Technology
MinusOneDB for SSP's
High-Performance Inventory Intelligence & Optimization Foundation
The SSP's Challenge: Maximizing Yield in a Dynamic Landscape
Today's Supply-Side Platforms face intensifying pressures:
Identity fragmentation: Cookie deprecation and device ID restrictions threatening core matching capabilities
Publisher expectations: Demands for higher yield, better analytics, and unique demand access
Demand partner complexity: Managing large numbers of DSP integrations with varying capabilities
Infrastructure cost explosion: Scaling infrastructure to handle peak traffic while managing costs
The reality gap: While SSPs possess enormous potential for value creation, most cannot fully leverage their scale and position due to infrastructure limitations. Current cloud providers extract a massive compute tax from query-heavy workloads - at a typical floor price of $10/query/terabyte, even basic auction analytics rapidly become prohibitively expensive, with a single query on a petabybe costing $10,000.
What is an average per-query cost estimate we are comfortable with?
How widely do we want to democratize access to our data? Which people and AI systems should be able to query it?
How many queries would we like to be running in year 1, 2, 3?
What questions do we want to ask of the data?
Project the implications in detail in this cost calculator.
MinusOneDB: Unique Architectural Benefits for SSP's
MinusOneDB isn't another point solution. It's a foundation layer parallel database that collapses the warehouse, lake, stream processor, feature store, and queue into one rebuilt distributed‑search datastore that is 1000x more efficient per query on a price/performance basis. Our engineering team rebuilt storage from first principles to create a system where indexed storage, rather than compute, bears the bulk of the query workload.
Constant-time searches – Our rebuilt distributed search architecture enables traversing petabytes in <10 ms through optimized index structure.
True streaming ingest – Each write, including from streaming, is index-visible within ~1 second; no micro‑batch lag or complicated ETL pipelines.
Deterministic rebuild – Any dataset at any scale, can be rebuilt from object store in <3 hours—essential for disaster recovery, DevOps at scale, and data sovereignty.
Capacity‑based pricing – You lease infrastructure, not queries—with costs 80-95% lower than pay-per-query warehouses ($2-10/TB/per query for traditional warehouses vs. our flat capacity model).
Our architecture delivers eventually consistent semantics (typically under 1 second), which is sufficient for most workloads. For true ACID requirements, we offer integration with transactional stores for those use cases while maintaining our performance advantages for query-driven workloads.
SSP Pain Points
MinusOneDB’s performance leap and streamlining of data engineering has the potential to power solutions to a diverse array of SSP pain points. Exploring them:
Core value-prop pressure point for SSPs | Why it hurts in 2025-style programmatic | Where / how MinusOneDB can play |
“Query-tax” costs that explode with scale | Per-query billing in Snowflake, BigQuery, Databricks, etc. turns every new QPS spike into P&L pain; a single mis-tuned query can be a five-figure surprise. | Capacity-based pricing and near-flat-time queries at any size |
Heavy ETL & multi-system sprawl | Kafka → ClickHouse for hot data, S3 → Snowflake for cold, Redis for sessions … each hop adds seconds of lag and DevOps head-count. | Four built-in stores (search, session, lake, archive) behind one API erase most ETL and let a lean team run what used to take multiple specialties. |
Stale or broken identity graphs | Nightly batch resolution drops split/merged cookies; audience accuracy decays and hurts frequency capping and yield. | Real-time identity resolution keeps a live identity object with lineage and improves effectiveness |
Partner clean-rooms that throttle usage | Snowflake/Databricks cross-domain queries are metered, so teams ration the very analyses that uncover value; buyers balk at “usage-based surprises.” | Federated “bring-compute-to-data” clean-room lets partners land once in S3 and run cross-domain filters inside an m1db node—no runaway bill, no lock-in. |
Slow feature velocity | Every new targeting dimension triggers schema work, back-fills and re-indexing, delaying product launches by months. | REST/JSON APIs let teams prototype directly on prod data |
Yield leakage from “lazy floors” & weak bid-density forecasts | Rule-of-thumb floors clear inventory too cheaply or leave impressions unsold. | Run per-impression pricing models on live signals without taxing the cluster; dynamic floors recapture CPMs. |
Cross-channel convergence (CTV, retail media, DOOH) | Separate IDs, schemas and viewability rules block holistic frequency & yield management. | Land VAST beacons, scanner receipts and web auctions together; cross-channel queries run in one pass |
Publisher trust & need for real-time transparency | Sellers want dashboards that update literally as auctions settle; metered warehouses make that cost-prohibitive. | No per-query fees mean you can expose second-by-second “glass-box” consoles without bill shock. |
Instant inventory forecasting for Programmatic Guaranteed & CTV pods | Sales teams need second-by-second forecasts to reserve impressions across devices and pod positions; traditional nightly Spark jobs miss fast-moving CTV supply. | Forecast queries are just high-cardinality filters and aggregations—M1DB executes them on the live denormalised doc set, combined with detailed machine learning models executed on the historical datasets. |
Carbon-footprint accounting & green media buys | Brands attach CO₂ budgets to campaigns; SSPs must show grams per 1 000 impressions. | Store energy-meter logs next to delivery data; surface per-campaign emissions without a separate ESG warehouse. |
The Technical Foundation: ModelForge & IdentityForge
MinusOneDB's SSP solution is powered by two revolutionary implementations that we adapt to your architecture:
ModelForge: Unlocking Hyperscale AutoML & Continuous Model Scoring
Because writes are equivalently inexpensive and performant to reads inside MinusOneDB, workloads like autoML that currently are limited by write throughput at scale become financially and computationally feasible.
