TL;DR
-
For bursty, sub-second embedded analytics at GB to TB scale, billing granularity, warm-start behavior, and compute isolation usually matter more than headline benchmark speed.
-
Snowflake, BigQuery, ClickHouse Cloud, Databricks SQL, and Redshift Serverless can all handle concurrency technically, but they are often optimized for different workload shapes than embedded analytics.
-
MotherDuck is designed for embedded analytics: Hypertenancy isolates compute per user or service account, and Pulse bills per compute-unit second.
-
If your workload is short-query, spiky, and latency-sensitive, optimize for predictable cost and predictable P95 latency before raw benchmark scale.
Why some cloud data warehouses are a poor fit for embedded analytics
In this article, we use embedded analytics to mean bursty, interactive, customer-facing analytics workloads, typically at gigabyte to terabyte scale.
This article is not asking which warehouse is fastest in the abstract, or which one scales to the largest dataset. It is asking a narrower question: how well does each system handle embedded analytics when the data footprint is modest enough that petabyte-oriented architecture is not the main bottleneck?
For embedded analytics, the usual failure mode is not that a warehouse cannot execute the query. It is that the end-to-end system becomes awkward or expensive once you combine short dashboard queries, spiky traffic, many concurrent users, strict P95 latency expectations, and the need to keep cost predictable.
That is why comparing OLAP engines only by benchmark throughput is misleading. For embedded analytics, the practical questions are how compute is isolated under concurrency, what happens when traffic goes from idle to hot, how billing maps to real query behavior, how effectively the system avoids unnecessary data reads, and how much tuning or warm capacity is required to keep latency predictable.
How to evaluate OLAP engines for embedded analytics
Use a fixed rubric. For embedded analytics, six factors usually matter more than single-query speed tests.
-
Billing granularity: Does the platform bill per query, per second of active compute, per minute of active compute, per resume event, or per byte scanned? For spiky traffic, the key question is when minimum charges trigger and how often real traffic patterns trigger them.
-
Warm/cold behavior: Does the system resume quickly from idle, or do you need warm capacity or acceleration layers to protect latency? If your product SLA expects sub-second responses, warm-start behavior matters as much as raw scan speed.
-
Concurrency isolation model: Are users sharing one compute pool, isolated into separate compute units, or routed through read replicas or separate services? Under concurrency, the engineering question is how much isolation you get before you need to pay for extra always-on capacity.
-
Scan efficiency and pruning: How effectively does the system avoid reading unnecessary data through partitioning, clustering, caching, pruning metadata, materialized views, or in-memory acceleration? Systems that are excellent at large-scale exploratory scans can still be a poor fit for frequent short dashboard queries if they require too much scanned data or too much warm infrastructure to stay fast.
-
Operational complexity: How much tuning is required around caching, partitioning, materialized views, service sizing, or warehouse topology? Some systems are powerful but ask for much more modeling and operational discipline to deliver predictable embedded analytics performance.
-
Cost predictability: Can you forecast cost from expected traffic, or does pricing depend heavily on scan volume, auto-scaling behavior, or warm compute policy? For embedded analytics, predictability often matters as much as raw cost.
How billing mechanics change the economics
Interactive analytics workloads are affected by both latency mechanics and billing mechanics, but they are not the same thing. A platform may bill per query, per second while compute is active, with a minimum each time compute starts or resumes, or per byte scanned. For spiky traffic, minimum active periods and warm-capacity requirements can create an idle tax even when each individual query is short.
