cost service: add platform base + per-VIN resource model with CPU/RAM display
- Updated cost model to show: (Platform Base) + (Per-VIN × VINs) - Platform base: 176 cores / 896GB RAM (Kafka, ClickHouse, MongoDB, Redis, PostgreSQL, gateway, monitoring) - Per-VIN marginal: 50mc / 82MB per vehicle - Added RESOURCE USAGE MODEL and COST FORMULA sections to report - Added CPU (mc) and RAM (MB) columns to TOP COST VEHICLES table - Updated README with new report output - virtual-vehicle: documented Vault cert TTL error troubleshooting
This commit is contained in:
Chris Rai
@@ -12,60 +12,68 @@ This service estimates the cost of running cloud services per VIN by:
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## Cost Estimation Methodology
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### Resource Estimates Per Active VIN
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### Cost Model
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| Activity Level | Messages/15min | CPU (cores) | Memory (GB) |
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|---------------|----------------|-------------|-------------|
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| Low | < 100 | 0.80 | 1.20 |
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| Medium | 100-1000 | 1.20 | 1.80 |
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| High | > 1000 | 1.60 | 2.40 |
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The cost model separates fixed platform costs from variable per-VIN costs:
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These estimates account for the full data pipeline per vehicle:
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- Data ingestion (MQTT/HTTP endpoints)
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- Kafka message processing
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- Stream processing and transformations
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- ClickHouse storage and queries
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- Redis caching
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- MongoDB document storage
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- API serving
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```
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Total Cost = Platform Base Cost + (Per-VIN Cost × Number of VINs) + Managed Services
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```
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### Base Infrastructure Costs
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Whether you have 100 vehicles or 100,000, you still need Kafka, databases, and gateway services running. That's your platform base cost. Then each additional vehicle adds a small marginal cost on top.
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Shared infrastructure costs are distributed across active vehicles each collection interval:
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### Platform Base Resources (Fixed)
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| Component | Cloud ($/15min) | On-Prem ($/15min) |
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|-----------|-----------------|-------------------|
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| Storage, Event Hubs, Defender, monitoring | $10.00 | $0.90 |
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What it takes to run the cloud services platform:
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### Cost Rates (per hour)
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| Component | CPU (cores) | Memory (GB) | Notes |
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|-----------|-------------|-------------|-------|
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| Kafka brokers | 32 | 128 | 3-node cluster |
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| ClickHouse | 64 | 256 | 3 shards for HA |
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| MongoDB | 16 | 128 | Replica set |
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| Redis | 16 | 128 | Cluster mode |
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| PostgreSQL | 32 | 128 | Primary + replicas |
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| Gateway services | 8 | 64 | API gateway, auth |
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| Monitoring/logging | 8 | 64 | Prometheus, Grafana, Loki |
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| **Total Platform Base** | **176** | **896** | |
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| Resource | Cloud (Azure) | On-Prem |
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|----------|---------------|---------|
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| CPU/core | $0.30 | $0.02 |
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| Memory/GB| $0.08 | $0.005 |
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### Per-VIN Resources (Marginal)
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#### Cloud Rates (Fudged Higher)
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- Based on Azure D-series VM pricing + 50% managed services overhead
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- Includes: AKS compute, managed Kafka (Event Hubs), CosmosDB, Azure Storage, networking, monitoring
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- Intentionally conservative (higher) to show true cloud TCO
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Incremental resources needed for each additional connected vehicle:
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#### On-Prem Rates (Fudged Lower)
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- Based on 3-year hardware amortization only
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- Assumes: owned hardware, minimal ops overhead
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- Intentionally optimistic (lower) to show on-prem savings
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- Does NOT include: datacenter costs, staff, power, cooling, network, maintenance
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| Activity Level | Messages/15min | CPU (millicores) | Memory (MB) |
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|---------------|----------------|------------------|-------------|
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| Low | < 100 | 50 | 80 |
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| Medium | 100-1000 | 75 | 120 |
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| High | > 1000 | 100 | 160 |
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### Cost Rates
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| Resource | Cloud (Azure) | On-Prem/Bare Metal |
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|----------|---------------|-------------------|
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| CPU/core-hour | $0.30 | $0.02 |
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| Memory/GB-hour | $0.08 | $0.005 |
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| Managed Services/15min | $10.00 | $2.50 |
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#### Why On-Prem is ~90% Cheaper
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- **Platform base**: Same hardware, but cloud charges ~15x more for managed services
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- **Per-VIN compute**: Cloud VMs cost ~15x more than amortized bare metal
