About Managed Models

Who I Am

I've spent the better part of a decade deploying ML models into production environments. I've wrestled with GPU availability on every major cloud, migrated inference workloads between providers when pricing changed overnight, and burned more hours than I'd like to admit debugging fine-tuning runs that silently diverged. I started Managed Models because I kept hitting the same problem: every platform's marketing page promises "effortless deployment," but the actual experience varies wildly depending on your model size, traffic patterns, and latency requirements.

This site exists to give practitioners the information I wish I'd had before signing annual contracts or committing to a platform's ecosystem. I test these tools myself, run real workloads through them, and report what I find. When a platform has a rough edge or a pricing trap, I say so. When something genuinely works well, I say that too.

How We Make Money

Managed Models earns revenue through affiliate commissions. When you click a link to a provider on this site and sign up for their service, we may receive a referral fee. This is the standard way independent review sites stay funded without putting content behind a paywall.

Here's what that does not mean: affiliate relationships never influence our assessments. We pick which tools to review based on what ML teams actually use in production, not on who offers the highest commission. Our reviews are written before any affiliate partnership is in place, and the editorial content doesn't change if a partnership ends. If a tool has problems, we document them regardless of our business relationship with the vendor.

Editorial Standards

  • No sponsored content appears on this site without explicit disclosure at the top of the page.
  • Rankings and recommendations are based on hands-on testing, documented methodology, and measurable criteria like latency, throughput, and cost per token.
  • No vendor can pay for placement or a higher score. There are no pay-for-play rankings.
  • When we make a mistake, we correct it publicly and note the change.
  • Factual claims are sourced. If we cite a benchmark number or pricing figure, we link to where we got it or describe how we measured it.