Workload-fit comparison for embedded analytics
| Platform | Primary Compute Billing Model | Concurrency Model | Technical Concurrency Capability | Economic Fit |
|---|---|---|---|---|
| MotherDuck | Pulse billed per compute-unit second; larger instances billed per second according to instance type | Hypertenancy with isolated Ducklings per user or service account | Strong, because compute is isolated per user and can scale independently | Designed for embedded analytics |
| ClickHouse Cloud | Compute units billed over active service time; metering is per minute | Shared data with service-level isolation and optional Warehouses for compute-compute separation | Strong, especially for large read-heavy workloads and real-time event analytics | Can fit, but often needs more service design and warm-capacity choices than simpler embedded analytics workloads |
| Snowflake | Warehouses bill per second while running, with a 60-second minimum each time a warehouse resumes | Shared warehouse compute; can scale with larger or multi-cluster warehouses | Strong | Technically capable, but can become expensive for spiky short-query embedded analytics if resume events or always-warm warehouses are frequent |
| Google BigQuery | On-demand per TiB scanned, or capacity pricing via slots billed over time | Shared serverless execution with optional BI Engine acceleration | Strong | Technically capable, but often a weaker economic fit for frequent short embedded-analytics queries unless pricing mode, partitioning, clustering, and BI acceleration are carefully managed |
| Databricks SQL Serverless | SQL warehouses consume DBUs based on warehouse size over active time | Shared serverless SQL warehouses with caching and auto-scaling | Strong | Reasonable if you already live in Databricks, but often heavier than needed for embedded analytics at modest scale |
| Amazon Redshift Serverless | RPU-hours billed per second with a 60-second minimum charge | Isolated workgroups with serverless scaling | Strong | Viable, but often less efficient for sparse, spiky embedded analytics than for steadier warehouse workloads |
Why burst testing is mandatory
A single benchmark query does not tell you much about embedded analytics. You need to test repeated dashboard refreshes, cache-warm and cache-cold runs, concurrent users hitting the same dashboard, mixed short and heavy queries, and cost behavior during idle-to-burst transitions. That exposes the real trade-offs around queueing, resume behavior, scan volume, and active billing time.
Cloud data warehouses at a glance: workload-fit comparison
The right warehouse depends less on a universal ranking than on the workload you are actually trying to serve.
| Platform | Best Fit | Why Teams Choose It | Trade-off |
|---|---|---|---|
| MotherDuck | Customer-facing analytics, embedded BI, interactive product analytics, small-to-growing warehouse use cases | Isolated per-user compute, low operational overhead, sub-second interactive focus | Managed DuckLake is still a newer growth path and should be evaluated carefully for production constraints |
| ClickHouse Cloud | Very large event streams, observability-style analytics, high-ingest systems, engineering-led analytics stacks | Exceptional performance at large row counts, strong compression, flexible service sizing | Often requires more tuning, service topology decisions, and warm-capacity planning for strict embedded-analytics SLAs |
| Snowflake | Enterprise BI, governed data sharing, mixed workloads across many teams | Mature ecosystem, governance, data sharing, familiar warehouse abstractions | For short, spiky customer queries, active-compute billing and warm-warehouse policy matter a lot |
| Google BigQuery | Elastic ad hoc analytics, large-scale data mining, GCP-centric analytics | Minimal ops, strong elasticity, excellent scale characteristics | On-demand scan pricing can be awkward for frequent interactive dashboards unless the workload is modeled and priced carefully |
| Databricks SQL Serverless | Lakehouse-centric organizations combining SQL, data engineering, and ML/AI | Unified platform around Delta Lake and Databricks workflows | More platform complexity than many teams need for embedded analytics |
| Amazon Redshift Serverless | AWS-native analytics with tight integration into the broader AWS stack | Familiar Redshift model with serverless operations and AWS integration | Active-time billing with a 60-second minimum can create waste for spiky short-query traffic |
Evaluating six warehouses for embedded analytics
MotherDuck
Best-fit workload
We built MotherDuck for customer-facing analytics, embedded BI, internal dashboards, and application analytics where interactive latency matters and teams want to avoid running distributed infrastructure too early.
Architecture and concurrency model
MotherDuck uses a scale-up DuckDB-based architecture and a Hypertenancy model. Each internal user, customer, or service account can be assigned its own Duckling, which provides compute isolation rather than forcing many users through one shared warehouse. For embedded analytics, that matters because the isolation model maps cleanly to product traffic patterns: one tenant’s heavy query is less likely to interfere with another tenant’s read path.
