Build a lightweight, self-hosted Supply-Side Platform (SSP) to monetize web and CTV/OTT video inventory via server-to-server header bidding, starting with Magnite and FreeWheel, with transparent reporting and reconciliation. Reduce reliance on third-party ad servers while maintaining operational simplicity.
- Start small, scale deliberately: MVP supports ≤500 RPS; architecture is horizontally scalable but not over-engineered.
- Own the stack: Self-hosted Prebid Server for full control over auction logic and data.
- Video-first: VAST 3.0/4.0 compliant; bid caching enabled.
- Reconciliation-ready: Reporting prioritizes accuracy over real-time speed (5–60 min latency acceptable).
- Future-proof: No architectural blockers for display, mobile, or direct campaigns later.
| Capability Area | MVP | Post-MVP |
|---|---|---|
| Inventory Formats | Web video, CTV/OTT (VAST) | Mobile app, display, audio |
| Demand Sources | Magnite, FreeWheel (RTB only) | Additional DSPs, direct-sold campaigns, PMPs |
| Auction Logic | Static first-price auction, fixed timeouts/floors | Dynamic floors, priority rules, hybrid auctions |
| Compliance | ads.txt, sellers.json, SCA propagation | Advanced privacy controls, consent-aware routing |
| User Data & Targeting | None (pass-through IDs only) | Segmentation, blacklists, supply/DSP-specific rules |
| Tracking & Measurement | Basic PBS logging | Unified event endpoint, pixel multiplexing, dynamic pixel injection |
| Reporting | Hourly aggregates + partner reconciliation | Near-real-time KPIs, anomaly detection, attribution-ready |
| Optimization | Manual configuration | Automated KPI-driven tuning (eCPM, sellout, CPA, etc.) |
| Component Name | Function | MVP? | Readymade Solutions (GCP / AWS) | Approx. Workload for Delivery |
|---|---|---|---|---|
| Inventory Gateway | Accepts VAST/Player requests; normalizes to OpenRTB; injects metadata & schain | ✅ Yes | Cloud Run (GCP) / Lambda + API Gateway (AWS) | 1 week |
| Prebid Server | Orchestrates S2S auction with demand partners; handles bid caching, timeouts, SCA | ✅ Yes | Self-hosted on GKE (GCP) / EKS or Fargate (AWS) | 1 week (config + deploy) |
| Log Storage | Stores raw PBS request/response logs for reporting | ✅ Yes | Cloud Storage (GCP) / S3 (AWS) | <1 day |
| Batch ETL Pipeline | Ingests logs hourly; pulls partner reports; enriches & loads into DWH | ✅ Yes | Cloud Dataflow / Composer (GCP) / Glue + EventBridge (AWS) | 2 weeks |
| Data Warehouse (DWH) | Central store for aggregated metrics (impressions, wins, eCPM, etc.) | ✅ Yes | BigQuery (GCP) / Redshift or Athena (AWS) | <1 day (provisioning) |
| Reconciliation Dashboard | Visualizes internal vs. partner-reported metrics | ✅ Yes | Looker (GCP) / QuickSight or Metabase on EC2 (AWS) / Self-hosted Grafana | 1 week |
| SSP Configuration UI | Web interface to manage demand partners, bid floors, timeouts, schain settings, and reporting views | ❌ No | — | 4–6 weeks (Post-MVP) |
| CDP (Customer Data Platform) | Hosts user segments, blacklists; applies rules per supply/DSP | ❌ No | — | 4–6 weeks (Post-MVP) |
| Event Tracking Service | Aggregates tracking pixels; exposes unified /track endpoint; injects pixels into ad markup; |
❌ No | — | 3–5 weeks (Post-MVP) |
| Post-Optimization Service | Analyzes performance data; auto-adjusts floors, weights, campaign settings | ❌ No | — | 4–6 weeks (Post-MVP) |
| Direct Campaign DB | Stores direct-sold line items, targeting rules, priorities | ❌ No | Firestore / Cloud SQL (GCP) / DynamoDB / RDS (AWS) | 2–3 weeks (Post-MVP) |
| Compliance Assets | Hosts ads.txt, sellers.json, schain validation |
✅ Yes | Cloud CDN / Firebase Hosting (GCP) / S3 + CloudFront (AWS) | <1 day |
- All MVP components can be built with minimal custom code (mostly config + light glue logic).
- Post-MVP services require custom development but can leverage cloud-managed backends (e.g., DWH, auth, queues).
- Total MVP engineering effort: ~4 weeks
- While not strictly required for MVP (configs can be managed via YAML/CI), a configuration UI becomes essential for managing dynamic floors, DSP routing, direct campaigns and optimization rules. It’s best prioritized in early Post-MVP (Phase 2) to avoid operational bottlenecks.
graph LR
A[[Supply Side Player/SDK]] ==>|Bid Request| B
B ==> O
O((Ad Template)) ==> |Bid Response| A
A -.->|Event Tracking|M
M --->|Inject Pixels| O
subgraph "Core SSP (MVP)"
B(Inventory Gateway)
B <==>|OpenRTB| C[Prebid Server]
C -->|Structured Logs| F[("Log Storage")]
end
subgraph "External"
D[Magnite]
E[FreeWheel]
N[Verificators]
I[Magnite API]
J[FreeWheel API]
C --> D
C --> E
end
subgraph "Reporting & Ops (MVP)"
F --> G[Batch ETL]
G --> H[("DWH")]
I --> G
J --> G
H --> K[Reconciliation Dashboard]
end
subgraph "Post-MVP"
L[Custom CDP]
Q[(Campaign Database)]
B <--> |User Context|L
L -->|Segments/Blacklists| C
C -->|Win Notification| M[Event Tracking Service]
C --> |Direct Sales/PMP| Q
M --> |Session-based Analytics| H
M -.->|Multiplex| D
M -.->|Multiplex| E
M -.->|Multiplex| N
H --> P[Post-Optimization Service]
P -->|Dynamic Floors / Rules| C
P -->|Campaign Settings| Q
end
classDef mvp fill:#000,stroke:#2e8b57;
classDef future fill:#555,stroke:#1e5dbf;
class B,C,D,E,F,G,H,I,J,K,O mvp
class L,M,N,P,Q future
- Green (MVP): Components required for the initial launch.