Continuous model evolution: Score, compare, and evolve thousands of model variants nightly
Rapid feature engineering: Test new signals across your entire feature store without pre-planning
Real-time feedback loops: Update scores as new data arrives, not during overnight batch windows
Reduced time-to-production: Deploy many new models per day in minutes to hours rather than weeks of infrastructure work
Comprehensive model lineage: Maintain version history, variant performance, and governance audit trails
IdentityForge: Enabling Real-Time Identity Resolution
Resolving a stream of update data to profiles is currently usually a batch job because of the computational and financial expense - but not in MinusOneDB, where it can be resolved to profiles or identities in real time, and immediately be available for querying - bringing live data analytics on large datasets, anomaly detection, audience updating and more within reach.
Continuous identity graph: Process billions of identity signals in real-time for optimal matching
Cross-device connectivity: Link users across browsers, apps, and devices in milliseconds
Alternative identifiers: Can integrate UID 2.0, RampID, and other solutions
Probabilistic modeling: Maintain reach and targeting through privacy-safe techniques
Privacy-first architecture: Enforce consent and compliance across all operations
Sell Side Benefits of MinusOneDB
For Revenue Teams:
Superior yield performance through algorithmic optimization
Better competitive positioning via unique publisher value proposition
Higher-value inventory packaging through enriched contextual intelligence
Improved demand partner relationships through data-driven insights
For Product Teams:
Faster feature delivery through simplified architecture
More comprehensive analytics without sampling or aggregation
Enhanced publisher tools leveraging real-time data processing
Future-proof identity strategy resilient to regulatory changes
For Engineering Teams:
Reduced infrastructure complexity through unified solution
Predictable scalability through capacity-based pricing
Lower operational overhead with self-healing architecture
Superior performance without specialized optimization
For Publisher Relations Teams:
Better publisher retention through demonstrably higher yield
Enhanced reporting capabilities with real-time insights
More compelling value proposition versus competing SSPs
Higher-value publisher partnerships through data collaboration
Get Started Today
Contact: info@minusonedb.com
Transform your infrastructure from a technical challenge to a competitive advantage.
High-Performance Inventory Intelligence & Optimization Foundation
The SSP's Challenge: Maximizing Yield in a Dynamic Landscape
Today's Supply-Side Platforms face intensifying pressures:
Identity fragmentation: Cookie deprecation and device ID restrictions threatening core matching capabilities
Publisher expectations: Demands for higher yield, better analytics, and unique demand access
Demand partner complexity: Managing large numbers of DSP integrations with varying capabilities
Infrastructure cost explosion: Scaling infrastructure to handle peak traffic while managing costs
The reality gap: While SSPs possess enormous potential for value creation, most cannot fully leverage their scale and position due to infrastructure limitations. Current cloud providers extract a massive compute tax from query-heavy workloads - at a typical floor price of $10/query/terabyte, even basic auction analytics rapidly become prohibitively expensive, with a single query on a petabybe costing $10,000.
What is an average per-query cost estimate we are comfortable with?
How widely do we want to democratize access to our data? Which people and AI systems should be able to query it?
How many queries would we like to be running in year 1, 2, 3?
What questions do we want to ask of the data?
