Data warehouses weren't built for what SaaS needs from them
Snowflake, Redshift, and BigQuery were designed to store data and run analytics on it. They weren't designed to power real-time, customer-facing analytics across thousands of tenants inside a SaaS product. The mismatch shows up later as cost, complexity, and roadmap drag.
Five places it bites hardest — and the design moves that defuse each one.
The value of multi-tenant analytics
Done right, multi-tenant analytics moves a SaaS app into the mission-critical column. That matters more during a downturn, when customers are consolidating tools and only the stickiest ones survive. Four returns to plan around:
- Customer satisfaction and retention. Users get the reports they actually want, and they stop leaving.
- New revenue. Analytics earns its place as a premium tier — not a free add-on.
- Competitive differentiation. In categories where the rest of the feature set is at parity, the analytics layer is what closes the deal.
- Roadmap focus. The product team stops building one-off reports and goes back to differentiating the product.
Multi-tenancy is what makes any of this efficient operationally — one shared application stack, logically isolated data walls between tenants, far more efficient than running a separate database per customer while keeping the same privacy guarantees.
Data warehouse blueprint for multi-tenant analytics
Snowflake and Redshift are great at what they were built for: storing data and running analytics on it. Multi-tenant SaaS analytics wasn't the primary use case. You can't just point customer-facing charts at a shared data warehouse and call it done — and the biggest reason is data security.
Enforcing tenant security takes custom middleware: metadata tables, user access controls, and a semantic layer that mediates between the SaaS app's tenant model and the warehouse's query model. The five challenges below are the places that work gets expensive.
Design difficulties
Data warehouses don't understand multi-tenancy. To get them to play nice with your SaaS app's tenant model, you build a semantic layer that translates tenant-scoped front-end logic into warehouse logic. It's connectivity work nobody enjoys, and it grows in complexity fast.
The common fallback — provisioning a separate database per tenant — multiplies the problem instead of solving it. The front-end has to talk to every database, and performance gets worse, not better.
Devoted design
Both AWS and Snowflake have published patterns for multi-tenant data design. AWS describes three: Pool, Bridge, Silo. Snowflake describes three of its own: Multi-tenant table (MTT), Object per tenant (OPT), Account per tenant (APT). The two overlapping recommendations — AWS's “Pool” and Snowflake's “MTT” — are the patterns Qrvey recommends.
“The pool model represents an all-in, multi-tenant model where all tenants share the same storage constructs and provides the most benefit in simplifying the AaaS (Analytics as a Service) solution.”
— AWS
“MTT is the most scalable design pattern in terms of the number of tenants an application can support. This approach supports apps with millions of tenants. It has a simpler architecture within Snowflake. Simplicity matters because object proliferation makes managing myriad objects increasingly difficult over time.”
— Snowflake
Higher costs
Data warehouses for multi-tenant analytics require heavy modeling and engineering upfront — plus middleware between the database and the customer-facing app. The bill keeps growing after launch, too. Usage-based pricing scales fast when traffic is unpredictable.
Snowflake launched as a low-cost alternative to Oracle and Teradata, which carry six- and seven-figure upfront license fees. Snowflake swapped that for pay-as-you-go. But each performance tier roughly doubles in cost, and SaaS apps are designed to push utilization up. Customer success looks great. The bill doesn't.
Real-time, interactive embedded analytics require larger environments — which is exactly where Snowflake costs accelerate.
“Unoptimized queries cause the execution to take longer, affecting the performance of the database and increasing cost. The cost consideration is especially important in a warehouse solution like Google's BigQuery, which charges by query execution time.”
— RudderStack
Optimized costs
Pull what you need, when you need it. Qrvey's Live Connect — introduced in 8.0 — supports real-time data from Snowflake, Redshift, and PostgreSQL, and lets you decide what syncs live vs. what runs on schedule. Predictable workloads sync during designated windows. Unpredictable ones stay live. Costs follow actual demand, not peak provisioning.
Scalability struggles
One shared data warehouse serving multiple tenants with isolated data per tenant sounds simple in a deck. In practice, data warehouses don't scale out for multi-tenancy without significant engineering. You add concurrency scaling, more clusters, more pipelines, more operational overhead. Each path compounds as the customer base grows.
Anything can scale with enough brute-force effort. The question is how much effort it takes to get there.
“If sufficient resources are not available to execute all the queries submitted to the warehouse, Snowflake queues the additional queries until the necessary resources become available.”
— Snowflake
“As the number of tenants increased, you could either turn on concurrency scaling or create additional clusters. However, the addition of new clusters means additional ingestion pipelines and increased operational overhead.”
— AWS
Smooth scaling
Pick an architecture that scales with tenants instead of fighting them. Auto-scaling, optimized data partitioning, query performance tuning — all targeted at consistent performance across every tenant, with minimal operational overhead in the middle.
Analytics performance challenges
Concurrency is hard to optimize without bigger clusters. Bigger clusters cost more — even sitting idle. Snowflake pricing follows compute, storage, and cloud services usage, with each warehouse tier doubling in cost and power. Provisioning for peak gives users speed, but you pay for off-hours capacity you don't actually use.
It's a familiar pattern. Many Qrvey customers running on Snowflake report bills that grow well beyond their forecasts.
Perfecting performance
Producer and consumer clusters. Ingest data into a producer cluster, then share that live data out to consumer clusters that don't impact each other's performance. Each consuming cluster operates on its own compute capacity, getting consistent performance for its workload without dragging the others down.
Data security and governance gaps
Data warehouses are built for a single source of truth at a single company. They are not natively built for row-level security across thousands of tenants. Every data warehouse solution requires additional engineering to enforce tenant-level data separation. Layer user-level access controls on top, and the work compounds again.
“A particular concern for multi-tenant data solutions is the level of customization you support. … Avoid forking or providing custom infrastructure for individual tenants. Customized infrastructure inhibits your ability to scale, to test your solution, and to deploy updates. Instead, consider using feature flags and other forms of tenant configuration.”
— Microsoft
Good governance and data security
Role-based access control can be built into a data warehouse. The honest part: it's expensive, time-consuming, and has to be designed in from day one. The cost of getting it wrong shows up later as either a security incident or a re-architecture project — neither of which has a happy timeline.
Data lake for multi-tenant analytics
When Qrvey set out to push embedded analytics past dashboards-and-charts, legacy relational databases didn't keep up. So we built a different data management approach — OpenSearch + S3 + DynamoDB on AWS, deployed as a low-cost, highly-scalable multi-tenant analytics data lake.
OpenSearch is the workhorse: NoSQL, no rigid schema requirements, no preprocessing before data lands, and indexing plus aggregation features that run larger queries in less time. Scalability and features built for multi-tenant analytics — not retrofitted for it.
SaaS teams underestimate how much an unoptimized data layer caps the analytics they can offer — and customers asking for self-service end up with one-size-fits-all reports instead.
Most teams don't have the in-house expertise to fix that. Qrvey does — leading software focused specifically on embedded analytics for SaaS companies.
Evaluating analytics for your SaaS product?
The Embedded Analytics Evaluation Guide picks up where this one ends — a framework for vendor evaluation, the questions to ask, and the platform limitations to watch for.
Read the guide →
