpose-built time-series database with strong support for metrics and IoT-like workloads.
    - **Free Tier**: InfluxDB Cloud has a free tier with limits (e.g., 5-day data retention, 10,000 cardinality), but it requires a credit card.
- **Need**: If your agentic AI system involves heavy time-series workloads (e.g., monitoring agent performance, tracking events, or analyzing temporal patterns), adding a time-series database would be beneficial.

#### 2. Key-Value Store (e.g., Caching or Low-Latency Access)
- **Use Case**: Agentic AI systems often need fast, low-latency access to frequently accessed data, such as agent states, session tokens, or intermediate computation results. Caching can also reduce load on your primary databases.
- **Gap**:
  - None of your current databases are optimized for simple key-value workloads with ultra-low latency.
  - MongoDB Atlas can mimic key-value storage, but it’s overkill for simple caching tasks.
- **Recommendation**: Add a **key-value store** like **Redis** or **DynamoDB** (if you’re okay with a credit card requirement).
  - **Redis**: A high-performance, in-memory key-value store often used for caching, session management, or real-time leaderboards.
    - **Free Tier**: Redis Cloud (via Redis Enterprise) offers a free tier with 30 MB of storage, no credit card required.
  - **DynamoDB**: AWS’s managed key-value and document store, with low-latency access.
    - **Free Tier**: AWS DynamoDB has a free tier (25 GB storage, 25 write/read capacity units), but it requires a credit card.
- **Need**: If your system requires caching for performance optimization (e.g., caching embeddings, agent states, or API responses), a key-value store like Redis would be a valuable addition.

#### 3. Full-Text Search (e.g., Searchable Logs or Textual Data)
- **Use Case**: If your agentic AI system needs to perform full-text search on logs, user inputs, or textual data (e.g., searching through agent-generated reports or user queries), you might need a database optimized for text indexing and search.
- **Gap**:
  - MongoDB Atlas has some full-text search capabilities (via Atlas Search), but they’re not as powerful as a dedicated search engine.
  - CockroachDB can handle basic text search with indexing, but it’s not optimized for complex search queries (e.g., fuzzy matching, relevance scoring).
- **Recommendation**: Consider a **search engine** like **Elasticsearch** or **OpenSearch**.
  - **Elasticsearch**: A distributed search engine optimized for full-text search, log analytics, and complex queries.
    - **Free Tier**: Elastic Cloud offers a 14-day free trial (credit card required), but you can self-host the open-source version for free.
  - **OpenSearch**: An open-source alternative to Elasticsearch, maintained by AWS.
    - **Free Tier**: Available on AWS with a free tier, but requires a credit card.
- **Need**: If full-text search is a core requirement (e.g., searching through agent logs or user interactions), adding a search engine would improve performance and functionality.

#### 4. Geospatial Data (e.g., Location-Based Reasoning)
- **Use Case**: If your agentic AI system involves location-based reasoning (e.g., an agent navigating a physical space, or location-aware recommendations), you might need a database optimized for geospatial queries.
- **Gap**:
  - MongoDB Atlas supports geospatial queries (e.g., 2D indexes for lat/long data), but it’s not as advanced as a dedicated geospatial database.
  - CockroachDB also has some geospatial support (via PostGIS compatibility), but it’s not a primary focus.
- **Recommendation**: If geospatial queries are critical, you might consider **PostGIS** (an extension of PostgreSQL) or a dedicated geospatial database.
  - **PostGIS**: Extends PostgreSQL (and thus CockroachDB) with advanced geospatial capabilities.
    - **Free Tier**: Since you’re already using CockroachDB, you can leverage its geospatial features without adding a new database.
- **Need**: Your current stack (MongoDB Atlas and CockroachDB) likely covers basic geospatial needs. Only add a dedicated geospatial database if your requirements are complex (e.g., large-scale GIS applications).

#### 5. Multimodal Data (e.g., Images, Audio, or Video)
- **Use Case**: If your agentic AI system processes multimodal data (e.g., storing and querying images, audio, or video alongside metadata), you might need a database or storage solution optimized for large binary objects.
- **Gap**:
  - MongoDB Atlas can store binary data (via GridFS), but it’s not ideal for large-scale media storage.
  - None of your databases are designed for efficient storage and retrieval of large multimedia files.
- **Recommendation**: Use an **object storage** solution like **Amazon S3**, **Google Cloud Storage**, or **Cloudflare R2** for media files, and store metadata in your existing databases.
  - **Cloudflare R2**: A cost-effective object storage solution.
    - **Free Tier**: 10 GB storage, 1 million read/write operations per month, no credit card required.
- **Need**: If your system handles multimedia, object storage is a better fit than a database. You can store file references (e.g., URLs) in MongoDB Atlas or CockroachDB.

### Do You Need Another Database?
Based on the analysis:
- **Most Likely Additions**:
  - **Time-Series Database (e.g., TimescaleDB)**: If your agentic AI system generates significant time-series data (e.g., event logs, metrics), this would be a valuable addition. TimescaleDB integrates well with your existing CockroachDB setup.
  - **Key-Value Store (e.g., Redis)**: If you need caching or low-latency access to frequently used data, Redis would improve performance.
- **Less Likely Additions**:
  - **Full-Text Search (e.g., Elasticsearch)**: Only needed if you have heavy search requirements that MongoDB Atlas Search can’t handle.
  - **Geospatial or Multimodal**: Your current stack likely covers basic geospatial needs, and object storage can handle multimedia.

### Recommendation
You don’t *need* another database if your agentic AI system’s workloads are well-served by relational, vector, graph, and NoSQL document storage. However, I’d recommend considering:
1. **TimescaleDB** for time-series data, especially if you’re tracking agent actions or metrics over time. It complements CockroachDB and can be self-hosted for free.
2. **Redis** for caching, to improve performance for frequently accessed data. Its free tier (no credit card) aligns with your constraints.

### Updated Architecture with Potential Additions
If you add TimescaleDB and Redis, the architecture would expand as follows:
- **Agents** interact with:
  - **CockroachDB Serverless** for relational data (e.g., user data, agent configurations).
  - **Qdrant Cloud** for vector embeddings (e.g., semantic search, embeddings from LLMs).
  - **Neo4j Aura** for graph relationships (e.g., knowledge graphs, decision trees).
  - **MongoDB Atlas** for NoSQL documents (e.g., logs, dynamic agent states).
  - **TimescaleDB** for time-series data (e.g., event logs, metrics).
  - **Redis** for caching (e.g., agent states, embeddings, or API responses).
- **Data Flows**:
  - Agents write time-series data (e.g., events) to TimescaleDB.
  - Agents cache frequently accessed data (e.g., embeddings, states) in Redis, reducing load on Qdrant or MongoDB.
  - Redis can also cache query results from CockroachDB or Neo4j for faster access.

### Conclusion
Your current stack is strong, but adding a time-series database (TimescaleDB) and a key-value store (Redis) would make it more comprehensive for an agentic AI system, especially if you anticipate time-series workloads or performance optimization needs. Both can be integrated without violating your no-credit-card constraint if you self-host TimescaleDB and use Redis Cloud’s free tier. If these use cases don’t apply, your existing stack is sufficient.
