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| author | Everton Seiei Arakaki <30481051+EvertonSA@users.noreply.github.com> | 2023-04-17 14:41:58 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-04-17 14:41:58 +0200 |
| commit | 22177b7aafcc36503c44642d450206e906d6a388 (patch) | |
| tree | 9e1d937b40142672ee8d5ac2bc9855410ced52ce /ADOPTERS.md | |
| parent | 20b67db0a9fb13bab72ac04255159fd5aff64431 (diff) | |
docs: add early adopters
Diffstat (limited to 'ADOPTERS.md')
| -rw-r--r-- | ADOPTERS.md | 7 |
1 files changed, 7 insertions, 0 deletions
diff --git a/ADOPTERS.md b/ADOPTERS.md new file mode 100644 index 0000000..63473e0 --- /dev/null +++ b/ADOPTERS.md @@ -0,0 +1,7 @@ +# KRR Adopters
+
+This is a list of adopters of Robusta KRR operator:
+
+Everton Arakaki - WAES Platform Consultant for Semiconductor Industry
+
+> I used Robusta KRR in my production clusters, and it took me less than 5 minutes to get very "robust" recommendations. Our applications and platform tooling were discovered automatically; our kubecontext was discovered automatically; and our kube-prometheus-stack was discovered automatically. It's just a matter of having Python and you are ready to use KRR. I'm eager to implement KRR recommendations in production, and try once again. I will come back here with more feedback.
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