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oci-deal-accelerator/kb/pricing/compute.yaml
root ca93a0aa4e Auto-refresh KB pricing, align skill with Anthropic best practices 
Why this set of changes:
- KB pricing was drifting silently — domain files (database.yaml,
  storage.yaml, etc.) had prices 30-800% off the live Oracle API and
  nobody read them. The skill was auditing as stale on every check
  with no path to fix it.
- The skill itself violated Anthropic's spec (`name` field had
  uppercase/spaces) and was over the 500-line guideline (647 lines),
  hurting discovery and load performance.
- Welcome flow occasionally improvised the menu instead of reading
  SKILL.md, missing options.

Pricing — single source of truth, fully automated:
- Extend tools/refresh_sku_catalog.py with --refresh-domain compute,
  pulls shape-level prices from the Oracle public pricing API
  (apexapps.oracle.com), preserves manual fields (notes, GPU specs,
  free-tier annotations, estimation_helpers), recomputes derived
  monthly values, and protects $0 free-tier prices from overwrite.
- Delete 12 redundant pricing/<domain>.yaml files. They duplicated
  oci-sku-catalog.yaml with worse abstractions and were nobody's
  source of truth (no tool consumed them).
- Migrate the genuinely valuable knowledge from those 12 files
  (billing models, BYOL rules, free-tier rules, ECPU vs OCPU,
  X11M elastic model, hyperscaler comparisons, service nuances)
  into kb/field-knowledge/pricing-knowledge.yaml — non-numeric,
  no refresh needed.
- Result: pricing freshness check goes from 13 stale files to 0.

KB freshness automation:
- Add tools/kb_freshness.py — wrapper around kb_linter.check_freshness()
  with --check, --auto-refresh, --json, --quiet modes. Bridges stale
  files to their refresh tools (SKU catalog, compute domain, arch
  center). Wired into the welcome flow as a pre-flight banner that
  asks the user before refreshing.
- Fix pre-existing kb_linter bug: it crashed on the 45 multi-doc
  YAML files (frontmatter + body pattern) because it used safe_load
  instead of safe_load_all. Freshness check was effectively dead.
- Standardize timestamp field: linter now accepts last_verified,
  last_updated, and last_refreshed; refresh_arch_catalog writes
  last_verified instead of last_refreshed.
- Add make freshness / make freshness-refresh targets.

Skill alignment with Anthropic Agent Skills best practices:
- Rename `name: OCI Deal Accelerator` to `oci-deal-accelerator`
  to comply with the [a-z0-9-]{1,64} spec.
- Refactor SKILL.md from 674 to 445 lines via progressive disclosure:
  extract WA review output format, ECAL readiness format, and output
  conventions into docs/skill/*.md referenced from the main file.
- Add scripts/sync-skill.py + make sync-skill: source of truth is
  root SKILL.md, .agents/skills/oci-deal-accelerator/SKILL.md is
  auto-generated. make lint validates sync.
- Add evaluations/ with 3 manual baseline scenarios (welcome-flow,
  full-proposal, wa-review) per the Anthropic best-practices guidance
  to "build evaluations first."

Welcome flow hardening:
- Tighten CLAUDE.md to MANDATE reading SKILL.md before showing the
  menu (no improvising), and document the freshness pre-flight check
  with the ask-before-refresh user flow.
- Update SKILL.md welcome flow to instruct: parse kb_freshness JSON,
  show banner with stale count + oldest file, prompt user to refresh
  (only when an automated tool exists), fall back silently on errors.

Linter hygiene (zero remaining issues):
- Expand config/kb-tags.yaml taxonomy with features, operations,
  metrics, limitations sections covering 31 previously-unknown tags
  used in field findings (rac, ecpu, refreshable-clone, hnsw, etc.).
- Assign owners for kb/compatibility/, kb/competitive/,
  kb/well-architected/ (Diego Cabrera as default until team grows);
  kb/pricing/ marked as "Auto-refreshed" since it no longer needs
  human ownership.
- kb_linter accepts top-level `date` as fallback for contributor
  block; migrate FF-202603-008 from legacy `reported_by` to
  contributor block.
- Result: linter goes from 45 issues to 0.

Other:
- Recompute estimation_helpers monthly values in compute.yaml after
  the price refresh (they were derived from the old E5/A1 numbers).
- Add kb/README.md — contributor guide (directory map, frontmatter
  spec, refresh tooling, review cadence).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-08 23:59:32 -03:00

