"Convert this PDF" is an underspecified request until someone answers convert it to what, and the three most common answers, image, DOCX, and flattened PDF, aren't interchangeable. Picking the wrong one doesn't usually fail loudly. It just produces an output that's technically correct and practically useless for whatever the next step in the pipeline actually needed.
Image output: when the PDF's content doesn't matter, only its appearance
Rendering a PDF page to PNG or JPEG throws away every notion of text, structure, and searchability, and keeps only exact visual appearance. That's a bad trade for most conversion use cases and exactly the right one for a specific few: thumbnail previews in a document list, a quick visual diff between two versions of a file, or embedding a snapshot of a page somewhere that can't render PDFs natively. If nothing downstream ever needs to search, copy, or edit the content, image output is the cheapest and most reliable option, because there's no layout-fidelity tradeoff to worry about, the image is by definition a pixel-accurate copy.
result = pdf_api.run({"action": "convert", "file": file, "to": "png", "pages": "1"})
The failure mode with image output is almost always someone reaching for it out of habit and then getting stuck later needing to search or edit content that got flattened into pixels with no way back.
DOCX output: when someone needs to actually edit the content
Converting to DOCX aims at the opposite goal: preserving editable text and structure, at the cost of exact visual fidelity. A PDF built from a complex multi-column layout, custom fonts, or precise absolute positioning will come out of a PDF-to-DOCX conversion looking approximately right, not pixel-identical, because DOCX's layout model doesn't work the same way a PDF's does. For a contract that needs redlining, a report someone needs to update the numbers in, or any document whose whole point is further editing, that tradeoff is obviously worth it. For a signed, finalized document nobody should be able to modify, it's the wrong tool entirely.
result = pdf_api.run({"action": "convert", "file": file, "to": "docx"})
The most common mistake here is treating DOCX conversion as a universal "make this PDF editable" button and being surprised when a heavily designed PDF, a marketing one-pager with precise graphic layout, say, comes back looking noticeably different from the source once it's been reflowed into a Word document's layout model.
Flattened PDF output: when you need a PDF that stays a PDF
Flattening isn't a format change at all, it's a PDF that goes in and a PDF that comes out, but with form fields, layers, and annotations merged permanently into the page content so nothing about it can be edited or filled in afterward. This is the right output for anything that needs to look and behave exactly like the source but can no longer be modified: a signed contract, an approved invoice, a filled-out form that's now final. Where image output sacrifices content and DOCX sacrifices exact layout, flattening sacrifices nothing about appearance and instead removes editability on purpose, which is the entire point.
result = pdf_api.run({"action": "flatten", "file": file})
Matching the format to what happens next, not what's easiest to generate
The recurring mistake across all three isn't picking a bad format in isolation, it's picking a format based on what's simplest to generate right now instead of what the next step in the pipeline actually consumes. A document management system that stores flattened PDFs for the archive but needs image thumbnails for the browse view needs both outputs from the same source file, not one format doing double duty badly. Building the conversion step around "what does the consumer need" rather than "what's the default" avoids having to redo the conversion later once the real requirement surfaces.
Combining formats instead of picking just one
Nothing requires committing to a single output per document. A common pattern is generating an image for the list view thumbnail, keeping a flattened PDF as the canonical archived copy, and offering DOCX conversion only on demand when someone explicitly requests an editable copy. Three conversion calls against the same source file, each serving a different downstream need, cost more in API calls than picking one format and hoping it covers every case, but far less than discovering midway through a project that the format chosen up front doesn't support a feature that turned out to matter.
Getting the source quality right before converting at all
None of these three outputs can recover information that wasn't legible in the source PDF to begin with. A scanned document with no underlying text layer converts to DOCX with garbled or missing text no matter how good the conversion engine is, because there was no text to preserve in the first place, only pixels. Checking whether a PDF has real text content before promising a clean DOCX conversion saves the awkward conversation of explaining afterward why the "editable" version isn't actually editable.
Conversion failures don't look the same across formats
A failed image conversion is usually obvious immediately, a blank or corrupted file that nobody would mistake for success. A failed or degraded DOCX conversion is often much quieter: the file opens fine, looks mostly right, and the missing table or misplaced paragraph only surfaces when someone actually reads it closely later. That difference matters for how much manual review each format needs before an output gets treated as final. Image and flattened PDF outputs can generally be trusted on a status-code check alone, since there isn't much room for a subtly wrong success. DOCX output benefits from at least a spot check on complex source documents, tables, multi-column layouts, embedded images, since "the conversion succeeded" and "the conversion is faithful" aren't quite the same claim for that format the way they are for the other two.
One API, three outputs, one decision to make correctly
None of the three formats is a default anyone should reach for without first checking what happens to the output next. All three are handled by the same multi-format PDF conversion API alongside merge, split, compress, rotate, and watermark, priced per successful result, which means the cost of picking the right format for each specific use case rather than one format for everything is a design decision, not an infrastructure one.
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