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- **Managed services**: Event Hubs, CosmosDB, etc. have significant markup vs self-hosted equivalents
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### Savings Calculation
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```
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Per-VIN Cost = (CPU_cores × rate + Memory_GB × rate) × hours + (base_infra / active_vins)
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Cloud Cost = Per-VIN costs summed across fleet
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On-Prem Cost = Same formula with on-prem rates
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Platform Base Cost = (176 cores × rate + 896 GB × rate) × hours
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Per-VIN Cost = (0.05 cores × rate + 0.08 GB × rate) × hours × activity_multiplier
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Total Cost = Platform Base + (Per-VIN × VIN count) + Managed Services
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Cloud Cost = Total with cloud rates
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On-Prem Cost = Total with on-prem rates
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Savings = Cloud Cost - On-Prem Cost
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Savings % = (Savings / Cloud Cost) × 100
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```
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Expected savings: **~85-88%** with on-prem hosting (hardware costs only).
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Expected savings: **~90%** with on-prem/bare metal hosting.
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### Projected Annual Costs (5000 vehicles)
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@@ -114,40 +122,50 @@ curl http://localhost:8077/cost/report
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╔══════════════════════════════════════════════════════════════════╗
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║ COST SERVICE REPORT ║
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╠══════════════════════════════════════════════════════════════════╣
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║ Period: 2026-01-03 to 2026-02-03
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║ Period: 2026-01-05 to 2026-02-05
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╠══════════════════════════════════════════════════════════════════╣
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║ FLEET OVERVIEW ║
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║ ─────────────────────────────────────────────────────────────── ║
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║ Active Vehicles: 3143
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║ Cloud Cost: $5658.12
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║ On-Prem Cost: $389.98
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║ Savings: $5268.15 (93.1%)
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║ Active Vehicles: 3229
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║ Cloud Cost: $9761.61
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║ On-Prem Cost: $677.88
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║ Savings: $9083.73 (93.1%)
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╠══════════════════════════════════════════════════════════════════╣
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║ RESOURCE USAGE MODEL ║
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║ ─────────────────────────────────────────────────────────────── ║
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║ Platform Base: 176 cores / 896 GB RAM (fixed)
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║ Per-VIN Marginal: 50 millicores / 82 MB RAM
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║ Total Fleet: 337.5 cores / 1154.3 GB RAM
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╠══════════════════════════════════════════════════════════════════╣
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║ COST FORMULA ║
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║ ─────────────────────────────────────────────────────────────── ║
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║ (Platform Base) + (Per-VIN × 3229 VINs) + Managed Services
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╠══════════════════════════════════════════════════════════════════╣
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║ COST RATES ║
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║ ─────────────────────────────────────────────────────────────── ║
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║ Cloud: CPU $0.30/core-hr Memory $0.080/GB-hr
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║ On-Prem: CPU $0.02/core-hr Memory $0.005/GB-hr
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║ Base Infra: Cloud $10.00/15min On-Prem $0.90/15min
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║ Base Infra: Cloud $10.00/15min On-Prem $2.50/15min
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╠══════════════════════════════════════════════════════════════════╣
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║ ANNUAL PROJECTION (based on current usage) ║
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║ ─────────────────────────────────────────────────────────────── ║
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║ Cloud Annual: $67897.44
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║ On-Prem Annual: $4679.70
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║ Annual Savings: $63217.74
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║ Cloud Annual: $117139.28
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║ On-Prem Annual: $8134.50
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║ Annual Savings: $109004.77
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╚══════════════════════════════════════════════════════════════════╝
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TOP COST VEHICLES:
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VIN Cloud $ On-Prem $ Savings %
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─────────────────── ────────── ────────── ────────
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VCF1EBU27PG008191 6.84 0.48 93.0%
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VCF1ZBU28PG005207 6.84 0.48 93.0%
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VCF1ZBU29PG005488 6.84 0.48 93.0%
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VCF1ZBU26PG004962 6.76 0.48 93.0%
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VCF1EBU20PG008145 6.75 0.47 93.0%
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VIN CPU (mc) RAM (MB) Cloud $ On-Prem $ Savings %
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─────────────────── ──────── ──────── ────────── ────────── ────────
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VCF1UBU21PG008884 100 164 14.10 1.04 92.6%
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VCF1EBU24PG007242 100 164 14.10 1.04 92.6%
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VCF1ZBU29PG006267 100 164 14.10 1.04 92.6%
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VCF1EBU26PG007307 75 123 13.41 0.99 92.6%
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VCF1EBU22PG011967 50 82 12.77 0.93 92.8%
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...
