Understand what you are actually buying when you buy an LLM
For platform, infrastructure and ops teams who need to run AI systems in production without treating the model as a black box someone else understands.
Sound familiar?
- Infrastructure teams get handed an LLM API and are told to make it production-ready with no shared vocabulary for what that means.
- Cost, latency and reliability trade-offs are guessed at rather than measured.
- Nobody owns the decision between hosted APIs, self-hosted models and hybrid setups.
- Scaling and monitoring patterns from classic services get reused on AI workloads where they quietly fail.
What we do
How the models actually work
Enough of the underlying mechanics — tokens, context windows, inference cost — that infrastructure decisions stop being guesswork.
Deployment models compared
Hosted APIs, self-hosted open models and hybrid architectures, with the real cost, latency and control trade-offs of each.
Scaling, caching and cost control
Practical patterns for handling load, caching responses and keeping inference cost from becoming your biggest line item.
Monitoring and reliability
What to observe in an LLM-backed system that classic APM tools do not show you by default.
Framework
- Duration
- 1 day (approx. 7 hours incl. breaks)
- Group size
- 8-12 participants
- Format
- In-house at your site or remote
- Prerequisites
- Experience operating production infrastructure; no prior AI-specific knowledge required.
- Who it's for
- Platform engineers, SREs and infrastructure leads
- Investment
- on request (fixed in-house day rate)
Agenda
- Model mechanics: tokens, context windows, inference cost
- Hosted vs. self-hosted vs. hybrid — building the decision framework
- Scaling and caching patterns for LLM workloads
- Cost control exercise on a sandbox setup
- Monitoring and observability beyond classic APM
- Bringing the framework back to your own architecture
Questions we get
Do we need to pick a specific cloud or model provider before the workshop?
No, we cover the major hosted and self-hosted options and help you build the decision framework, not just apply it to one vendor.
Is this workshop hands-on or conceptual?
Both. We work through real cost and scaling scenarios on a sandbox setup, so the concepts land as decisions you can defend.
Who typically attends?
Platform engineers, SREs and infrastructure leads who will own the AI stack once it is in production.
Tell us what's running in production.
We'll tell you what we'd check first — and what we wouldn't bother with.
Book a call