u/Immediate-Screen7893

I’ve been experimenting with a few AI tools for coding recently and wanted to get a sense of how others are using them in practice.

The ones I’ve tried so far include:

• GitHub Copilot
• Grok
• ChatGPT (OpenAI)
• DeepSeek
• Gemini

From my experience, each one seems to behave differently depending on the task.

ChatGPT has been the most consistent for general debugging and explaining code, especially in longer conversations, though results can vary depending on complexity.

DeepSeek has been useful for straightforward code generation tasks.

Copilot is generally helpful but sometimes feels limited when logic gets more complex.

Gemini hasn’t been as strong for my specific workflow, but I’ve only used it in a few cases.

Grok has been a bit inconsistent, but occasionally produces good results for simpler problems.

Curious how others are approaching this right now. Are you mainly sticking to one tool, or switching between multiple depending on what you’re building?

Hi,

We’ve been running Veritas NetBackup in an on-prem setup for a few years, but the cost of licensing + infrastructure is getting harder to justify, so we’re now re-evaluating our backup approach.

The main focus is VMware workloads, and we’re trying to understand what actually makes sense today in terms of cost and long-term scalability.

For a simplified reference point, here’s a smaller scenario we’re using for modeling:

We have 10 VMs (~200GB each). Retention is split, 5 VMs kept for 1 year and the other 5 kept for 30 days. That puts us at roughly 2TB initial backup size, with a small daily change rate (~0.1%).

From there, we’re trying to understand how costs scale when you factor in:

• long-term retention tiers
• snapshot / backup storage growth
• data transfer / egress (if applicable)
• licensing vs managed service pricing

What we’re trying to compare is basically:

• AWS-native approach (AWS Backup / EBS snapshots / S3 lifecycle tiers)
• Veeam or similar third-party backup tools
• Legacy enterprise setups like NetBackup / Veritas

The part that’s still unclear for us is where the real cost differences actually show up in practice. On paper, AWS-native looks simpler, but I’m not sure if it holds up once you scale retention and restore requirements. On the other hand, tools like Veeam add flexibility, but it’s not obvious if they reduce cost or just shift it into a different layer.

Before taking anything to management, I’m trying to get a clearer view of what actually drives cost in real deployments (storage growth, retention policies, licensing, operational overhead, etc.) and whether third-party tools genuinely justify their complexity.

Would appreciate any real-world input from anyone who’s done a similar migration or comparison.

We're looking to move away from Veeam due to ongoing support issues and instability in our current setup. Their support has been slow and unhelpful, and we've had persistent problems with S3 storage jobs for months without a proper fix.

We're seeing issues like backup jobs failing, files getting locked unexpectedly, cascading failures across jobs, and frequent checkpoint removal errors. Support engagement hasn't helped resolve the underlying problems.

At this point, we're exploring other backup and replication platforms that can handle S3-based workflows reliably at scale especially in environments with immutable storage and hybrid on-prem + DR setups.

What are people actually using as solid alternatives these days?