Pricing model
MotherDuck’s pricing is instance-based. As of April 2026, Pulse is billed per compute-unit second, while larger instance types are billed per second by instance size. Business and enterprise plans add organization pricing and fixed-capacity options.
Strengths
MotherDuck aligns well with embedded analytics because per-user compute isolation reduces cross-tenant contention and avoids forcing every tenant through the same shared warehouse path. It also keeps operational overhead low compared with tuning a distributed warehouse for modest data sizes, while read scaling and larger Duckling sizes give teams room to grow before redesigning the architecture. Standard tool compatibility still applies across SQL, dbt, ingestion tools, and BI connectors.
Failure mode
Managed DuckLake gives teams a larger-scale storage path, but it is still a newer part of the product surface. Teams with strict production requirements should review current limitations before assuming the scale path is operationally equivalent to mature open-table-format stacks.
Scaling path with Managed DuckLake
For teams whose data grows beyond native warehouse storage, Managed DuckLake offers a path to petabyte-scale data while keeping the SQL interface and MotherDuck control plane consistent. DuckLake’s core design uses database-backed metadata rather than file-walk metadata, which can materially improve metadata-heavy operations and partition pruning compared with file-oriented table formats.
Because Managed DuckLake is still evolving, here are the current preview limitations:
- automatic compaction and garbage collection are still being automated,
- streaming writes are still evolving,
- row-level and column-level security are planned rather than fully available,
- external DuckLakes remain limited,
- and some multi-user write semantics are still being improved.
ClickHouse Cloud
Best-fit workload
ClickHouse Cloud is best suited to very large event streams, real-time analytics, observability-style workloads, and engineering-heavy analytics systems where row counts, ingest volume, and compression efficiency are first-order concerns.
Architecture and concurrency model
ClickHouse Cloud separates storage from compute and now supports Warehouses, which introduce compute-compute separation for different workloads or teams sharing the same data. That makes the old “everything shares one cluster” framing too simplistic. It remains a distributed system, but it is a distributed system with better isolation options than many older comparisons assume.
Pricing model
ClickHouse Cloud bills active compute in normalized compute units. Current pricing is expressed per compute unit per hour, with per-minute metering. Service cost depends on node count, node size, and plan tier.
Strengths
ClickHouse is very strong at high row counts and sustained analytical load, and it remains an excellent fit for event-heavy, read-heavy, or ingestion-heavy systems. Flexible service sizing and newer warehouse isolation options also improve how teams can design for concurrency.
Failure mode
For modest embedded analytics, ClickHouse can be more system than you need. To hold strict sub-second latency under intermittent traffic, teams may still end up making deliberate choices around service topology, idling behavior, materialized views, and warm capacity. That is a valid trade if you need ClickHouse’s scale characteristics; it is less attractive if your main problem is simply serving short customer dashboard queries economically.
Snowflake
Best-fit workload
Snowflake is best suited to enterprise BI, governed data sharing, mixed workloads across departments, and organizations that value operational maturity, ecosystem depth, and warehouse abstractions over minimal runtime footprint.
Architecture and concurrency model
Snowflake uses shared storage with separate virtual warehouses for compute. Concurrency can be handled through warehouse sizing, auto-scaling, and multi-cluster warehouses. Technically, that gives Snowflake strong concurrency support.
Pricing model
Snowflake warehouses bill per second while running, with a 60-second minimum each time a warehouse is resumed. Credits consumed depend on warehouse size, cluster count, and active runtime. That is not a per-query model; it is an active-compute model with a resume minimum.
Strengths
Snowflake offers strong technical concurrency support, mature caching behavior for repeated queries, and well-understood governance and sharing features. It is especially attractive when the same platform must serve many internal workloads beyond embedded analytics.
Failure mode
Snowflake becomes harder to justify economically when a customer-facing product generates many short bursts against warehouses that frequently suspend and resume, or when teams keep warehouses warm just to avoid latency. In steadier workloads that minimum charge is often amortized well. In highly spiky product analytics, it can become a noticeable idle tax.
