What is Turbo Mode?
Turbo Mode is a per-Robot Transloadit setting, controlled by the turbo parameter, that parallelizes supported work to return results sooner. It is opt-in for /video/encode and /video/concat, which can complete up to 22x faster, and on by default for /document/thumbs and /file/decompress.
How Turbo Mode works
Turbo Mode changes how supported Transloadit Robots schedule work by distributing processing that would otherwise proceed more serially and by emitting eligible results as they become available. The media Instructions and requested output remain the governing recipe; the acceleration comes from concurrency and earlier emission. Defaults differ per Robot: Turbo Mode is off for the video Robots and on for /document/thumbs and /file/decompress. Check the cost and latency trade-off for each Step rather than assuming one default. It belongs in latency-sensitive Assembly design where capacity use, result ordering, and downstream readiness are planned together.
Key facts
- 1The documented up-to-22x acceleration applies to
/video/encodeand/video/concat, whereturbodefaults tofalse; chunked encoding consumes extra Priority Job Slots, and thechunk_durationparameter tunes how many. - 2For
/document/thumbs,turbodefaults totrue: pages are extracted and resized in parallel and emitted individually. Pricing adds the input document’s file size for every extracted page, so controlling multi-page cost means settingturbo: falseexplicitly. - 3
/file/decompressalso defaults toturbo: true, emitting extracted files as soon as they are available at a billing surcharge, so downstream Steps must handle early results and cost-sensitive archive workflows should disable it.
When Turbo Mode matters
For the video Robots, enable Turbo Mode when lower completion latency justifies the extra Priority Job Slot usage. For /document/thumbs and /file/decompress it is already on and adds processing charges, so set turbo: false when cost matters more than speed.
Common use cases for platform workflows
These examples cover platform workflows broadly, not specifically Turbo Mode.
- Running repeatable upload, import, processing, AI, storage, and notification pipelines.
- Tracking long-running media work independently from an application request.
- Referencing centrally stored credentials by name instead of sending storage secrets with each request.
Working with platform workflows
This guidance covers platform workflows broadly, not just Turbo Mode.
A client authenticates and submits files or references together with workflow instructions. The platform validates the request, schedules dependent operations, records state transitions, and exposes results through a response, polling endpoint, or notification.
Platform concepts become reliable only when their lifecycle is explicit. Authentication, idempotency, retries, timeouts, observability, quotas, and terminal states should be designed together rather than added after failures occur.
What you gain
- Reusable workflows separate application intent from processing infrastructure.
- Stable job identifiers and lifecycle events improve observability and recovery.
- Managed queues and workers let products scale without embedding every media tool.
What it costs
- Synchronous responses are simple but keep connections open while long work executes.
- Aggressive retries improve recovery from transient faults but can duplicate work or overload a dependency.
- Higher concurrency reduces queue time until resource contention or a downstream limit becomes the bottleneck.
Before production
- 1Define authentication, authorization, idempotency, retries, and terminal error behavior.
- 2Observe queue time, execution time, callbacks, and partial results with stable identifiers.
- 3Exercise malformed, duplicate, interrupted, and unauthorized requests before launch.