The results are in from our 2025 Apache Cassandra community survey, and the numbers tell a compelling story about the intersection of distributed databases and artificial intelligence. The data reveals that AI use cases leveraging Cassandra are projected to triple by 2026, fundamentally changing how we think about data infrastructure for AI applications.
The Survey Data
Over 2,500 Cassandra practitioners from around the world participated in our comprehensive survey, representing organizations from startups to Fortune 500 companies. The findings paint a clear picture of AI’s growing importance in the database landscape.
Key Findings
- 68% of respondents are already using or planning to use Cassandra for AI workloads
- AI use cases are expected to triple between now and 2026
- Vector search is the most requested AI-related feature (implemented in Cassandra 5.0)
- Real-time inference applications are driving the highest adoption rates
Why Cassandra for AI?
The surge in AI use cases isn’t happening in a vacuum. Several factors make Cassandra particularly well-suited for AI applications:
Scale Requirements
Modern AI applications generate and consume massive amounts of data. Large language models, recommendation engines, and real-time personalization systems require databases that can handle:
- Billions of records with sub-millisecond latency
- High write throughput for feature stores
- Global distribution for edge AI applications
Vector Search Integration
With Cassandra 5.0’s native vector search capabilities, organizations can now:
- Store embeddings alongside traditional data
- Perform similarity searches at scale
- Build RAG applications without additional infrastructure
- Maintain data consistency across hybrid workloads
Real-Time Performance
AI applications increasingly require real-time responses. Whether it’s fraud detection, recommendation engines, or conversational AI, the ability to serve fresh data with consistent low latency is crucial.
Dominant Use Cases Emerging
The survey revealed several AI use cases driving adoption:
1. Retrieval-Augmented Generation (RAG)
42% of AI-focused respondents are building RAG applications. These systems combine large language models with real-time data retrieval to provide more accurate, up-to-date responses.
-- Example: Storing documents and embeddings for RAG
CREATE TABLE knowledge_base (
doc_id UUID PRIMARY KEY,
content TEXT,
embedding VECTOR<FLOAT, 1536>,
metadata MAP<TEXT, TEXT>,
last_updated TIMESTAMP
);
2. Real-Time Recommendation Systems
38% of respondents are using Cassandra for recommendation engines that need to update in real-time based on user behavior and inventory changes.
3. Feature Stores for ML
31% are building feature stores that serve both training and inference pipelines, taking advantage of Cassandra’s ability to handle both analytical and operational workloads.
4. Conversational AI and Chatbots
29% are powering conversational AI applications that require fast access to conversation history, user preferences, and contextual information.
The Enterprise Shift
Perhaps most significantly, the survey reveals a shift in where AI applications are being deployed. Enterprise adoption is accelerating:
- 73% of Fortune 1000 respondents have AI initiatives using Cassandra
- Average deployment size for AI workloads is 2.3x larger than traditional applications
- Multi-region deployments are 4x more common for AI use cases
This enterprise adoption is driven by several factors:
Data Sovereignty and Compliance
Regulations like GDPR and industry-specific compliance requirements mean AI applications often need to keep data within specific regions or jurisdictions. Cassandra’s multi-datacenter capabilities make this straightforward.
Hybrid Cloud Requirements
Enterprises rarely deploy AI applications in a single cloud. Cassandra’s cloud-agnostic architecture enables seamless hybrid and multi-cloud AI deployments.
Integration with Existing Systems
Most enterprise AI applications need to integrate with existing data systems. Cassandra’s flexible data model and extensive ecosystem make this integration simpler.
Technology Evolution Driving Growth
Several technical developments are accelerating AI adoption:
Improved Vector Search Performance
Cassandra 5.0’s Storage Attached Indexes (SAI) provide efficient vector search that scales linearly with cluster size. This eliminates the need for separate vector databases in many architectures.
ACID Transactions for AI Workflows
The addition of ACID transactions in Cassandra 5.0 enables more complex AI workflows, particularly in financial services and other domains requiring strong consistency guarantees.
Better Integration with AI Frameworks
Libraries like CassIO make it easier to integrate Cassandra with popular AI frameworks like LangChain, reducing the complexity of building AI applications.
Looking Ahead: What This Means for 2026
The projected tripling of AI use cases has significant implications:
Infrastructure Planning
Organizations need to prepare for AI workloads that are fundamentally different from traditional applications:
- Higher memory requirements for vector operations
- More complex query patterns mixing traditional and vector searches
- Increased importance of global distribution for edge AI
Skills and Training
The convergence of AI and distributed databases requires new skills:
- Database teams need to understand vector operations and AI workflows
- AI teams need to understand distributed systems and consistency models
- Both need to collaborate more closely than ever
Tooling Evolution
We can expect rapid evolution in tooling and operational practices:
- Better monitoring for AI workloads
- Automated optimization for vector search performance
- Enhanced backup and recovery for AI model artifacts
The Bottom Line
The survey data confirms what many of us have suspected: AI isn’t just changing how we build applications - it’s changing the fundamental requirements for data infrastructure. The projected tripling of AI use cases by 2026 represents both an opportunity and a challenge.
Organizations that start preparing now - building expertise, scaling infrastructure, and adopting the right technologies - will be positioned to capitalize on this wave. Those that wait risk being left behind as AI becomes table stakes across industries.
Apache Cassandra’s evolution to support AI workloads natively positions it well for this future. With vector search, ACID transactions, and proven scalability, Cassandra is becoming the go-to choice for AI applications that need to operate at enterprise scale.
The future of data is AI-driven, and based on our survey results, that future is arriving faster than most organizations expected. The question isn’t whether your data infrastructure needs to support AI - it’s whether you’re ready for the scale and complexity that’s coming.
The full Apache Cassandra 2025 Community Survey results are available on the Apache Cassandra website. Special thanks to the 2,500+ community members who participated and shared their insights.