{ "nbformat": 4, "nbformat_minor": 5, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.13.0" }, "blog_metadata": { "topic": "What GPT-5.6 on Azure Databricks Means for Data Platform Economics", "slug": "what-gpt-5-6-on-azure-databricks-means-for-data-platform-eco", "generated_by": "LinkedIn Post Generator + Azure OpenAI", "generated_at": "2026-07-13T21:02:00.965Z" } }, "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# What GPT-5.6 on Azure Databricks Means for Data Platform Economics\n", "\n", "This notebook turns the blog post into a hands-on validation workflow focused on platform economics rather than model novelty. It explores how data gravity, governance, quotas, and chargeback affect the real cost of using reasoning models near a governed Databricks estate." ] }, { "cell_type": "code", "metadata": {}, "source": [ "%pip install pandas matplotlib" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "from datetime import datetime\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from collections import defaultdict" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Economic framing: data gravity, governance, and inference economics\n", "\n", "The blog argues that the main benefit is not just access to a new model endpoint, but better operational placement. If governed silver and gold data already live in a lakehouse, moving prompts to the data can be cheaper than copying governed data into a separate AI tier.\n", "\n", "This quick model compares a sidecar AI architecture with a Databricks-adjacent architecture using simple cost assumptions." ] }, { "cell_type": "code", "metadata": {}, "source": [ "cost_components = {\n", " \"separate_ai_tier\": {\n", " \"data_copy_storage_usd\": 450,\n", " \"connectors_networking_usd\": 300,\n", " \"duplicate_governance_reviews_usd\": 700,\n", " \"extra_support_surface_usd\": 550,\n", " \"inference_usd\": 900,\n", " },\n", " \"databricks_adjacent_ai\": {\n", " \"data_copy_storage_usd\": 100,\n", " \"connectors_networking_usd\": 120,\n", " \"duplicate_governance_reviews_usd\": 250,\n", " \"extra_support_surface_usd\": 220,\n", " \"inference_usd\": 900,\n", " },\n", "}\n", "\n", "rows = []\n", "for architecture, components in cost_components.items():\n", " total = sum(components.values())\n", " row = {\"architecture\": architecture, **components, \"total_monthly_usd\": total}\n", " rows.append(row)\n", "\n", "df_arch = pd.DataFrame(rows)\n", "print(df_arch)\n", "\n", "ax = df_arch.set_index(\"architecture\")[\"total_monthly_usd\"].plot(kind=\"bar\", figsize=(8, 4), title=\"Illustrative monthly platform economics\")\n", "ax.set_ylabel(\"USD\")\n", "plt.tight_layout()\n", "plt.show()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code example: tag and log GPT-5.6 inference usage\n", "\n", "The blog recommends instrumenting usage on day one and tagging by workspace, use case, and data product. This creates enough signal for showback and later chargeback, even before perfect cost accounting exists." ] }, { "cell_type": "code", "metadata": {}, "source": [ "from datetime import datetime\n", "\n", "def log_inference_usage(workspace, use_case, data_product, model, prompt_tokens, completion_tokens):\n", " total_tokens = prompt_tokens + completion_tokens\n", " record = {\n", " \"ts_utc\": datetime.utcnow().isoformat(),\n", " \"workspace\": workspace,\n", " \"use_case\": use_case,\n", " \"data_product\": data_product,\n", " \"model\": model,\n", " \"prompt_tokens\": prompt_tokens,\n", " \"completion_tokens\": completion_tokens,\n", " \"total_tokens\": total_tokens,\n", " }\n", " print(record) # replace with Delta/Event Hub/App Insights sink\n", " return record\n", "\n", "record = log_inference_usage(\"adb-prod-eus\", \"customer-support-copilot\", \"claims360\", \"gpt-5.6\", 1800, 420)\n", "record" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code example: roll up usage into a simple cost view\n", "\n", "This example converts token usage into an estimated cost view for platform economics reporting. It is intentionally simple and useful for early showback discussions by workspace, use case, and data product." ] }, { "cell_type": "code", "metadata": {}, "source": [ "PRICE_PER_1K_TOKENS = 0.012 # example only\n", "\n", "usage = [\n", " {\"workspace\": \"adb-prod-eus\", \"use_case\": \"customer-support-copilot\", \"data_product\": \"claims360\", \"total_tokens\": 