Ship innovation—not technical debt.
Building your own GenAI infrastructure sounds exciting until you hit the hidden costs, latency spikes and 3am pager alerts.
In the race to adopt Generative AI, companies face a make-or-break decision: build your LLM training and inference stack from scratch or invest in a managed platform?
This free comprehensive guide reveals what DIY AI really takes beyond just spinning up the latest LLM.
What You'll Learn:
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See the whole iceberg, not just the tip: Understand every moving part—data pipelines, LLM fine‑tuning workflows, serving optimizations, GPUs, observability, governance and more—before you commit.
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Calculate true total cost of ownership (TCO): Go beyond cloud bills to account for talent, tooling, and opportunity cost.
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Apply a decision‑making framework that scales: Plug your own constraints into a scoring rubric to get a clear build‑or‑buy answer.
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Learn from real‑world trenches: How Checkr, Convirza, and other engineering teams saved months—and millions—by pivoting to managed GenAI platforms.
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Fast‑track your next sprint: Actionable checklists and best‑practice that let you move from “AI initiative” to deployed app in production in days, not quarters.
Who Should Read This?
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Software engineers and ML engineers tasked with “adding AI.”
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CTOs and VPs of Engineering balancing speed vs. control.
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Product leaders who need answers when the board asks, “Why haven’t we shipped that GenAI feature yet?”