Estuary provides a unified platform for CDC, streaming, batch data movement, and transformation.
These events may eventually update a database, but the event itself can carry useful timing and context that is lost if the AI waits for a later table update.
Materialize lets developers define those business objects in SQL and keeps the results incrementally updated as the underlying data changes. Instead of rerunning an expensive query from scratch, the platform updates only the affected parts of the computation when new data arrives.
A CDC pipeline can keep a warehouse, database, feature environment, or other downstream system synchronized without repeatedly scanning entire tables.
Real-Time Data Platforms for AI at a Glance
| Platform | Real-Time Approach | Strong AI Use Case |
|---|---|---|
| Artie | Managed CDC and event ingestion | Keeping warehouses, databases, and AI data stores continuously synchronized with production systems |
| Confluent | Kafka-based event streaming and Flink processing | Event-driven AI, streaming agents, and contextual inference |
| Materialize | Incrementally maintained streaming SQL views | Live business context and continuously updated data products for agents |
| Redpanda | High-performance streaming, SQL, and agent data infrastructure | AI agents operating on event streams and live enterprise systems |
| Estuary | CDC, streaming, batch, and transformations | Unified AI data movement across operational, analytical, and vector destinations |
| Airbyte | Replication plus an agent context layer | Connecting agents to fresh SaaS, database, and operational context |
6 Leading Real-Time Data Platforms for Powering AI Applications
1. Artie
Its platform is built on Apache Kafka and Apache Flink, allowing organizations to ingest continuous streams of business events, process and enrich them in motion, and distribute them to applications, models, databases, vector stores, and other systems.
Examples include detecting fraud as transactions occur, updating recommendations based on current user behavior, processing IoT telemetry, reacting to logistics events, and triggering an AI workflow when something meaningful happens in another enterprise system.
The table below summarizes the six platforms covered in this guide, how each one delivers fresh data, and the kind of AI workload it suits best. Use it to build a shortlist. Start with where your source of truth lives and how fresh your data actually needs to be. The detailed sections that follow explain the trade-offs.
A model can be extremely capable and still produce the wrong result because its view of the business is several hours old. A support agent may tell a customer that an already-resolved ticket is still open. A recommendation system may promote an item that has just sold out. A fraud model may receive a transaction only after the money has moved. An autonomous agent may make a reasonable decision based on an account balance that is no longer accurate.
The three models are complementary. A mature AI architecture may use CDC to capture operational state, events to represent behavior, and incremental computation to turn both into current business context.
Change data capture reads database transaction logs and turns inserts, updates, and deletes into a continuous stream of changes.
2. Confluent
Start with three questions. Does it connect to the systems where your data lives? Can it meet the freshness your workload needs? How does it handle schema changes and failures without losing or duplicating data? After that, compare security controls, deployment options, and how much of it your team will have to operate.
Artie is a fully managed, real-time data replication platform built to keep analytical and operational destinations continuously synchronized with production databases.
For AI teams, the important result is not simply faster ETL. It is a continuously updated representation of operational state.
These technologies are often grouped together under “real-time data,” but they solve different architectural problems.
3. Materialize
An AI application may depend on customer records in Postgres, account information in MySQL, or product state spread across several production databases. If that information reaches the warehouse only through scheduled batch loads, features, embeddings, retrieval datasets, and agent context can all go stale between jobs.
Artie also supports column-level controls for excluding, encrypting, or hashing sensitive data in flight, and offers a bring-your-own-cloud deployment option for organizations that need data to stay within their own cloud environment.
Redpanda combines high-performance data streaming with a rapidly expanding set of infrastructure designed specifically for production AI agents.
4. Redpanda
Incrementally maintained SQL views can continuously combine raw streams and database changes into these higher-level objects.
Estuary combines these patterns in one platform rather than requiring separate tools for real-time and traditional data integration. Its CDC capabilities capture changes from operational databases, streaming pipelines can deliver data with sub-100 ms latency, and batch or scheduled materializations can be used where lower freshness requirements make continuous updates unnecessary.
These platforms do not all solve the problem the same way. Some copy database changes as they happen (change data capture, or CDC). Some carry streams of events, such as payments, clicks, or shipments, to the systems that need to react to them. Others keep live SQL views up to date so an agent can ask a business question and get a current answer. Several now combine more than one of these approaches.
5. Estuary
It is particularly effective when production databases already hold the authoritative state the AI application needs.
The fix is not a better model. It is a better data pipeline. Real-time data platforms move changes out of production databases, event streams, and business applications quickly enough that an AI system works from what is true now, not from what was true at the last nightly batch job.
CDC tells you a record changed, for example that an order’s status went from “processing” to “shipped.” Event streaming gives you the event itself, such as the shipment scan along with its time and location. Many AI systems use both: CDC for current state, and events for what just happened.
Confluent approaches real-time AI from the event-streaming side of the architecture.
