A Practical Playbook: Bringing Micro-Compute into Your Stack

You don’t need a rewrite to get the benefits. Follow this five-step playbook:

Step 1 — Pick a lighthouse workload
Choose something impactful but safe to iterate on: a nightly batch inference, a finetune job, or a render queue.

Step 2 — Add checkpoints & idempotency
Instrument the workflow so it can survive pre-emption. Document recovery time and acceptable redo %.

Step 3 — Declare micro-unit intents
Replace VM types with resource intents: 24 vCPU-min, 0.5 GPU-hr, 8 GB-hr RAM, deadline, and price cap.

Step 4 — Observe effective cost
Dashboards should show $ per completed unit, redo %, and slice utilization. Create budget guards based on those.

Step 5 — Expand by pattern
Clone success to adjacent workloads. Introduce fractional GPUs for inference, then training. Add reputation filters for providers as you scale.

Common pitfalls

• Treating storage as an afterthought—use durable, region-aware buckets.

• Over-optimizing for latency in batch pipelines—throughput usually wins.

• Ignoring egress—co-locate data with compute when possible.

Expected outcomes (first 60–90 days)

• 30–60% lower effective $/outcome on batch workloads

• Faster job starts (no waiting for a “perfect” VM)

• Cleaner cost attribution and fewer budget surprises

Start small, make checkpoints routine, and measure the right thing. Micro-compute will do the rest.