Graviton Migration
Graviton 20-40% cheaper. The migration.
Overview
Graviton migration moves workloads to AWS ARM-based instances for 20-40% cost-performance gains. Blanket migration is the wrong call; per-workload validation through benchmark and multi-arch builds is what produces real savings.
- Graviton 20-40% cheaper. Per-workload cost-performance gain; the price drop is real, but the workload must fit ARM.
- Multi-arch builds. Per-image multi-arch build; the same image runs on x86 and ARM; supports gradual migration.
- Per-workload benchmark. Per-workload performance comparison; produces evidence; not all workloads benefit equally.
- Per-language compatibility plus migration plan. Per-language ARM compat check; per-quarter migration progress.
The approach
The practical approach: per-workload benchmark before migration, multi-arch builds for portability, per-language compatibility verification, quarterly migration plan, documented per-service rationale. The team’s discipline produces matched compute.
- Per-workload benchmark. Per-workload performance comparison; the data informs the migration call per service.
- Multi-arch builds. Per-image multi-arch build; one Dockerfile, two architectures; the migration becomes a deploy decision.
- Per-language testing. Per-language ARM compatibility; some libraries lack ARM wheels; verify before deploy.
- Per-quarter migration plan. Quarterly migration progress tracked; the migration is a project, not an event.
- Document the migration. Per-service rationale committed to the repo; supports operational reviews.
Why this compounds
Graviton discipline compounds across services. Each migrated workload produces ongoing savings; the team’s compute expertise grows; new services ship multi-arch by default.
- Better cost efficiency. 20-40% savings on matched workloads; the bill drops as adoption scales.
- Better performance. Per-workload matched performance; some workloads run faster on ARM, not just cheaper.
- Better operational fit. Right architecture matches workload; the team picks per service, not per organisation.
- Institutional knowledge. Each migration teaches CPU patterns; the team’s compute engineering muscle grows.
Graviton discipline is an infrastructure discipline that pays off across years. Nova AI Ops integrates with compute telemetry, surfaces patterns, and supports the team’s compute discipline.