When would you use PLINQ instead of Parallel.ForEach, and when should you use neither?

PLINQ vs Parallel.ForEach for CPU-bound work

Chooses a parallel API based on whether the workload is a data query or an imperative loop and keeps CPU work pure, bounded, and measurable.

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I would use PLINQ for an in-memory data transformation or aggregation that is naturally expressed as a query and whose elements can be processed independently. I would use Parallel.ForEach for an imperative CPU-bound loop where I need explicit control around each iteration. I would use neither for ordinary asynchronous I/O; Task.WhenAll, bounded async concurrency, or a channel is usually a better fit there. I would also keep the sequential version when the collection or per-item work is too small to repay parallel overhead.

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CPU-bound parallelism, declarative transformations, side effects, ordering, partitioning overhead, cancellation, and sequential baselines.

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Parallel APIs differ in expression and control, but both spend thread-pool workers and add partitioning and merge overhead. A good choice starts by proving the work is independent and CPU-bound.
I would use PLINQ for an in-memory data transformation or aggregation that is naturally expressed as a query and whose elements can be processed independently. I would use Parallel.ForEach for an imperative CPU-bound loop where I need explicit control around each iteration. I would use neither for ordinary asynchronous I/O; Task.WhenAll, bounded async concurrency, or a channel is usually a better fit there. I would also keep the sequential version when the collection or per-item work is too small to repay parallel overhead.
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Using PLINQ for database or network calls because the API name contains parallel.
Writing to a normal List<T> from parallel iterations.
Calling AsOrdered without checking whether output order is required.
Skipping the sequential baseline and assuming a large collection guarantees speedup.

When would you use PLINQ instead of Parallel.ForEach, and when should you use neither?

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