Project the implications in detail in this cost calculator.
MinusOneDB: Unique Architectural Benefits for SSP's
MinusOneDB isn't another point solution. It's a foundation layer parallel database that collapses the warehouse, lake, stream processor, feature store, and queue into one rebuilt distributed‑search datastore that is 1000x more efficient per query on a price/performance basis. Our engineering team rebuilt storage from first principles to create a system where indexed storage, rather than compute, bears the bulk of the query workload.
Constant-time searches – Our rebuilt distributed search architecture enables traversing petabytes in <10 ms through optimized index structure.
True streaming ingest – Each write, including from streaming, is index-visible within ~1 second; no micro‑batch lag or complicated ETL pipelines.
Deterministic rebuild – Any dataset at any scale, can be rebuilt from object store in <3 hours—essential for disaster recovery, DevOps at scale, and data sovereignty.
Capacity‑based pricing – You lease infrastructure, not queries—with costs 80-95% lower than pay-per-query warehouses ($2-10/TB/per query for traditional warehouses vs. our flat capacity model).
Our architecture delivers eventually consistent semantics (typically under 1 second), which is sufficient for most workloads. For true ACID requirements, we offer integration with transactional stores for those use cases while maintaining our performance advantages for query-driven workloads.
SSP Pain Points
MinusOneDB’s performance leap and streamlining of data engineering has the potential to power solutions to a diverse array of SSP pain points. Exploring them:
Core value-prop pressure point for SSPs | Why it hurts in 2025-style programmatic | Where / how MinusOneDB can play |
“Query-tax” costs that explode with scale | Per-query billing in Snowflake, BigQuery, Databricks, etc. turns every new QPS spike into P&L pain; a single mis-tuned query can be a five-figure surprise. | Capacity-based pricing and near-flat-time queries at any size |
Heavy ETL & multi-system sprawl | Kafka → ClickHouse for hot data, S3 → Snowflake for cold, Redis for sessions … each hop adds seconds of lag and DevOps head-count. | Four built-in stores (search, session, lake, archive) behind one API erase most ETL and let a lean team run what used to take multiple specialties. |
Stale or broken identity graphs | Nightly batch resolution drops split/merged cookies; audience accuracy decays and hurts frequency capping and yield. | Real-time identity resolution keeps a live identity object with lineage and improves effectiveness |
Partner clean-rooms that throttle usage | Snowflake/Databricks cross-domain queries are metered, so teams ration the very analyses that uncover value; buyers balk at “usage-based surprises.” | Federated “bring-compute-to-data” clean-room lets partners land once in S3 and run cross-domain filters inside an m1db node—no runaway bill, no lock-in. |
Slow feature velocity | Every new targeting dimension triggers schema work, back-fills and re-indexing, delaying product launches by months. | REST/JSON APIs let teams prototype directly on prod data |
Yield leakage from “lazy floors” & weak bid-density forecasts | Rule-of-thumb floors clear inventory too cheaply or leave impressions unsold. | Run per-impression pricing models on live signals without taxing the cluster; dynamic floors recapture CPMs. |
Cross-channel convergence (CTV, retail media, DOOH) | Separate IDs, schemas and viewability rules block holistic frequency & yield management. | Land VAST beacons, scanner receipts and web auctions together; cross-channel queries run in one pass |
Publisher trust & need for real-time transparency | Sellers want dashboards that update literally as auctions settle; metered warehouses make that cost-prohibitive. | No per-query fees mean you can expose second-by-second “glass-box” consoles without bill shock. |
Instant inventory forecasting for Programmatic Guaranteed & CTV pods | Sales teams need second-by-second forecasts to reserve impressions across devices and pod positions; traditional nightly Spark jobs miss fast-moving CTV supply. | Forecast queries are just high-cardinality filters and aggregations—M1DB executes them on the live denormalised doc set, combined with detailed machine learning models executed on the historical datasets. |
Carbon-footprint accounting & green media buys | Brands attach CO₂ budgets to campaigns; SSPs must show grams per 1 000 impressions. | Store energy-meter logs next to delivery data; surface per-campaign emissions without a separate ESG warehouse. |
The Technical Foundation: ModelForge & IdentityForge
MinusOneDB's SSP solution is powered by two revolutionary implementations that we adapt to your architecture:
ModelForge: Unlocking Hyperscale AutoML & Continuous Model Scoring
Because writes are equivalently inexpensive and performant to reads inside MinusOneDB, workloads like autoML that currently are limited by write throughput at scale become financially and computationally feasible.