2220},\n", " {\"workspace\": \"adb-prod-eus\", \"use_case\": \"sql-assistant\", \"data_product\": \"finops-mart\", \"total_tokens\": 980},\n", "]\n", "\n", "for row in usage:\n", " row[\"estimated_cost_usd\"] = round((row[\"total_tokens\"] / 1000) * PRICE_PER_1K_TOKENS, 4)\n", "\n", "summary = {}\n", "for row in usage:\n", " key = (row[\"workspace\"], row[\"use_case\"], row[\"data_product\"])\n", " summary[key] = summary.get(key, 0) + row[\"estimated_cost_usd\"]\n", "\n", "print(summary)\n", "\n", "usage_df = pd.DataFrame(usage)\n", "usage_df" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quotas and throughput planning\n", "\n", "The blog highlights quotas as a design constraint, not an afterthought. The following simulation helps validate whether expected request volume fits within a simple token quota and shows where throttling risk appears." ] }, { "cell_type": "code", "metadata": {}, "source": [ "daily_quota_tokens = 10000\n", "simulated_requests = [\n", " {\"request_id\": 1, \"workspace\": \"adb-prod-eus\", \"use_case\": \"customer-support-copilot\", \"tokens\": 2200},\n", " {\"request_id\": 2, \"workspace\": \"adb-prod-eus\", \"use_case\": \"sql-assistant\", \"tokens\": 980},\n", " {\"request_id\": 3, \"workspace\": \"adb-prod-eus\", \"use_case\": \"claims-summarization\", \"tokens\": 3100},\n", " {\"request_id\": 4, \"workspace\": \"adb-prod-eus\", \"use_case\": \"fraud-investigation\", \"tokens\": 2900},\n", " {\"request_id\": 5, \"workspace\": \"adb-prod-eus\", \"use_case\": \"executive-briefing\", \"tokens\": 1700},\n", "]\n", "\n", "running_total = 0\n", "results = []\n", "for req in simulated_requests:\n", " running_total += req[\"tokens\"]\n", " req_result = req.copy()\n", " req_result[\"running_total_tokens\"] = running_total\n", " req_result[\"within_quota\"] = running_total <= daily_quota_tokens\n", " results.append(req_result)\n", "\n", "quota_df = pd.DataFrame(results)\n", "print(quota_df)\n", "\n", "quota_df.plot(x=\"request_id\", y=\"running_total_tokens\", marker=\"o\", figsize=(8, 4), title=\"Running token consumption vs daily quota\")\n", "plt.axhline(daily_quota_tokens, color=\"red\", linestyle=\"--\", label=\"daily quota\")\n", "plt.ylabel(\"Tokens\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code example adaptation: governance checks across Databricks and Azure AI resources\n", "\n", "The original blog includes PowerShell for governance checks. Since this notebook is Python-first, the next cell recreates the same validation pattern with mock resource metadata so you can test the logic in a notebook without Azure access." ] }, { "cell_type": "code", "metadata": {}, "source": [ "resources = [\n", " {\n", " \"name\": \"adb-prod-eus\",\n", " \"type\": \"Databricks\",\n", " \"managed_rg_encryption_prepared\": True,\n", " \"tags\": {\"owner\": \"platform\", \"environment\": \"prod\", \"costCenter\": \"1001\", \"dataProduct\": \"claims360\", \"useCase\": \"analytics\"},\n", " },\n", " {\n", " \"name\": \"aoai-prod-eus\",\n", " \"type\": \"AzureAI\",\n", " \"public_network_access\": \"Disabled\",\n", " \"tags\": {\"owner\": \"ai-platform\", \"environment\": \"prod\", \"costCenter\": \"1001\"},\n", " },\n", "]\n", "\n", "governance_rows = []\n", "for r in resources:\n", " governance_rows.append({\n", " \"Resource\": r[\"name\"],\n", " \"Type\": r[\"type\"],\n", " \"TagsPresent\": len(r.get(\"tags\", {})) > 0,\n", " \"ManagedRG\": r.get(\"managed_rg_encryption_prepared\"),\n", " \"PublicNetworkAccess\": r.get(\"public_network_access\"),\n", " })\n", "\n", "governance_df = pd.DataFrame(governance_rows)\n", "governance_df" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code example adaptation: enforce a minimal tag and policy posture\n", "\n", "The blog also includes PowerShell for identifying missing tags. This Python version checks a mock inventory for the minimum tags needed for cost allocation and deployment consistency." ] }, { "cell_type": "code", "metadata": {}, "source": [ "required_tags = [\"owner\", \"environment\", \"costCenter\", \"dataProduct\", \"useCase\"]\n", "\n", "resource_inventory = [\n", " {\n", " \"Name\": \"adb-prod-eus\",\n", " \"ResourceType\": \"Microsoft.Databricks/workspaces\",\n", " \"Tags\": {\"owner\": \"platform\", \"environment\": \"prod\", \"costCenter\": \"1001\", \"dataProduct\": \"claims360\", \"useCase\": \"analytics\"},\n", " },\n", " {\n", " \"Name\": \"aoai-prod-eus\",\n", " \"ResourceType\": \"Microsoft.CognitiveServices/accounts\",\n", " \"Tags\": {\"owner\": \"ai-platform\", \"environment\": \"prod\", \"costCenter\": \"1001\"},\n", " },\n", " {\n", " \"Name\": \"adb-dev-eus\",\n", " \"ResourceType\": \"Microsoft.Databricks/workspaces\",\n", " \"Tags\": {\"owner\": \"platform\", \"environment\": \"dev\", \"useCase\": \"experimentation\"},\n", " },\n", "]\n", "\n", "non_compliant = []\n", "for resource in resource_inventory:\n", " tags = resource.get(\"Tags\", {})\n", " missing = [tag for tag in required_tags if tag not in tags]\n", " if missing:\n", " non_compliant.append({\n", " \"Resource\": resource[\"Name\"],\n", " \"Type\": resource[\"ResourceType\"],\n", " \"MissingTags\": \",\".join(missing),\n", " \"Status\": \"NonCompliant\",\n", " })\n", "\n", "compliance_df = pd.DataFrame(non_compliant)\n", "compliance_df" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Architecture flow for showback and governance\n", "\n", "The blog's flow can be represented as a simple sequence: a Databricks use case triggers GPT-5.6 inference, usage is tagged, telemetry lands in an observability sink, cost is estimated, and a showback or chargeback dashboard is produced. Governance checks should apply to both the data platform and the model access layer." ] }, { "cell_type": "code", "metadata": {}, "source": [ "flow_steps = [\n", " \"Business use case in Databricks\",\n", " \"GPT-5.6 inference call\",\n", " \"Usage tags: workspace/useCase/dataProduct\",\n", " \"Observability sink: Delta or Log Analytics\",\n", " \"Cost model: tokens x price\",\n", " \"Chargeback / showback dashboard\",\n", "]\n", "\n", "for i, step in enumerate(flow_steps, start=1):\n", " print(f\"{i}. {step}\")\n", "\n", "flow_df = pd.DataFrame({\"step_number\": list(range(1, len(flow_steps)+1)), \"step\": flow_steps})\n", "flow_df" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Where savings show up and where teams still overpay\n", "\n", "The blog identifies savings from fewer data copies, less custom integration, fewer parallel governance reviews, and fewer duplicate monitoring surfaces. It also warns that teams still overpay when they use premium reasoning for low-value tasks, ignore quotas, or split ownership across too many teams.\n", "\n", "The next cell scores a few example use cases to help decide whether premium reasoning is economically justified." ] }, { "cell_type": "code", "metadata": {}, "source": [ "use_cases = [\n", " {\"use_case\": \"customer-support-copilot\", \"reasoning_need\": 8, \"business_value\": 9, \"latency_sensitivity\": 6},\n", " {\"use_case\": \"sql-assistant\", \"reasoning_need\": 5, \"business_value\": 7, \"latency_sensitivity\": 8},\n", " {\"use_case\": \"simple-faq-bot\", \"reasoning_need\": 2, \"business_value\": 4, \"latency_sensitivity\": 9},\n", " {\"use_case\": \"fraud-investigation\", \"reasoning_need\": 9, \"business_value\": 10, \"latency_sensitivity\": 5},\n", "]\n", "\n", "rows = []\n", "for u in use_cases:\n", " premium_fit_score = round((u[\"reasoning_need\"] * 0.5) + (u[\"business_value\"] * 0.4) - (u[\"latency_sensitivity\"] * 0.1), 2)\n", " recommendation = \"Use premium reasoning\" if premium_fit_score >= 6 else \"Consider cheaper model tier\"\n", " rows.append({**u, \"premium_fit_score\": premium_fit_score, \"recommendation\": recommendation})\n", "\n", "fit_df = pd.DataFrame(rows).sort_values(\"premium_fit_score\", ascending=False)\n", "fit_df" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "This notebook validated the blog's central claim: the economics shift when inference moves closer to a governed Databricks core. The biggest gains often come from reducing duplicated controls, copies, and operating surfaces rather than from token price alone.\n", "\n", "## Next Steps\n", "\n", "- Replace mock usage logs with a real sink such as Delta, Event Hub, or Application Insights.\n", "- Connect quota checks to actual Azure model deployment limits and expected throughput.\n", "- Extend the cost model to include storage, networking, support, and governance effort.\n", "- Add real Azure resource inventory queries for governance and tag compliance.\n", "- Separate experimentation economics from production economics before formal chargeback." ] } ] }