docs(ms-ai-architect): KB-refresh critical cost 6/8 — token-counting verifisert mot fine-tuning-cost (RFT) + prompt-caching

Verifisert mot live Microsoft Learn (foundry/openai/how-to/fine-tuning-cost-management,
prompt-caching, use-your-data):
- RFT (Reinforcement Fine-Tuning) lagt til (filen dekket kun SFT/DPO): tidsbasert «pris = treningstid
  × timepris + grader-tokens», ~$100/time core training for o4-mini-2025-04-16, per-job-tak $5000
  (pauses + deploybart checkpoint ved tak, kan resume uten videre pristak). Cost-control-strategier med.
- Cache-TTL-tabell hadde FEIL akse («Foundry Classic vs AI Services») → korrigert til in-memory
  (5-10 min/maks 1t, alle GPT-4o+) vs extended (24h, GPT-5-serien+gpt-4.1, default for nyere).
- prompt_cache_key ~15 req/min overflow-caveat lagt til.
- On Your Data deprecation 2026-10-14 flagget (3 steder: token-est-seksjon, Foundry-seksjon, kilde #6).
- tiktoken model-set manglet o1-mini/o3-mini → lagt til.
- Header 2026-02→2026-06, verification-date 2026-02-04→2026-06. Priser urørt (kanonisk peker). validate 239/0.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

skills/ms-ai-security/references/cost-optimization/token-counting-optimization.md

@@ -1,6 +1,6 @@
 # Token Counting and Optimization Strategies
 
-**Last updated:** 2026-02
+**Last updated:** 2026-06
 **Status:** GA
 **Category:** Cost Optimization & FinOps for AI
 
@@ -56,7 +56,7 @@ def num_tokens_from_messages(messages, model="gpt-4o"):
     except KeyError:
         encoding = tiktoken.get_encoding("o200k_base")
 
-    if model in {"gpt-4o", "gpt-4o-mini", "gpt-5", "gpt-4.1", "o1", "o3", "o4-mini"}:
+    if model in {"gpt-4o", "gpt-4o-mini", "gpt-5", "gpt-4.1", "o1", "o1-mini", "o3", "o3-mini", "o4-mini"}:
         tokens_per_message = 3
         tokens_per_name = 1
 
@@ -97,6 +97,8 @@ string TrimToTokenLimit(string text, int maxTokens, Tokenizer tokenizer)
 
 ### Token Usage Estimation (Azure OpenAI On Your Data)
 
+> ⚠️ **On Your Data er deprecated og pensjoneres 2026-10-14.** Migrer til Foundry Agent Service + Foundry IQ for grounding. Token-estimering-metodikken under er fortsatt illustrativ for RAG-token-budsjettering.
+
 ```python
 import tiktoken
 
@@ -127,14 +129,14 @@ token_output = TokenEstimator().estimate_tokens(input_text)
 |-----------|-------|--------|
 | Minimum prompt length | 1 024 tokens | Cache hit kan først oppnås |
 | Granularitet | 128 tokens | Etter første 1024 tokens, cache hit per 128 tokens |
-| Cache TTL | 24 timer | Microsoft Foundry Classic |
-| Cache TTL | 5-10 min idle, max 1 time | Azure AI Services |
+| Cache TTL (in-memory) | 5-10 min idle, max 1 time | Alle GPT-4o eller nyere |
+| Cache TTL (extended) | Opptil 24 timer (`prompt_cache_retention: "24h"`) | GPT-5-serien + gpt-4.1; default 24h for nyere modeller |
 | Kostnad (Standard) | 50% rabatt på cached tokens | Varierer per modell |
 | Kostnad (Provisioned) | Opptil 100% rabatt | Inkludert i PTU-pris |
 
 **Design-prinsipper:**
 1. **Plasser repetitivt innhold først** — system messages, instructions, reference docs
-2. **Bruk `prompt_cache_key`** for å påvirke routing og øke cache hit rate
+2. **Bruk `prompt_cache_key`** for å påvirke routing og øke cache hit rate (NB: samme prefix + key over ~15 req/min → noen requests overflower til ekstra maskiner, og cache-effektiviteten faller)
 3. **Unngå variasjon i første 1024 tokens** — én endring = cache miss
 
 ```python
@@ -277,6 +279,8 @@ chunks = text_splitter.split_documents(pages)
 Cost = # training tokens × # epochs × price per token
 ```
 
+> **RFT (Reinforcement Fine-Tuning)** bruker en *tidsbasert* kostnadsmodell (ikke token-basert) med per-job-tak på $5 000 — se «Fine-Tuning Costs» under.
+
 **Token validation pre-training:**
 ```python
 import json
@@ -371,7 +375,7 @@ Er prompt >1024 tokens og repetitiv?
 