6. Airbyte
No. A fraud model or an agent that takes actions needs data that is seconds old. A forecasting model or a mostly static knowledge base can run on hourly or daily updates. Spend on real-time infrastructure only where stale data changes the outcome.
Typical AI examples include keeping customer profiles, account state, orders, subscriptions, product data, and application records current.
Real-Time Is Not One Speed
This gives Redpanda a distinctive position for applications where real-time data and agent execution are converging into a single architecture. It is a good fit for organizations building event-heavy AI systems, or agent architectures in which governance and live data access need to work together.
Its traditional Data Replication product supports hundreds of sources and destinations and can use CDC for incremental database replication. The platform can run as managed cloud infrastructure or be self-hosted, with a large connector ecosystem covering databases, SaaS applications, warehouses, data lakes, and other systems.
| AI Workload | Typical Freshness Need | Why |
|---|---|---|
| Fraud detection | Milliseconds to seconds | A decision made after the transaction may be useless. |
| Dynamic pricing | Seconds | Inventory, demand, and competitor signals can change rapidly. |
| Customer support agent | Seconds to a few minutes | Order, account, and ticket state should reflect recent actions. |
| Recommendation engine | Seconds to minutes | Recent behavior can materially affect relevance. |
| Operational AI agent | Seconds | The agent may take actions against current business state. |
| RAG over changing business records | Seconds to minutes | Stale documents or records can produce confidently outdated answers. |
| Model training | Minutes to hours | Continuous freshness may help, but millisecond latency is rarely necessary. |
| Long-term forecasting | Hours to daily | Historical completeness usually matters more than instant arrival. |
CDC, Event Streaming, and Live SQL Solve Different AI Problems
Airbyte has expanded from its original focus on open-source data integration into infrastructure designed specifically to connect AI agents with enterprise data.
CDC Answers: What Changed in the Database?
Its core approach uses change data capture (CDC) to read database changes and replicate inserts, updates, deletes, and schema changes into destinations such as Snowflake, BigQuery, Redshift, and other databases. Artie targets sub-minute latency while handling much of the infrastructure and merge logic that teams would otherwise need to build themselves around systems such as Kafka and Debezium.
Its Data Platform provides Kafka-compatible real-time streaming, along with connectors and increasingly integrated SQL capabilities. The company has built on that foundation with an Agentic Data Plane intended to give AI agents governed access to live streams, operational systems, historical data, APIs, and enterprise tools.
An AI application often needs something more useful than raw database changes or individual events. It needs the current answer to a business question:
Artie replicates those source changes continuously instead. Its Events API also lets teams ingest event data alongside CDC, extending the platform beyond database replication. Automatic schema evolution handles source changes without manual pipeline rebuilds, and exactly-once delivery is designed to prevent duplicate or lost records during failures and restarts.
Event Streaming Answers: What Just Happened?
Event streaming focuses on things that occur, rather than only on the resulting database state.
The clearest trend across these platforms is that data movement and AI agents are converging. Vendors that started in replication, streaming, or streaming SQL now describe their products as context layers or data planes for agents. That means AI systems will increasingly read live business state directly instead of waiting for a warehouse to catch up. Expect governance to grow in importance alongside this shift. Once an agent can act on current data, controlling which records it can see and change matters as much as latency. Expect the line between CDC, event streaming, and live SQL to keep blurring as well, with more platforms offering two or all three in one product. Not every workload will move to millisecond freshness. The practical direction is “right-time” data: paying for speed where stale information leads to wrong decisions, and using cheaper batch updates everywhere else. Teams that map their AI use cases to real freshness requirements now will be better placed to choose a platform, and less likely to overbuild one.
The company’s “right-time” approach is based on the idea that not every AI workload needs the same latency. Some data should move in milliseconds, while other datasets are perfectly useful with scheduled or micro-batch updates.
One of the easiest mistakes in an AI infrastructure project is describing every workload as “real time.” That can lead to unnecessary complexity and infrastructure cost.
Live SQL Answers: What Does the Business Look Like Right Now?
Materialize focuses on a different problem: continuously maintaining the derived business context that applications and AI agents need to query. Operational data is rarely useful in its raw form.
- How much available inventory does this customer have access to?
- Which accounts are at risk right now?
- Which orders are delayed, given the latest logistics events?
An AI agent answering a customer question may need to combine account information, orders, payments, support history, entitlement status, and current product usage. Querying each of those systems independently at runtime adds latency and complexity.
This flexibility is useful in AI architectures because the data feeding a model rarely has a single, uniform freshness requirement.
Where Real-Time Data for AI Is Heading
Event streams are therefore particularly useful for reactive AI.
FAQs
Does every AI application need streaming data?
Different AI decisions tolerate very different levels of data staleness.
What is the difference between CDC and event streaming?
By Glenn Blake
What should we look for when choosing a platform?
This makes Confluent especially relevant when AI needs to react to events rather than simply query the latest replicated state.
A page view happened. A payment was initiated. A sensor crossed a threshold. A shipment moved. A user clicked an offer.