- Blue (Post-MVP): The intelligence and control layer that enables advanced optimization, privacy-safe tracking, and data-driven decisioning.
- The Inventory Gateway acts as the central entry point, enriching requests with context from the CDP.
- The Event Tracking Service decouples your supply from vendor-specific tracking, giving you control and auditability.
- The Post-Optimization Service closes the loop by using reporting data to automatically tune the auction
- Provides battle-tested OpenRTB auction logic, concurrency, and timeout handling.
- Avoids rebuilding core RTB infrastructure (saves 3–6 months of dev effort).
- Enables rapid integration with future demand partners via community adapters.
- Fully inspectable and auditable—aligns with transparency goals.
✅ No custom code in MVP if standard Prebid adapters exist. If not, minimal adapter development (~1–2 weeks).
| Risk | Impact | Likelihood | Mitigation Strategy |
|---|---|---|---|
| Demand partner lacks an adapter for PBS | Delivery delayed by 1-2 weeks; revenue impact | Low-Medium | • Audit adapter availability immediately • Budget 2 weeks for custom adapter dev • Use generic OpenRTB adapter as fallback if endpoints are standard |
| Reporting reconciliation mismatches (internal vs. partner numbers diverge) |
Erodes trust; difficulties in KPI fulfilment | High | • Define canonical event schema early (impression = win + visible render) • Use partner-reported spend as source of truth • Invest into data analyst early to localize discrepancies |
| Autoscaling failure under unexpected traffic spike (e.g., viral CTV content → 2K+ RPS) |
Revenue loss, latency spikes, auction timeouts | Medium | • Design stateless, containerized PBS deployment • Keep some extra resources online during peak hours • Use load testing pre-launch • Add circuit breaker: degrade gracefully (e.g., disable slow bidders) |
| Understaffing post-MVP (especially UI or data engineers) |
Post-MVP features stall; tech debt accumulates; accidents in production might cost a fortune | High | • Hire or assign at least 1 full-stack engineer with UI experience during MVP planning • Prioritize UI early (even basic CRUD) to avoid config-as-code bottleneck • Consider low-code dashboards (Metabase, Retool) to reduce frontend load |
| Lack of observability in production (can’t debug low fill rates or latency) |
Slow incident response; poor optimization | High | • Enforce structured JSON logging from Day 1 • Monitor key SLOs: p95 latency <800ms, error rate <1% • Setup alerting for important KPIs and partner bid response rates |
| Video-specific issues (VAST parsing errors, creative validation, player timeouts) |
Poor user experience; impression losses | Medium | • Validate VAST responses in gateway • Consider PBS bid caching for video • Set conservative timeouts (≤600ms for auction) |
| Compliance gaps (missing schain, invalid sellers.json) |
Bids rejected by premium DSPs | Low | • Automate schain propagation in PBS • Validate everything via IAB toolset • Include compliance checks in CI pipeline |
| Over-reliance on open-source Prebid Server without upgrade path | Security/feature stagnation, costly innovations | Medium | • Pin to stable PBS version but monitor releases • Avoid deep forks; use config/hooks for customization • Allocate 10% engineering time for dependency updates • Identify essential requirements after 6-12 months of operations and consider building a custom replacement |
| Business success outpaces infrastructure (e.g., 10x traffic in 3 months) |
System instability, missed revenue | Medium | • Choose cloud-native, elastic services from Day 1 (no VMs) • Design log pipeline, CDP and pixel multiplexing and to scale independently • Establish cost-per-RPS monitoring to forecast budget needs |
| No clear ownership of optimization logic (who tunes floors? who defines KPIs?) |
Post-MVP optimization stalls, more traction with partners | High | • Define product/engineering partnership model early • Start with simple rule engine (e.g., “raise floor if fill >90%”) before heavy-duty automation |
- Hiring risk is real: UI and/or Ops debt will cripple iteration speed
- Scale surprises are typical in OLV: A single popular title can spike CTV traffic 10–100x. Need to design failsafes or overspend on infrastructure.
- Reconciliation is expensive: to efficiently and timely resolve problems causing irregularities, having a relevant competency on the team is a must
- Start observability early: Debugging RTB issues without logs/metrics is a hopeless gamble
- Team:
- 1 BE Engineer
- 1 DevOps / Data Engineer (shared)
- MVP TTM: 4 weeks
- Week 1: Deploy & configure Prebid Server + gateway
- Week 2: Integrate demand partners + logging
- Weeks 3-4: Reporting pipeline + reconciliation validation
- Post-MVP Team :
- 2 BE Engineers
- 1 FE / Full-Stack Engineer
- 1 DevOps / Data Engineer
- 1 Data Engineer / Data Analyst