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```
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*Note: Report generated 2026-02-03. Costs accumulate over time as the collector runs every 15 minutes.*
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*Note: Report generated 2026-02-05. Costs accumulate over time as the collector runs every 15 minutes.*
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## Configuration
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@@ -220,6 +220,16 @@ func GetReport(w http.ResponseWriter, r *http.Request) {
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║ On-Prem Cost: $%.2f
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║ Savings: $%.2f (%.1f%%)
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╠══════════════════════════════════════════════════════════════════╣
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║ RESOURCE USAGE MODEL ║
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║ ─────────────────────────────────────────────────────────────── ║
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║ Platform Base: %.0f cores / %.0f GB RAM (fixed)
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║ Per-VIN Marginal: %.0f millicores / %.0f MB RAM
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║ Total Fleet: %.1f cores / %.1f GB RAM
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╠══════════════════════════════════════════════════════════════════╣
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║ COST FORMULA ║
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║ ─────────────────────────────────────────────────────────────── ║
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║ (Platform Base) + (Per-VIN × %d VINs) + Managed Services
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╠══════════════════════════════════════════════════════════════════╣
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║ COST RATES ║
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║ ─────────────────────────────────────────────────────────────── ║
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║ Cloud: CPU $%.2f/core-hr Memory $%.3f/GB-hr
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@@ -237,12 +247,20 @@ func GetReport(w http.ResponseWriter, r *http.Request) {
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annualOnprem := summary.TotalOnpremCost * 12
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annualSavings := annualCloud - annualOnprem
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// Calculate total fleet resources
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totalCPU := services.PlatformBaseCPUCores + (services.PerVinCPUCores * float64(summary.VehicleCount))
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totalRAM := services.PlatformBaseMemoryGB + (services.PerVinMemoryGB * float64(summary.VehicleCount))
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fmt.Fprintf(w, report,
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from.Format("2006-01-02"), to.Format("2006-01-02"),
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summary.VehicleCount,
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summary.TotalCloudCost,
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summary.TotalOnpremCost,
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summary.TotalSavings, summary.SavingsPercent,
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services.PlatformBaseCPUCores, services.PlatformBaseMemoryGB,
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services.PerVinCPUCores*1000, services.PerVinMemoryGB*1024,
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totalCPU, totalRAM,
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summary.VehicleCount,
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services.CloudCPUPerCoreHour, services.CloudMemoryPerGBHour,
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services.OnpremCPUPerCoreHour, services.OnpremMemoryPerGBHour,
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services.BaseInfraCloudCost, services.BaseInfraOnpremCost,
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@@ -252,11 +270,11 @@ func GetReport(w http.ResponseWriter, r *http.Request) {
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// Add top cost VINs if any
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if len(summary.TopCostVins) > 0 {
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fmt.Fprintf(w, "\nTOP COST VEHICLES:\n")