161 lines
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last_verified: '2026-04-08'
source: https://apexapps.oracle.com/pls/apex/cetools/api/v1/products/
description: OCI Compute pricing for estimation purposes. Auto-refreshed from the
Oracle public pricing API by tools/refresh_sku_catalog.py --refresh-domain compute.
GPU shapes, secure desktops, estimation_helpers, and discounts are NOT auto-refreshed.
currency: USD
---
flexible_shapes:
VM.Standard.E6.Flex:
ocpu_per_hour: 0.03
memory_per_gb_hour: 0.002
monthly_730h:
ocpu: 21.9
memory_per_gb: 1.46
notes: AMD EPYC (latest gen), best price/performance for general workloads
VM.Standard.E5.Flex:
ocpu_per_hour: 0.03
memory_per_gb_hour: 0.002
monthly_730h:
ocpu: 21.9
memory_per_gb: 1.46
notes: AMD EPYC, general-purpose workloads
VM.Standard.E4.Flex:
ocpu_per_hour: 0.025
memory_per_gb_hour: 0.0015
monthly_730h:
ocpu: 18.25
memory_per_gb: 1.09
notes: AMD EPYC (previous gen)
VM.Standard.E3.Flex:
ocpu_per_hour: 0.025
memory_per_gb_hour: 0.0015
monthly_730h:
ocpu: 18.25
memory_per_gb: 1.09
notes: AMD EPYC (older gen)
VM.Standard3.Flex:
ocpu_per_hour: 0.0336
memory_per_gb_hour: 0.0022
monthly_730h:
ocpu: 24.53
memory_per_gb: 1.61
notes: Intel Xeon, for Intel-optimized workloads
VM.Standard.A2.Flex:
ocpu_per_hour: 0.014
memory_per_gb_hour: 0.002
monthly_730h:
ocpu: 10.22
memory_per_gb: 1.46
notes: Ampere Arm (latest A2), best cost for Arm-compatible workloads
VM.Standard.A1.Flex:
ocpu_per_hour: 0.0127
memory_per_gb_hour: 0.0008
monthly_730h:
ocpu: 9.27
memory_per_gb: 0.58
free_tier: 3,000 OCPU-hours + 18,000 GB-hours per month
notes: Ampere Arm A1, eligible for Always Free tier
VM.Standard.x86.Generic:
ocpu_per_hour: 0.0252
memory_per_gb_hour: 0.0017
notes: Generic x86 pricing when specific shape not yet selected
VM.Optimized3.Flex:
ocpu_per_hour: 0.054
memory_per_gb_hour: 0.0015
monthly_730h:
ocpu: 39.42
memory_per_gb: 1.09
notes: Highest single-thread performance
bare_metal_shapes:
BM.Standard.E5:
ocpu_per_hour: 0.03
notes: AMD EPYC bare metal
BM.Standard.E4:
ocpu_per_hour: 0.025
notes: AMD EPYC bare metal (previous gen)
BM.Standard.E3:
ocpu_per_hour: 0.025
notes: AMD EPYC bare metal (older gen)
BM.Standard3:
ocpu_per_hour: 0.0504
notes: Intel Xeon bare metal
BM.Standard.A1:
ocpu_per_hour: 0.0127
notes: Ampere Arm bare metal
BM.Standard.x9:
ocpu_per_hour: 0.04
notes: Intel Xeon x9 bare metal
gpu_shapes:
BM.GPU.B300:
per_gpu_hour: 26.88
gpu_count: 8
gpu_model: NVIDIA B300
notes: Blackwell latest, HPC/AI training
BM.GPU.B200:
per_gpu_hour: 20.16
gpu_count: 8
gpu_model: NVIDIA B200
notes: Blackwell, large-scale training
BM.GPU.H200:
per_gpu_hour: 18.54
gpu_count: 8
gpu_model: NVIDIA H200
notes: Hopper H200, large model training
BM.GPU.H100:
per_gpu_hour: 12.96
gpu_count: 8
gpu_model: NVIDIA H100
notes: Hopper H100, training workloads
BM.GPU.A100:
per_gpu_hour: 6.72
gpu_count: 8
gpu_model: NVIDIA A100 40GB/80GB
notes: Training and inference, 40GB or 80GB HBM2e
VM.GPU.A10.1:
per_gpu_hour: 0.72
gpu_count: 1
gpu_model: NVIDIA A10
notes: Inference workloads, cost-effective
VM.GPU.A10.2:
per_gpu_hour: 0.72
gpu_count: 2
gpu_model: NVIDIA A10
notes: Inference workloads
BM.GPU.L40S:
per_gpu_hour: 2.42
gpu_count: 4
gpu_model: NVIDIA L40S
notes: Ada Lovelace, inference and graphics
secure_desktops:
per_desktop_month: 1750.0
notes: Managed virtual desktop infrastructure
estimation_helpers:
typical_app_server:
config: VM.Standard.E5.Flex, 4 OCPU, 64 GB
monthly: 181.04
typical_web_server:
config: VM.Standard.E5.Flex, 2 OCPU, 16 GB
monthly: 67.16
typical_bastion:
config: VM.Standard.E5.Flex, 1 OCPU, 8 GB
monthly: 33.58
typical_arm_app_server:
config: VM.Standard.A1.Flex, 4 OCPU, 24 GB
monthly: 51.1
discounts:
preemptible: ~50% off on-demand
capacity_reservation: ~15% off on-demand (85% of list)
reserved_1yr: ~35% off on-demand
reserved_3yr: ~55% off on-demand
universal_credits: Volume discounts on committed spend
os_pricing:
free:
- Oracle Linux
- CentOS
- Ubuntu
- Oracle Autonomous Linux
paid:
windows_server:
notes: Pricing varies by edition, added to compute cost
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