Batch vs real-time inference: the cost of immediacy

Quick answer

Real-time inference keeps serving capacity always ready to respond to each request instantly, so you pay for that capacity's uptime. Batch inference processes many predictions together on compute spun up for the job and torn down after (ideal for Spot), so you pay only for the bulk run. When predictions do not need to be immediate, batch is far cheaper because it avoids always-on serving capacity and uses cheap transient compute. Use real-time only when immediacy is required.

Not every prediction needs to be made the instant it is requested. Real-time inference keeps capacity always ready to respond immediately; batch inference processes predictions in bulk on transient compute. If the use case can tolerate predictions being computed ahead of time or in scheduled batches, batch is dramatically cheaper.

The two modes

	Real-time	Batch
Capacity	Always-on, ready to respond	Transient, spun up per job
Cost	Serving capacity uptime	Only the bulk run (Spot-friendly)
Latency	Immediate	Delayed (scheduled or on-demand bulk)
Best for	Interactive, per-request predictions	Bulk, scheduled, precomputable predictions

Real-time serving pays for capacity kept running to answer requests instantly, even during quiet periods. Batch inference runs predictions for many inputs at once on compute created for the job and torn down after, which can run on cheap Spot capacity and costs nothing between runs. So batch avoids the always-on cost that real-time requires.

When batch wins

Batch is far cheaper whenever predictions do not need to be immediate: scoring a whole dataset nightly, precomputing recommendations, generating reports, enriching records in bulk, or any workflow where results can be ready ahead of when they are used. Precomputing predictions in batch and serving them from a cache or database can replace real-time inference entirely for many use cases, at a fraction of the cost.

Choosing the mode for cost

Use real-time inference only when a prediction genuinely must be made the instant it is requested (interactive applications, live personalization, fraud checks at transaction time). For everything else, prefer batch: process predictions in bulk on transient, Spot-friendly compute, and where possible precompute results so real-time serving is not needed at all. The immediacy of real-time has a real always-on cost, so pay for it only when the use case requires it, the same defer-when-you-can logic as async processing.

FAQ

Is batch or real-time inference cheaper?

Batch is far cheaper when predictions do not need to be immediate. Real-time inference keeps serving capacity always on to respond instantly, paying for uptime even in quiet periods. Batch processes predictions in bulk on transient compute (ideal for Spot) that costs nothing between runs. Use real-time only when immediacy is genuinely required.

When should I use batch inference?

Whenever predictions do not need to be immediate: scoring a whole dataset nightly, precomputing recommendations, generating reports, enriching records in bulk, or any workflow where results can be ready ahead of when they are used. Batch runs on cheap transient compute and costs nothing between runs, far cheaper than always-on real-time serving.

How does batch inference save money?

It avoids always-on serving capacity. Real-time inference pays for capacity kept running to answer requests instantly, even when idle. Batch inference spins up compute for the bulk job and tears it down after, running on cheap Spot capacity and costing nothing between runs. So batch replaces continuous serving cost with occasional bulk-run cost.

Can I replace real-time inference with batch?

Often yes, by precomputing. For many use cases, computing predictions in batch ahead of time and serving them from a cache or database replaces real-time inference entirely, at a fraction of the cost. This works whenever the predictions can be computed before they are needed, such as recommendations or scores that do not depend on instant-of-request inputs.

When do I actually need real-time inference?

When a prediction genuinely must be made the instant it is requested: interactive applications, live personalization, and fraud checks at transaction time. There the immediacy is essential and worth the always-on serving cost. For everything else, batch or precomputed predictions are cheaper, so reserve real-time for use cases that truly require immediacy.

Does C3X estimate batch and real-time inference cost?

C3X prices both the always-on serving infrastructure for real-time and the transient compute for batch jobs from your Terraform, so you can compare the two modes before deploy. Actual cost depends on request load for real-time and job frequency for batch, which you model as usage assumptions.

What to do next

Compare batch and real-time inference costs before you build. C3X reads your Terraform and prices your resources against a live catalog. Start with the quickstart.