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fmt.Fprintf(w, "%-20s %12s %12s %10s\n", "VIN", "Cloud $", "On-Prem $", "Savings %")
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fmt.Fprintf(w, "%-20s %12s %12s %10s\n", "───────────────────", "──────────", "──────────", "────────")
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fmt.Fprintf(w, "%-20s %10s %10s %12s %12s %10s\n", "VIN", "CPU (mc)", "RAM (MB)", "Cloud $", "On-Prem $", "Savings %")
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fmt.Fprintf(w, "%-20s %10s %10s %12s %12s %10s\n", "───────────────────", "────────", "────────", "──────────", "──────────", "────────")
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for _, v := range summary.TopCostVins {
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fmt.Fprintf(w, "%-20s %12.2f %12.2f %9.1f%%\n",
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truncateVIN(v.VIN), v.TotalCloudCost, v.TotalOnpremCost, v.SavingsPercent)
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fmt.Fprintf(w, "%-20s %10.0f %10.0f %12.2f %12.2f %9.1f%%\n",
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truncateVIN(v.VIN), v.AvgCPUCores*1000, v.AvgMemoryGB*1024, v.TotalCloudCost, v.TotalOnpremCost, v.SavingsPercent)
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}
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}
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}
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@@ -13,23 +13,29 @@ const (
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CloudCPUPerCoreHour = 0.30 // $/core/hour (Azure D-series + managed services + support)
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CloudMemoryPerGBHour = 0.08 // $/GB/hour (includes managed DB, Redis, caching layers)
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// On-prem costs (amortized hardware only)
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// On-prem/bare metal costs (amortized hardware only)
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// Assumes: 3-year hardware amortization, minimal ops overhead
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// Does NOT include: datacenter, power, cooling, staff, network
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OnpremCPUPerCoreHour = 0.02 // $/core/hour
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OnpremMemoryPerGBHour = 0.005 // $/GB/hour
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// Estimated resource usage per active VIN
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// Connected vehicle telemetry pipeline: ingestion → Kafka → processing → storage → APIs
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// Each active VIN requires dedicated processing capacity across the stack
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EstimatedCPUPerVin = 0.8 // 800 millicores per active VIN (realistic for full pipeline)
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EstimatedMemoryPerVin = 1.2 // 1.2GB per active VIN (buffers, state, caches, connections)
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// Per-VIN resource usage (marginal cost per vehicle)
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// This is the incremental CPU/RAM needed for each additional connected vehicle
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// Covers: telemetry ingestion, Kafka processing, storage writes, API queries
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PerVinCPUCores = 0.05 // 50 millicores per VIN (marginal)
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PerVinMemoryGB = 0.08 // 80MB per VIN (marginal)
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// Base infrastructure cost per collection interval (shared services)
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// Storage, Event Hubs, Defender, other fixed costs
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// ~$45k/month fixed = ~$10/15min
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BaseInfraCloudCost = 10.00 // $/15min for shared infra (cloud)
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BaseInfraOnpremCost = 0.90 // $/15min for shared infra (on-prem)
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// Platform base resources (fixed cost regardless of VIN count)
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// This is the minimum infrastructure to run the cloud services platform:
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// Kafka brokers, ClickHouse, MongoDB, Redis, PostgreSQL, gateway services, etc.
