I’ve been in a few recent conversations about whether to use Apache Beam on its own or run it with Google Dataflow. On the surface, it’s a tooling decision. But it also reflects a broader conversation about how teams build systems.
Beam offers a consistent programming model for unifying batch and streaming logic. It doesn’t dictate where that logic runs. You can deploy pipelines on Flink or Spark, or you can use a managed runner like Dataflow. Each option outfits the same Beam code with very different execution semantics.
What’s added urgency to this choice is the growing pressure on data systems to support machine learning and AI workloads. It’s no longer enough to transform, validate, and load. Teams also need to feed real-time inference, scale feature processing, and orchestrate retraining workflows as part of pipeline development. Beam and Dataflow are both increasingly positioned as infrastructure that supports not just analytics but active AI.
Choosing one path over the other means making decisions about flexibility, integration surface, runtime ownership, and operational scale. None of those are easy knobs to adjust after the fact.
The goal here is to unpack the trade-offs and help teams make deliberate calls about what kind of infrastructure they’ll want.
Apache Beam: A Common Language for Pipelines
Apache Beam provides a shared model for expressing data processing workflows. This includes the kinds of batch and streaming tasks most data teams are already familiar with, but it also now includes a growing set of patterns specific to AI and ML.
Developers write Beam pipelines using a single SDK that defines what the pipeline does, not how the underlying engine runs it. That logic can include parsing logs, transforming records, joining events across time windows, and applying trained models to incoming data using built-in inference transforms.
Support for AI-specific workflow steps is improving. Beam now offers the RunInference API, along with MLTransform utilities, to help deploy models trained in frameworks like TensorFlow, PyTorch, and scikit-learn into Beam pipelines. These can be used in batch workflows for bulk scoring or in low-latency streaming pipelines where inference is applied to live events.
Crucially, this isn’t tied to one cloud. Beam lets you define the transformation once and pick the execution path later. You can run the exact same pipeline on Flink, Spark, or Dataflow. That level of portability doesn’t remove infrastructure concerns on its own, but it does allow you to focus your engineering effort on logic rather than rewrites.
Beam gives you a way to describe and maintain machine learning pipelines. What’s left is deciding how you want to operate them.
Running Beam: Self-Managed Versus Managed
If you’re running Beam on Flink, Spark, or some custom runner, you’re responsible for the full runtime environment. You handle provisioning, scaling, fault tolerance, tuning, and observability. Beam becomes another user of your platform. That degree of control can be useful, especially if model inference is only one part of a larger pipeline that already runs in your infrastructure. Custom logic, proprietary connectors, or non-standard state handling might push you toward keeping everything self-managed.
But building for inference at scale, especially in streaming, introduces friction. It means tracking model versions across pipeline jobs. It means watching watermarks and tuning triggers so inference happens precisely when it should. It means managing restart logic and making sure models fail gracefully when cloud resources or updatable weights are unavailable. If your team is already running distributed systems, that may be fine. But it isn’t free.
Running Beam on Dataflow simplifies much of this by taking infrastructure management out of your hands. You still build your pipeline the same way. But once deployed to Dataflow, scaling and resource provisioning are handled by the platform. Dataflow pipelines can stream through inference using native Beam transforms and benefit from newer features like automatic model refresh and tight integration with Google Cloud services.
This is particularly relevant when working with Vertex AI, which allows hosted model deployment, feature store lookups, and GPU-accelerated inference to plug straight into your pipeline. Dataflow enables those connections with lower latency and minimal manual setup. For some teams, that makes it the better fit by default.
Of course, not every ML workload needs end-to-end cloud integration. And not every team wants to give up control of their pipeline execution. That’s why understanding what each option provides is necessary before making long-term infrastructure bets.
Choosing the Execution Model That Matches Your Team
Beam gives you the foundation for defining ML-aware data pipelines. Dataflow gives you a specific way to execute them, especially in production environments where responsiveness and scalability matter.
If you’re building systems that require operational control and that already assume deep platform ownership, managing your own Beam runner makes sense. It gives flexibility where rules are looser and lets teams hook directly into their own tools and systems.
If instead you need fast iteration with minimal overhead, or you’re running real-time inference against cloud-hosted models, then Dataflow offers clear benefits. You onboard your pipeline without worrying about the runtime layer and deliver predictions without gluing together your own serving infrastructure.
If inference becomes an everyday part of your pipeline logic, the balance between operational effort and platform constraints starts to shift. The best execution model depends on more than feature comparison.
A well-chosen execution model involves commitment to how your team builds, evolves, and operates intelligent data systems over time. Whether you prioritize fine-grained control or accelerated delivery, both Beam and Dataflow offer robust paths forward. The key is aligning that choice with your long-term goals: consistency across workloads, adaptability for future AI demands, and a developer experience that supports innovation without compromising stability. As inference becomes a core part of modern pipelines, choosing the right abstraction sets a foundation for future-proofing your data infrastructure.
