Hey folks! Been heads down on a side project for the last couple of weekends, and I'm pretty stoked to share it with you all. As we've been integrating more AI/ML features into our pipelines, the question of "which LLM provider/service should we use?" kept popping up. So I built a comparison dashboard that tracks OpenClaw, AWS Bedrock, and custom self-hosted LLMs across metrics that actually matter for DevOps and production use.
It's not just about token cost or latency in a vacuum. I wanted to see how each option behaves under real deployment scenarios. The dashboard pulls data on:
* **Provisioning time & infra drift** (Terraform apply durations, CloudWatch metrics vs. self-hosted Prometheus)
* **CI/CD integration complexity** (e.g., steps needed to update a model in a GitOps flow)
* **Observability maturity** (out-of-the-box tracing, logging, and how easy it is to add custom exporters)
* **Scaling events & cost per 1000 inference requests** under different loads
Here's a snippet of the config I used to generate the Terraform plan comparison for Bedrock vs. a custom model on EKS:
```hcl
# Example module for tracking Bedrock model deployment state
module "bedrock_model_version" {
source = "./modules/bedrock_tracker"
model_arn = aws_bedrock_custom_model.main.arn
git_hash = var.app_version # Tied to our CI pipeline
}
```
The early results are fascinating! OpenClaw is surprisingly agile for rapid prototyping, but their GitOps story is still forming. Bedrock's integration with the rest of the AWS ecosystem is a huge win for teams already in that orbit, though you trade off some control. Custom LLMs, while more upfront work, give you that beautiful granular monitoring and can be cheaper at very high, steady throughput.
The real "so what" for me? This isn't just an academic comparison. Choosing a provider locks you into a certain operational workflow. If your team values infrastructure as code and unified monitoring, that heavily weights the decision. I'm hoping this tool can help others make a data-driven choice that fits their team's ethos.
You can check out the public read-only view of the dashboard [here]( https://example.com/dashboard) and the collection scripts are open-sourced on my GitHub. Would love to hear what metrics you'd add, or if you've run into similar decisions!
Keep deploying!
Keep deploying!