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PlatformBaseCPUCores = 176.0 // 176 cores for base platform
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PlatformBaseMemoryGB = 896.0 // 896GB RAM for base platform
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// Base infrastructure cost per collection interval (managed services, storage, networking)
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// Cloud: Event Hubs, CosmosDB, Azure Storage, Defender, monitoring (~$45k/month)
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// On-prem: ~75% cheaper - just hardware amortization for equivalent capacity
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BaseInfraCloudCost = 10.00 // $/15min for managed services (cloud)
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BaseInfraOnpremCost = 2.50 // $/15min for equivalent on-prem (75% cheaper)
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)
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// CalculateCosts computes cloud and on-prem costs for given resource usage
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@@ -43,6 +49,26 @@ func CalculateCosts(cpuCores, memoryGB float64, durationHours float64) (cloudCos
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return
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}
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// CalculatePlatformBaseCosts returns the fixed platform infrastructure costs
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// This is the cost to run the platform regardless of VIN count
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func CalculatePlatformBaseCosts(durationHours float64) (cloudCost, onpremCost float64) {
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cloudCost = (PlatformBaseCPUCores * CloudCPUPerCoreHour * durationHours) +
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(PlatformBaseMemoryGB * CloudMemoryPerGBHour * durationHours)
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onpremCost = (PlatformBaseCPUCores * OnpremCPUPerCoreHour * durationHours) +
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(PlatformBaseMemoryGB * OnpremMemoryPerGBHour * durationHours)
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return
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}
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// CalculatePerVinCosts returns the marginal cost for a single VIN
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func CalculatePerVinCosts(durationHours float64, activityMultiplier float64) (cpuCores, memoryGB, cloudCost, onpremCost float64) {
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cpuCores = PerVinCPUCores * activityMultiplier
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memoryGB = PerVinMemoryGB * activityMultiplier
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cloudCost, onpremCost = CalculateCosts(cpuCores, memoryGB, durationHours)
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return
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}
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// CalculateBaseCosts returns the shared infrastructure costs for a collection interval
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func CalculateBaseCosts() (cloudCost, onpremCost float64) {
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return BaseInfraCloudCost, BaseInfraOnpremCost
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@@ -79,12 +105,15 @@ func collectMetrics() {
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return
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}
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// Convert to cost records
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// Estimate resource usage based on message activity
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durationHours := 0.25 // 15 minutes = 0.25 hours
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records := make([]CostRecord, 0, len(activeVins))
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// Get base infrastructure costs and distribute across active VINs
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// Calculate platform base costs (distributed across all VINs for reporting)
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platformCloudCost, platformOnpremCost := CalculatePlatformBaseCosts(durationHours)
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platformCloudPerVin := platformCloudCost / float64(len(activeVins))
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platformOnpremPerVin := platformOnpremCost / float64(len(activeVins))
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// Get managed services base costs (also distributed)
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baseCloud, baseOnprem := CalculateBaseCosts()
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baseCloudPerVin := baseCloud / float64(len(activeVins))
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baseOnpremPerVin := baseOnprem / float64(len(activeVins))
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@@ -98,22 +127,20 @@ func collectMetrics() {
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activityMultiplier = 1.5
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}
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cpuCores := EstimatedCPUPerVin * activityMultiplier
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memoryGB := EstimatedMemoryPerVin * activityMultiplier
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// Calculate per-VIN marginal costs
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cpuCores, memoryGB, vinCloudCost, vinOnpremCost := CalculatePerVinCosts(durationHours, activityMultiplier)
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cloudCost, onpremCost := CalculateCosts(cpuCores, memoryGB, durationHours)
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// Add share of base infrastructure costs
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cloudCost += baseCloudPerVin
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onpremCost += baseOnpremPerVin
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// Total cost = per-VIN marginal + share of platform base + share of managed services
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totalCloudCost := vinCloudCost + platformCloudPerVin + baseCloudPerVin
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totalOnpremCost := vinOnpremCost + platformOnpremPerVin + baseOnpremPerVin
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records = append(records, CostRecord{
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VIN: v.VIN,
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Timestamp: to,
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CPUCores: cpuCores,
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MemoryGB: memoryGB,
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CloudCostUSD: cloudCost,
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OnpremCostUSD: onpremCost,
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CloudCostUSD: totalCloudCost,
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OnpremCostUSD: totalOnpremCost,
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PodCount: 1,
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})
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}
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@@ -124,5 +151,6 @@ func collectMetrics() {
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return
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}
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logger.Info().Msgf("Collected and stored cost metrics for %d VINs", len(records))
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logger.Info().Msgf("Collected and stored cost metrics for %d VINs (platform base: cloud $%.2f, on-prem $%.2f)",
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len(records), platformCloudCost, platformOnpremCost)
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}
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Reference in New Issue
Block a user