Continuous model evolution: Score, compare, and evolve thousands of model variants nightly
Rapid feature engineering: Test new signals across your entire feature store without pre-planning
Real-time feedback loops: Update scores as new data arrives, not during overnight batch windows
Reduced time-to-production: Deploy many new models per day in minutes to hours rather than weeks of infrastructure work
Comprehensive model lineage: Maintain version history, variant performance, and governance audit trails
IdentityForge: Enabling Real-Time Identity Resolution
Resolving a stream of update data to profiles is currently usually a batch job because of the computational and financial expense - but not in MinusOneDB, where it can be resolved to profiles or identities in real time, and immediately be available for querying - bringing live data analytics on large datasets, anomaly detection, audience updating and more within reach.
Continuous identity graph: Process billions of identity signals in real-time for optimal matching
Cross-device connectivity: Link users across browsers, apps, and devices in milliseconds
Alternative identifiers: Can integrate UID 2.0, RampID, and other solutions
Probabilistic modeling: Maintain reach and targeting through privacy-safe techniques
Privacy-first architecture: Enforce consent and compliance across all operations
Sell Side Benefits of MinusOneDB
For Revenue Teams:
Superior yield performance through algorithmic optimization
Better competitive positioning via unique publisher value proposition
Higher-value inventory packaging through enriched contextual intelligence
Improved demand partner relationships through data-driven insights
For Product Teams:
Faster feature delivery through simplified architecture
More comprehensive analytics without sampling or aggregation
Enhanced publisher tools leveraging real-time data processing
Future-proof identity strategy resilient to regulatory changes
For Engineering Teams:
Reduced infrastructure complexity through unified solution
Predictable scalability through capacity-based pricing
Lower operational overhead with self-healing architecture
Superior performance without specialized optimization
For Publisher Relations Teams:
Better publisher retention through demonstrably higher yield
Enhanced reporting capabilities with real-time insights
More compelling value proposition versus competing SSPs
Higher-value publisher partnerships through data collaboration
Get Started Today
Contact: info@minusonedb.com
Transform your infrastructure from a technical challenge to a competitive advantage.
High-Performance Inventory Intelligence & Optimization Foundation
The SSP's Challenge: Maximizing Yield in a Dynamic Landscape
Today's Supply-Side Platforms face intensifying pressures:
Identity fragmentation: Cookie deprecation and device ID restrictions threatening core matching capabilities
Publisher expectations: Demands for higher yield, better analytics, and unique demand access
Demand partner complexity: Managing large numbers of DSP integrations with varying capabilities
Infrastructure cost explosion: Scaling infrastructure to handle peak traffic while managing costs
The reality gap: While SSPs possess enormous potential for value creation, most cannot fully leverage their scale and position due to infrastructure limitations. Current cloud providers extract a massive compute tax from query-heavy workloads - at a typical floor price of $10/query/terabyte, even basic auction analytics rapidly become prohibitively expensive, with a single query on a petabybe costing $10,000.
What is an average per-query cost estimate we are comfortable with?
How widely do we want to democratize access to our data? Which people and AI systems should be able to query it?
How many queries would we like to be running in year 1, 2, 3?
What questions do we want to ask of the data?
Project the implications in detail in this cost calculator.
MinusOneDB: Unique Architectural Benefits for SSP's
MinusOneDB isn't another point solution. It's a foundation layer parallel database that collapses the warehouse, lake, stream processor, feature store, and queue into one rebuilt distributed‑search datastore that is 1000x more efficient per query on a price/performance basis. Our engineering team rebuilt storage from first principles to create a system where indexed storage, rather than compute, bears the bulk of the query workload.
Constant-time searches – Our rebuilt distributed search architecture enables traversing petabytes in <10 ms through optimized index structure.
True streaming ingest – Each write, including from streaming, is index-visible within ~1 second; no micro‑batch lag or complicated ETL pipelines.
Deterministic rebuild – Any dataset at any scale, can be rebuilt from object store in <3 hours—essential for disaster recovery, DevOps at scale, and data sovereignty.
Capacity‑based pricing – You lease infrastructure, not queries—with costs 80-95% lower than pay-per-query warehouses ($2-10/TB/per query for traditional warehouses vs. our flat capacity model).
