How Hybrid Storage and Queries Power Real-Time AI
“Fast” isn’t enough. In today’s enterprise environment, real-time AI doesn’t just mean low-latency—it means delivering the right answer based on what’s happening right now. That includes knowing what matters, who’s involved, and how one event might trigger another across your systems.
This level of intelligence requires data that’s not only fresh but also rich in structure and meaning. It’s not enough to query a flat table or rank based on similarity. You need the ability to explore relationships, infer intent, and react to change—all in the same moment. Real-time AI needs real context.
That’s where hybrid storage and querying make the difference. TigerGraph is built to support both structured graph logic and fast vector similarity search, so your AI can adapt in real time, reason across relationships, and surface answers you can trust.
The Problem with Single-Mode Systems
Most systems force you to choose between deep, explainable structure with graph databases or fast matching with vector search. But real-world AI isn’t binary and you need both.
Enterprise workloads involve live signals, changing relationships, and overlapping behaviors. Whether you’re trying to detect fraud, personalize recommendations, or route logistics in real time, the complexity of these systems defies simple modeling. For example:
- Anomaly detection: Vectors can flag an event as statistically unusual. But only graph can tell you why by exposing who or what that entity is connected to, and whether it mirrors known behavioral patterns.
- Customer intelligence: You may know which users are similar, but can you tell who they influence, or who influences them? Graph shows the hidden communities, peer networks, and pathways that define behavior.
- Real-time decision making: In use cases like dynamic pricing or supply chain rerouting, understanding cascading effects across systems is essential. Graph captures these dependencies. Vectors help identify relevant comparables fast.
Flattening this complexity into static queries—or relying solely on similarity scores—misses the big picture. Speed without understanding leads to brittle systems that fail when the unexpected happens.
TigerGraph’s Hybrid Engine
TigerGraph removes the tradeoff. It brings together the best of both worlds in a single engine designed for real-time, contextual prediction. TigerGraph supports:
- Graph-native queries to uncover how things are connected, influenced, or impacted—not just through direct links, but across multi-hop relationships that reflect real-world complexity.
- Vector-linked search to instantly surface semantically similar items based on high-dimensional embeddings. These embeddings, generated by LLMs or other AI models, capture things like user behavior, sentiment, or risk level. TigerGraph allows you to retrieve these similar entities in context, bridging intent and influence.
- Massively parallel processing to enable real-time responsiveness, even as your data grows in size and complexity. TigerGraph’s distributed, high-concurrency architecture is designed to handle deep graph traversal and hybrid search operations at scale, without dropping performance.
What makes TigerGraph different: vector search isn’t bolted on—it’s integrated directly into TigerGraph’s native GSQL query language as a callable function. That means you don’t have to switch engines or orchestrate separate pipelines to blend similarity and structure. You write one query, and TigerGraph handles both the semantic matching and the multi-hop reasoning.
This functionality isn’t just technically elegant—it’s practical. Developers can build, test, and deploy hybrid queries without context switching or maintaining synchronization between disparate systems. Data teams can iterate faster, and decision-makers can trust that what they’re seeing is grounded in both statistical similarity and structural reality. You can:
- Execute semantic similarity search inside a graph-native GSQL query
- Combine scoring with multi-hop traversal and filter logic
- Incorporate embeddings from LLMs or external AI models without compromising performance or transparency
Standalone vector systems are great at saying “this looks like that.” TigerGraph goes further and answers: “How are they connected? What patterns do they share? Why does it matter?”
That’s what we mean by enabling better predictions—not by replacing your ML stack, but by making it smarter, more contextual, and easier to trust.
Real-Time AI in Action
TigerGraph’s architecture is designed for speed and intelligence:
- Streaming ingestion: In many systems, incoming data must be pre-processed, batched, or synced before it’s available for analysis. TigerGraph supports native streaming ingestion, meaning data can be ingested, indexed, and queried in near real time.
- Multi-hop traversal: Explore indirect relationships across massive networks in milliseconds, uncovering dependencies that flat systems miss.
- Vector similarity search: Retrieve the most semantically similar items to a given input using high-dimensional embeddings. TigerGraph lets you specify how many matches to return, like the 10 most similar, and use that result set within a broader graph query.
This hybrid capability can power real-world applications in fraud detection, logistics optimization, real-time recommendations, and dynamic customer engagement. Enterprises can use TigerGraph to unify insight and speed, enabling systems that react quickly and think critically.
Why Hybrid Wins
Speed is table stakes. What sets real-time AI apart is its ability to reason, adapt, and explain.
With TigerGraph’s hybrid search, you’re not just querying data—you’re connecting the dots. You’re surfacing hidden signals, contextualizing behavior, and delivering timely answers that business teams can trust and act on.
That’s what today’s enterprises need: AI that understands the moment and the network behind it.
Ready to go beyond fast and start thinking smart? Try fully managed TigerGraph with native graph + vector search today.
Explore Savanna for free at https://tgcloud.io
