FlexInfer Platform Lab
I help teams ship private AI infrastructure for sensitive workloads: on-prem/hybrid inference operations, context governance, and healthcare-aligned integration reliability.
Recent outcomes
- Lab demos wired to live K8s data, not screenshots: cluster overview.
- CI visibility you can reason about end-to-end: pipeline visualization.
- Operational UX for inference platforms: model gallery.
Why this exists
Most teams don't fail at model quality. They fail at operations: runaway GPU spend, deploys that feel risky, and dashboards that don't answer "is it working?" quickly.
I publish practical patterns for operating private AI platforms in environments where reliability, traceability, and data boundaries matter.
How I work
Baseline
Measure cost and latency under a representative workload. Identify bottlenecks that actually move spend.
Outputs
- Workload profile (traffic shape + prompt mix)
- Cost curve (throughput vs spend)
- Bottleneck map (what is actually limiting)
Signals I track
Stabilize
Put guardrails in place: routing, budgets, autoscaling, and rollout/rollback that doesn’t require heroics.
Outputs
- Capacity model + safe scaling strategy
- Rollout/rollback plan with guardrails
- Cost controls (budgets, caps, routing rules)
Signals I track
Operate
Alerts that point to ownership, runbooks that match reality, and dashboards that answer “is it working?” fast.
Outputs
- SLO-style targets (latency, error, cost)
- Dashboards that match on-call questions
- Runbooks tied to alerts (not a wiki graveyard)
Signals I track
Deliverables
I try to leave artifacts, not vibes. If we work together, you should end up with a baseline you can reproduce, a rollout plan you can execute, and operational docs that your team will actually use.
Typical artifacts
- Baseline report (workload, costs, bottlenecks)
- Architecture notes + deployment model
- Dashboards + alert thresholds (first pass)
- Runbooks (alerts -> actions)
- 90-day plan with owners and milestones
Clarity
You know what drives cost and what to change first.
Control
Deploys have guardrails and rollbacks are rehearsed.
Operations
Dashboards + runbooks answer on-call questions fast.
The lab
Live, read-only dashboards. No screenshots. If it breaks, you see it.
Founder
Cody Blevins
I've spent a decade building and operating integration platforms in healthcare, then took that operational mindset into GPU clusters, inference tooling, and GitOps workflows for private environments. FlexInfer is where I publish those patterns and offer hands-on consulting.
Focus
Production AI infra
Style
Measurable, ops-first
Mode
Remote-friendly
Want help shipping this?
If inference is unstable, costs are spiking, or deploys feel risky, tell me what you're building and where it's getting stuck.