Our architecture delivers eventually consistent semantics (typically under 1 second), which is sufficient for most workloads. For true ACID requirements, we offer integration with transactional stores for those use cases while maintaining our performance advantages for query-driven workloads.
SSP Pain Points
MinusOneDB’s performance leap and streamlining of data engineering has the potential to power solutions to a diverse array of SSP pain points. Exploring them:
Core value-prop pressure point for SSPs | Why it hurts in 2025-style programmatic | Where / how MinusOneDB can play |
“Query-tax” costs that explode with scale | Per-query billing in Snowflake, BigQuery, Databricks, etc. turns every new QPS spike into P&L pain; a single mis-tuned query can be a five-figure surprise. | Capacity-based pricing and near-flat-time queries at any size |
Heavy ETL & multi-system sprawl | Kafka → ClickHouse for hot data, S3 → Snowflake for cold, Redis for sessions … each hop adds seconds of lag and DevOps head-count. | Four built-in stores (search, session, lake, archive) behind one API erase most ETL and let a lean team run what used to take multiple specialties. |
Stale or broken identity graphs | Nightly batch resolution drops split/merged cookies; audience accuracy decays and hurts frequency capping and yield. | Real-time identity resolution keeps a live identity object with lineage and improves effectiveness |
Partner clean-rooms that throttle usage | Snowflake/Databricks cross-domain queries are metered, so teams ration the very analyses that uncover value; buyers balk at “usage-based surprises.” | Federated “bring-compute-to-data” clean-room lets partners land once in S3 and run cross-domain filters inside an m1db node—no runaway bill, no lock-in. |
Slow feature velocity | Every new targeting dimension triggers schema work, back-fills and re-indexing, delaying product launches by months. | REST/JSON APIs let teams prototype directly on prod data |
Yield leakage from “lazy floors” & weak bid-density forecasts | Rule-of-thumb floors clear inventory too cheaply or leave impressions unsold. | Run per-impression pricing models on live signals without taxing the cluster; dynamic floors recapture CPMs. |
Cross-channel convergence (CTV, retail media, DOOH) | Separate IDs, schemas and viewability rules block holistic frequency & yield management. | Land VAST beacons, scanner receipts and web auctions together; cross-channel queries run in one pass |
Publisher trust & need for real-time transparency | Sellers want dashboards that update literally as auctions settle; metered warehouses make that cost-prohibitive. | No per-query fees mean you can expose second-by-second “glass-box” consoles without bill shock. |
Instant inventory forecasting for Programmatic Guaranteed & CTV pods | Sales teams need second-by-second forecasts to reserve impressions across devices and pod positions; traditional nightly Spark jobs miss fast-moving CTV supply. | Forecast queries are just high-cardinality filters and aggregations—M1DB executes them on the live denormalised doc set, combined with detailed machine learning models executed on the historical datasets. |
Carbon-footprint accounting & green media buys | Brands attach CO₂ budgets to campaigns; SSPs must show grams per 1 000 impressions. | Store energy-meter logs next to delivery data; surface per-campaign emissions without a separate ESG warehouse. |
The Technical Foundation: ModelForge & IdentityForge
MinusOneDB's SSP solution is powered by two revolutionary implementations that we adapt to your architecture:
ModelForge: Unlocking Hyperscale AutoML & Continuous Model Scoring
Because writes are equivalently inexpensive and performant to reads inside MinusOneDB, workloads like autoML that currently are limited by write throughput at scale become financially and computationally feasible.
Continuous model evolution: Score, compare, and evolve thousands of model variants nightly
Rapid feature engineering: Test new signals across your entire feature store without pre-planning
Real-time feedback loops: Update scores as new data arrives, not during overnight batch windows
Reduced time-to-production: Deploy many new models per day in minutes to hours rather than weeks of infrastructure work
Comprehensive model lineage: Maintain version history, variant performance, and governance audit trails
IdentityForge: Enabling Real-Time Identity Resolution
Resolving a stream of update data to profiles is currently usually a batch job because of the computational and financial expense - but not in MinusOneDB, where it can be resolved to profiles or identities in real time, and immediately be available for querying - bringing live data analytics on large datasets, anomaly detection, audience updating and more within reach.
