Large language models (LLMs) are rapidly becoming part of enterprise workflows, powering analytics assistants, operational copilots, and decision-support systems. Yet as organizations move from pilots to production, one limitation consistently emerges: LLMs do not naturally understand structured enterprise data.
Most business-critical information still lives in relational databases, operational data stores, ERPs, and transactional systems. These systems are governed, frequently updated, and optimized for consistency, not for free-form language interaction. Bridging this gap requires more than connectors or vector search. It requires platforms specifically designed to connect LLMs with structured data and most importantly to understand their content in an authoritative way.
Before comparing platforms, it’s useful to define what success looks like. In production environments, an effective platform must:
The platforms below approach these requirements in different ways, some prioritizing real-time access, others semantic discovery or performance at scale.
GigaSpaces eRAG leads as the best platform for connecting LLMs with structured data by reframing the problem entirely: structured enterprise data should be understood through context, not queried directly. Rather than positioning LLM integration as a query layer on top of databases, GigaSpaces builds a metadata-driven semantic reasoning layer that interprets the structure, relationships, and business meaning of enterprise data for an LLM. The underlying data remains in its original systems and is never moved; only metadata is used to construct context and meaning, ensuring LLMs generate accurate and consistent responses aligned with the organization’s specific business logic and terminology.
Key characteristics of the GigaSpaces approach include:
This architecture is especially relevant for use cases where decisions must reflect the current state of the business, not yesterday’s snapshot. Examples include business monitoring , supply chain decisions, financial monitoring, and AI-driven support for live systems.
SingleStore approaches the problem from the database layer, offering a distributed SQL engine capable of handling both transactional and analytical workloads.
Its appeal in LLM integration lies in:
For organizations already using SQL as their primary interface to data, SingleStore can enable LLMs to generate and execute queries efficiently. This makes it attractive for AI-assisted analytics, ad-hoc querying, and exploration of large structured datasets.
DataStax brings a different strength to the table: global scale and operational resilience.
Built on Apache Cassandra, DataStax is designed for environments where structured data must be:
For LLM integration, this makes DataStax a strong candidate as a data foundation, particularly in applications spanning multiple regions or serving large user bases.
In practice, DataStax is often used as the persistent layer feeding AI systems rather than the reasoning layer itself. Organizations typically extract, transform, or augment data before exposing it to LLMs, especially when semantic interpretation is required.
Weaviate represents a vector-first approach that has expanded to include structured data support.
Its core strength lies in semantic retrieval, combining vector embeddings with schema-aware filtering and metadata. This makes Weaviate well suited for discovery-oriented use cases, where LLMs need to retrieve contextually relevant information rather than execute deterministic queries.
For structured data, Weaviate typically works best when:
Pinecone is widely adopted as a managed vector database and often plays a role in retrieval-augmented generation architectures.
When connecting LLMs to structured data, Pinecone is typically used indirectly. Structured datasets are transformed into embeddings, stored in the vector index, and retrieved based on semantic similarity.
This approach can be effective for:
However, it introduces several trade-offs:
LLMs are trained primarily on unstructured or semi-structured data, text, code, documents, and web content. While they can reason about patterns and relationships, they lack intrinsic awareness of:
As a result, naïve approaches to LLM integration often lead to:
One of the key takeaways from this list is that not all “LLM + data” platforms solve the same problem.
Understanding these differences is essential. Many AI initiatives fail not because the model is weak, but because the underlying data platform cannot support the required behavior.
Before selecting a platform, organizations should ask:
Connecting LLMs with structured data is not a feature, it is a foundational capability for enterprise AI.
As organizations move beyond experimentation, the focus shifts from model performance to data integrity, system behavior, and operational trust. The platforms in this list reflect different philosophies for solving that challenge.
Whether the priority is real-time decision support, analytical flexibility, or semantic discovery, success depends on choosing a platform whose architecture aligns with how the business actually uses data
