Real-Time Intelligence with IBM Time Series Models on Confluent

Foundation models transformed how enterprises unlock value from unstructured data. The bigger prize is streaming data, where the mission-critical decisions live: how much to order, which payment to stop, when the pump will fail, how hard to run the line, what happened the last time it looked like this. IBM and Confluent are now bringing that unlock stream-native, and the models are live in Early Access on Confluent Cloud, running where the data already moves, with Confluent Platform next.

Until now, those decisions have run on outdated economics: one bespoke model at a time and months of expert work on each. So teams model the few hundred series where the money is and cover the rest with safety margins, extra inventory, extra headroom, extra tolerance, acted on after the window has closed. That margin is the cost of a decision nobody could forecast, paid every cycle.

A time series foundation model (TSFM) changes that. Trained once across vast, varied signals, it generalizes to a series it has never seen: give it a window of measurements and it tells you what comes next, how far behaviour sits from normal, which history looks like this one, and which settings best serve a target. Using one does not take an army of data scientists either: a demand planner, a fraud analyst or a process engineer can put these models to work on their own streams. Around the models, IBM is building functions that shift the work left, so forecasting, anomaly detection, optimization and semantic intelligence arrive as capabilities you call rather than projects you build.

Picture one tempering line in a chocolate factory, its temperature, speed and throughput sampled every few seconds and watched against fixed thresholds. Drop a foundation model into that stream and it forecasts the line's output through the evening shift, so the planner sees a shortfall while there is still time to act. It scores today's run against how the line normally behaves on dark chocolate, so a slow drift surfaces before a bar blooms. It finds the closest match in plant history, so the engineer knows how the last runs like it turned out. It conditions on the settings the crew controls, and fine-tunes when the last points of accuracy are worth it. No data science team required, and the same model rolls to every line in every factory.

IBM ran these models before offering them, in its own products and operations first, then with design partners in cement, steel, pulp and paper, food and telecommunications. The numbers make the case: every point of accuracy is worth millions, productivity gains run 5 to 10×, and work that waited for specialists now sits with the domain experts who own the decision.

Now that proof meets real-time context: IBM brings frontier models that understand how signals behave, 44M+ downloads behind them, and Confluent brings the live state of the business and reach to every system that acts. Together they run stream-native, hosted in Confluent Cloud and called from Flink. Access opens on Confluent Cloud on AWS. Confluent Platform follows, bringing the same models and capabilities to on-premises and hybrid environments.

Time series intelligence meets real-time context with zero configuration, built-in governance and efficiency

The months usually spent wiring a model into production are months you keep: Granite reads the signal, Confluent supplies the context, the governance and the delivery to everything downstream.

A signal's value decays with time: a pump caught drifting today is a work order, the same pump next week is an outage.

Forecasting and detection are stateful: the next value only means something against recent history, and an anomaly only exists against a running sense of normal. Flink manages that state, keyed per series and fault tolerant, so each model gets the history it needs without a separate data store or a database hit per call.

This is where the value compounds. Confluent's data streaming platform puts business data in motion and makes it usable for ML. The platform continuously streams, connects, governs, and processes real-time data, capturing live business signals that IBM Granite Time Series models use for forecasting, anomaly detection, similarity search, classification, gap-filling and optimization. Confluent provides what you need to implement streaming use cases quickly, reliably, and securely, so you can focus on developing real-time ML applications rather than managing data infrastructure.

Confluent Cloud, the cloud deployment of Confluent's data streaming platform, provides native inference, which allows you to run IBM Granite Time Series models directly within Apache Flink® on Confluent, providing greater flexibility, security, and cost efficiency for real-time data processing while unifying data and ML workflows. The benefits include:

- Real-time intelligence where the data lives: Run forecasting and anomaly detection directly on streaming data, at the very moment business conditions change, without extracting time-series data into a separate ML platform or data warehouse.

- Zero configuration: Confluent manages model serving, infrastructure, scaling, and runtime operations, so there is no provider credential to manage or glue between data pipelines and the model. Call IBM Granite Time Series models directly from Flink SQL for real-time anomaly detection and forecasting.

- Fresh, enriched context: Confluent continuously captures and processes data into an up-to-date view of the current state of the business, from sensor telemetry and payment activity to application metrics, so models can act on what's happening now rather than stale batch data to make more reliable, accurate predictions. Inference results are written to Kafka topics and shared with fanout, consumable by alerting systems, dashboards, lakehouses and AI agents.

- Built-in governance and traceability: Inference pipelines adhere to the same schemas, lineage and access controls as everything else on the platform. Kafka topics are durable and replayable, which supports auditing, troubleshooting, model evaluation, and rerunning inference against historical data.

- Cost efficiency: Native inference eliminates the need to provision and manage dedicated model-serving infrastructure or GPUs, with zero cloud ingress or egress fees.

- Enhanced security: Data stays within Confluent Cloud for inference and adheres to RBAC and privacy policies throughout the platform.

- Faster time to value: Teams can move from streaming data to a working forecast and anomaly-detection pipeline in minutes using familiar SQL syntax, rather than building a separate ML stack or point-to-point data pipelines.

By bridging operational and analytical estates, Confluent helps teams turn live business events into actionable intelligence, bringing IBM Granite Time Series models into the stream. And because no single model serves a shampoo line, a card network and a retail catalogue alike, IBM and Confluent offer a portfolio rather than a model.

A complementary portfolio of time series foundation models, matched to the decision you are making

Every decision asks the future a different question. A planning cycle needs a range of outcomes, a trading desk the most accurate number from data at every rate, a fleet of a hundred thousand series a cost that stays rational, and a security team the moment a stream stops behaving like itself and what happened last time it did. The portfolio is four complementary time series foundation models, all in Early Access and called through Confluent's existing AI_FORECAST and AI_DETECT_ANOMALIES Flink SQL functions. Switch models with one SQL parameter, no pipeline redesign.

The whole project is one call:

SELECT AI_FORECAST( load_kw, event_time, JSON_OBJECT ( 'model' VALUE 'ttm' , 'horizon' VALUE 12 ) ) OVER ( ORDER BY event_time RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW ) AS forecast FROM meter_readings;