Continuous identity graph: Process billions of identity signals in real-time for optimal matching
Cross-device connectivity: Link users across browsers, apps, and devices in milliseconds
Alternative identifiers: Can integrate UID 2.0, RampID, and other solutions
Probabilistic modeling: Maintain reach and targeting through privacy-safe techniques
Privacy-first architecture: Enforce consent and compliance across all operations
Sell Side Benefits of MinusOneDB
For Revenue Teams:
Superior yield performance through algorithmic optimization
Better competitive positioning via unique publisher value proposition
Higher-value inventory packaging through enriched contextual intelligence
Improved demand partner relationships through data-driven insights
For Product Teams:
Faster feature delivery through simplified architecture
More comprehensive analytics without sampling or aggregation
Enhanced publisher tools leveraging real-time data processing
Future-proof identity strategy resilient to regulatory changes
For Engineering Teams:
Reduced infrastructure complexity through unified solution
Predictable scalability through capacity-based pricing
Lower operational overhead with self-healing architecture
Superior performance without specialized optimization
For Publisher Relations Teams:
Better publisher retention through demonstrably higher yield
Enhanced reporting capabilities with real-time insights
More compelling value proposition versus competing SSPs
Higher-value publisher partnerships through data collaboration
Get Started Today
Contact: info@minusonedb.com
Transform your infrastructure from a technical challenge to a competitive advantage.
High-Performance Inventory Intelligence & Optimization Foundation
The SSP's Challenge: Maximizing Yield in a Dynamic Landscape
Today's Supply-Side Platforms face intensifying pressures:
Identity fragmentation: Cookie deprecation and device ID restrictions threatening core matching capabilities
Publisher expectations: Demands for higher yield, better analytics, and unique demand access
Demand partner complexity: Managing large numbers of DSP integrations with varying capabilities
Infrastructure cost explosion: Scaling infrastructure to handle peak traffic while managing costs
The reality gap: While SSPs possess enormous potential for value creation, most cannot fully leverage their scale and position due to infrastructure limitations. Current cloud providers extract a massive compute tax from query-heavy workloads - at a typical floor price of $10/query/terabyte, even basic auction analytics rapidly become prohibitively expensive, with a single query on a petabybe costing $10,000.
What is an average per-query cost estimate we are comfortable with?
How widely do we want to democratize access to our data? Which people and AI systems should be able to query it?
How many queries would we like to be running in year 1, 2, 3?
What questions do we want to ask of the data?
Project the implications in detail in this cost calculator.
MinusOneDB: Unique Architectural Benefits for SSP's
MinusOneDB isn't another point solution. It's a foundation layer parallel database that collapses the warehouse, lake, stream processor, feature store, and queue into one rebuilt distributed‑search datastore that is 1000x more efficient per query on a price/performance basis. Our engineering team rebuilt storage from first principles to create a system where indexed storage, rather than compute, bears the bulk of the query workload.
Constant-time searches – Our rebuilt distributed search architecture enables traversing petabytes in <10 ms through optimized index structure.
True streaming ingest – Each write, including from streaming, is index-visible within ~1 second; no micro‑batch lag or complicated ETL pipelines.
Deterministic rebuild – Any dataset at any scale, can be rebuilt from object store in <3 hours—essential for disaster recovery, DevOps at scale, and data sovereignty.
Capacity‑based pricing – You lease infrastructure, not queries—with costs 80-95% lower than pay-per-query warehouses ($2-10/TB/per query for traditional warehouses vs. our flat capacity model).
Our architecture delivers eventually consistent semantics (typically under 1 second), which is sufficient for most workloads. For true ACID requirements, we offer integration with transactional stores for those use cases while maintaining our performance advantages for query-driven workloads.
