In enterprise environments, deployment packages can naturally grow over time.
Teams may work with large application archives, generated bundles, folder-based packages, or artifacts with many nested files.
With Digital.ai Deploy 26.1, large artifact handling has been improved.
Deploy can now handle artifacts of 2 GB or larger more efficiently by using a streaming approach instead of loading the full artifact into memory.
Why does this matter?
Because deployment reliability is not only about the deployment logic itself. Sometimes, a deployment can be correctly configured but still run into problems because the package is large and difficult to process efficiently.
This improvement helps reduce memory pressure and makes large artifact deployments more resilient, especially for teams working with bigger packages or running multiple deployments in parallel.
This may be especially relevant if your teams deploy:
large application archives
folder-based deployment packages
generated enterprise bundles
packages with many nested files
multiple large artifacts in parallel
How do you currently manage large deployment artifacts?
Do you split packages, tune memory settings, use external storage, or rely on the deployment platform to handle large artifacts automatically?
For technical details, check the Deploy 26.1 documentation.