Do streaming and batch cost different token prices?

No. The token price for a given model is the same whether you stream the response or not. The cost difference is infrastructure — streaming holds a connection open for the full response, which ties up server capacity per concurrent request.

Why does streaming need more servers?

A streamed response keeps a connection and request slot occupied for its entire duration. At high request rates with slow responses, concurrent connections pile up, so you need enough server capacity to hold them all open simultaneously.

When is batch cheaper on infrastructure?

When work is asynchronous and can be queued. Batch processing decouples arrival rate from processing capacity, so you size for throughput rather than peak concurrency, which usually needs far fewer always-on servers.

Is this calculator exact?

No. It uses Little's Law to size concurrency and a simple per-server capacity model. Real systems add load balancers, autoscaling, and overhead, so treat the output as a planning estimate.

Streaming vs Batch Mode Cost Comparison

Streaming vs batch mode cost comparison

A common misconception is that streaming a response costs more in tokens. It does not — the token price is identical. What differs is infrastructure: streaming holds a connection open for the entire response, so at scale you must provision enough capacity to hold every in-flight stream at once. This tool models that difference so you can decide where streaming is worth the server bill.

How it works

Using Little’s Law, the number of concurrent connections you must hold equals your requests per second multiplied by the average response time. Dividing by how many streams a single server can hold gives the server count for streaming, and multiplying by your hourly server cost projects the monthly bill. Batch processing instead sizes for throughput: it queues requests and processes them with far fewer always-on workers, which the tool models as a fraction of the streaming fleet.

Tips and notes

Token cost is unchanged. Choose streaming for user-facing latency, not to save money — the savings, if any, are on the infrastructure side.
Slow responses are the killer. Concurrency scales with response time, so a 10-second average at high RPS demands a large fleet of held connections.
Batch for offline work. Anything a user is not waiting on belongs in a queue, where you size for throughput and run far fewer servers.
Planning estimate only. Add headroom for autoscaling, retries, and load-balancer overhead before sizing production capacity.