SSP Pain Points
MinusOneDB’s performance leap and streamlining of data engineering has the potential to power solutions to a diverse array of SSP pain points. Exploring them:
Core value-prop pressure point for SSPs | Why it hurts in 2025-style programmatic | Where / how MinusOneDB can play |
“Query-tax” costs that explode with scale | Per-query billing in Snowflake, BigQuery, Databricks, etc. turns every new QPS spike into P&L pain; a single mis-tuned query can be a five-figure surprise. | Capacity-based pricing and near-flat-time queries at any size |
Heavy ETL & multi-system sprawl | Kafka → ClickHouse for hot data, S3 → Snowflake for cold, Redis for sessions … each hop adds seconds of lag and DevOps head-count. | Four built-in stores (search, session, lake, archive) behind one API erase most ETL and let a lean team run what used to take multiple specialties. |
Stale or broken identity graphs | Nightly batch resolution drops split/merged cookies; audience accuracy decays and hurts frequency capping and yield. | Real-time identity resolution keeps a live identity object with lineage and improves effectiveness |
Partner clean-rooms that throttle usage | Snowflake/Databricks cross-domain queries are metered, so teams ration the very analyses that uncover value; buyers balk at “usage-based surprises.” | Federated “bring-compute-to-data” clean-room lets partners land once in S3 and run cross-domain filters inside an m1db node—no runaway bill, no lock-in. |
Slow feature velocity | Every new targeting dimension triggers schema work, back-fills and re-indexing, delaying product launches by months. | REST/JSON APIs let teams prototype directly on prod data |
Yield leakage from “lazy floors” & weak bid-density forecasts | Rule-of-thumb floors clear inventory too cheaply or leave impressions unsold. | Run per-impression pricing models on live signals without taxing the cluster; dynamic floors recapture CPMs. |
Cross-channel convergence (CTV, retail media, DOOH) | Separate IDs, schemas and viewability rules block holistic frequency & yield management. | Land VAST beacons, scanner receipts and web auctions together; cross-channel queries run in one pass |
Publisher trust & need for real-time transparency | Sellers want dashboards that update literally as auctions settle; metered warehouses make that cost-prohibitive. | No per-query fees mean you can expose second-by-second “glass-box” consoles without bill shock. |
Instant inventory forecasting for Programmatic Guaranteed & CTV pods | Sales teams need second-by-second forecasts to reserve impressions across devices and pod positions; traditional nightly Spark jobs miss fast-moving CTV supply. | Forecast queries are just high-cardinality filters and aggregations—M1DB executes them on the live denormalised doc set, combined with detailed machine learning models executed on the historical datasets. |
Carbon-footprint accounting & green media buys | Brands attach CO₂ budgets to campaigns; SSPs must show grams per 1 000 impressions. | Store energy-meter logs next to delivery data; surface per-campaign emissions without a separate ESG warehouse. |
The Technical Foundation: ModelForge & IdentityForge
MinusOneDB's SSP solution is powered by two revolutionary implementations that we adapt to your architecture:
ModelForge: Unlocking Hyperscale AutoML & Continuous Model Scoring
Because writes are equivalently inexpensive and performant to reads inside MinusOneDB, workloads like autoML that currently are limited by write throughput at scale become financially and computationally feasible.
Continuous model evolution: Score, compare, and evolve thousands of model variants nightly
Rapid feature engineering: Test new signals across your entire feature store without pre-planning
Real-time feedback loops: Update scores as new data arrives, not during overnight batch windows
Reduced time-to-production: Deploy many new models per day in minutes to hours rather than weeks of infrastructure work
Comprehensive model lineage: Maintain version history, variant performance, and governance audit trails
IdentityForge: Enabling Real-Time Identity Resolution
Resolving a stream of update data to profiles is currently usually a batch job because of the computational and financial expense - but not in MinusOneDB, where it can be resolved to profiles or identities in real time, and immediately be available for querying - bringing live data analytics on large datasets, anomaly detection, audience updating and more within reach.
Continuous identity graph: Process billions of identity signals in real-time for optimal matching
Cross-device connectivity: Link users across browsers, apps, and devices in milliseconds
Alternative identifiers: Can integrate UID 2.0, RampID, and other solutions
Probabilistic modeling: Maintain reach and targeting through privacy-safe techniques
Privacy-first architecture: Enforce consent and compliance across all operations
Sell Side Benefits of MinusOneDB
For Revenue Teams:
Superior yield performance through algorithmic optimization
Better competitive positioning via unique publisher value proposition
Higher-value inventory packaging through enriched contextual intelligence
Improved demand partner relationships through data-driven insights
For Product Teams:
Faster feature delivery through simplified architecture
More comprehensive analytics without sampling or aggregation
Enhanced publisher tools leveraging real-time data processing
Future-proof identity strategy resilient to regulatory changes
For Engineering Teams:
Reduced infrastructure complexity through unified solution
Predictable scalability through capacity-based pricing
Lower operational overhead with self-healing architecture
Superior performance without specialized optimization
For Publisher Relations Teams:
Better publisher retention through demonstrably higher yield
Enhanced reporting capabilities with real-time insights
More compelling value proposition versus competing SSPs
Higher-value publisher partnerships through data collaboration
Get Started Today
Contact: info@minusonedb.com
Transform your infrastructure from a technical challenge to a competitive advantage.
Author
MinusOneDB
Nov 14, 2025
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