 ### Microsoft Foundry
 
-**Token Usage Estimation (On Your Data):**
+**Token Usage Estimation (On Your Data — deprecated, retires 2026-10-14):**
 - Intent prompt: ~1 366 tokens (gjennomsnitt)
 - Generation prompt: ~4 297 tokens (gjennomsnitt)
 - Response: ~111 tokens (gjennomsnitt)
@@ -474,6 +478,13 @@ Image tokens (high-res) = (# tiles × 170) + 85
 - Global Standard: $2 per 1M tokens (gpt-4.1, eksempel)
 - Developer (spot): 50% rabatt, kan bli paused/resumed
 
+**Training (RFT — Reinforcement Fine-Tuning):**
+- Tidsbasert, IKKE token-basert: `pris = treningstid (timer) × timepris + grader-inferencing-tokens (hvis model-grader brukes)`
+- Timepris: ~$100/time core training for `o4-mini-2025-04-16` (eksempel; se pricing-side for gjeldende sats)
+- **Per-job-tak: $5 000** — når taket nås pauses training og et deploybart checkpoint opprettes; du kan validere og resume (videre billing uten pristak)
+- Ingen kostnad for kø, failed jobs eller jobber kansellert før training starter; ved kansellering belastes arbeid frem til siste checkpoint
+- Kostnadskontroll: `reasoning_effort=Low`, begrens validering/`eval_samples`, velg minste grader-modell, juster `compute_multiplier`
+
 **Hosting:**
 - $1.70/time per deployment (Standard/Global Standard)
 - Påløper selv om modellen ikke brukes
@@ -570,18 +581,18 @@ def track_token_usage(prompt, completion, model="gpt-4o"):
 ## Kilder og verifisering
 
 **Microsoft Learn Documentation:**
-1. [Prompt caching - Azure OpenAI](https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/prompt-caching)
+1. [Prompt caching - Azure OpenAI](https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/prompt-caching) — **Verified MCP 2026-06** (in-memory + extended 24h)
 2. [Work with chat completions models - Token management](https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/chatgpt#manage-conversations)
 3. [Plan and manage costs for Azure OpenAI](https://learn.microsoft.com/en-us/azure/foundry/concepts/manage-costs)
 4. [Token counting in AI - Dynamics 365 Business Central](https://learn.microsoft.com/en-us/dynamics365/business-central/dev-itpro/developer/ai-system-app-token-counting)
 5. [Use Microsoft.ML.Tokenizers for text tokenization](https://learn.microsoft.com/en-us/dotnet/ai/how-to/use-tokenizers)
-6. [Azure OpenAI On Your Data - Token usage estimation](https://learn.microsoft.com/en-us/azure/foundry-classic/openai/concepts/use-your-data#token-usage-estimation-for-azure-openai-on-your-data)
-7. [Cost management for fine-tuning](https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/fine-tuning-cost-management)
+6. [Azure OpenAI On Your Data - Token usage estimation](https://learn.microsoft.com/en-us/azure/foundry-classic/openai/concepts/use-your-data#token-usage-estimation-for-azure-openai-on-your-data) — ⚠️ *deprecated, retires 2026-10-14*
+7. [Cost management for fine-tuning](https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/fine-tuning-cost-management) — **Verified MCP 2026-06** (SFT/DPO + RFT tidsbasert + $5000-tak)
 
 **OpenAI Resources:**
 8. [OpenAI Cookbook - Token counting](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb)
 9. [tiktoken GitHub repository](https://github.com/openai/tiktoken)
 
-**Verification Date:** 2026-02-04
+**Verification Date:** 2026-06
 **MCP Calls:** 4 (microsoft_docs_search × 3, microsoft_docs_fetch × 2, microsoft_code_sample_search × 1)
 **Confidence Level:** High — all data sourced from official Microsoft Learn documentation and verified OpenAI tooling