Google BigQuery
Best-fit workload
BigQuery is best suited to elastic ad hoc analytics, large-scale exploratory querying, and organizations that want minimal warehouse operations while staying close to the broader Google Cloud ecosystem.
Architecture and concurrency model
BigQuery is a serverless distributed analytics engine. It is technically capable of serving very large concurrency and scale, and it does so without asking teams to manage clusters directly. BI Engine adds an optional in-memory acceleration layer for dashboard-oriented access patterns.
Pricing model
BigQuery supports two main compute models: on-demand pricing, billed per TiB scanned, and capacity pricing, billed for slot capacity over time. As of April 2026, BigQuery capacity pricing is billed per second with a one-minute minimum, while BI Engine capacity is purchased separately as reserved memory.
Strengths
BigQuery offers very strong elasticity and operational simplicity, and it remains a good fit for teams that value serverless scale and ad hoc exploration. BI Engine can also materially improve dashboard latency when the workload shape fits its acceleration model.
Failure mode
BigQuery is not weak at concurrency. The risk for embedded analytics is economic and modeling-driven, not architectural incapability. With on-demand pricing, frequent short queries can become expensive if scan volume is not tightly controlled. With capacity pricing and BI Engine, the system can be very effective, but only if teams actively manage partitioning, clustering, acceleration coverage, and reservation strategy. That makes BigQuery technically viable, but often a less natural fit for embedded analytics than for ad hoc analytics at scale.
Databricks SQL Serverless
Best-fit workload
Databricks SQL Serverless is best suited to teams already invested in Databricks for data engineering, Delta Lake, governance, ML, and AI workflows, and that want SQL analytics to live inside the same platform.
Architecture and concurrency model
Databricks SQL uses serverless SQL warehouses backed by the Photon engine. Concurrency is handled through warehouse sizing, auto-scaling, and caching inside the Databricks SQL architecture.
Pricing model
Databricks SQL Serverless bills by SQL warehouse size in DBUs per hour. That compute model is straightforward, but total cost still depends on how long warehouses remain active and how large they need to be for target concurrency.
Strengths
Databricks SQL makes the most sense when your data, orchestration, and governance are already centered on Databricks. Photon and result caching can deliver strong SQL performance, and the platform keeps analytics close to the same lakehouse stack used for ETL and ML.
Failure mode
For teams that only need fast customer-facing BI at modest scale, Databricks can be heavier than necessary. The platform makes more sense when SQL analytics is one piece of a larger Databricks strategy, not when embedded analytics is the only primary requirement.
Amazon Redshift Serverless
Best-fit workload
Redshift Serverless is best suited to AWS-centric analytics teams that want managed warehouse behavior with strong integration into S3, AWS identity, and adjacent AWS data services.
Architecture and concurrency model
Redshift Serverless provides isolated workgroups and serverless scaling. It is technically capable of serving concurrent workloads, especially when the rest of the stack already lives in AWS.
Pricing model
Redshift Serverless bills compute in RPU-hours on a per-second basis with a 60-second minimum charge. Like Snowflake, that is an active-compute billing model with a minimum floor, not a per-query charge.
Strengths
Redshift Serverless is a good fit for AWS-native organizations, and its serverless operations are simpler than managing traditional Redshift clusters. It is also a natural choice for teams already standardized on the broader Redshift and AWS ecosystem.
Failure mode
For sparse, short, bursty interactive queries, minimum active billing can create waste relative to systems that align billing more tightly to short-lived per-user demand. That does not make Redshift Serverless weak at concurrency. It means the economics can be awkward when embedded analytics traffic arrives in short bursts rather than steady warehouse sessions.
Which warehouse fits which use case?
Choose based on workload shape first, not brand recognition.
| If your priority is... | Strongest fits | Why |
|---|---|---|
| Sub-second customer-facing dashboards with bursty traffic at GB to TB scale and a future path to PBs | MotherDuck | The architecture and billing model are directly aligned with isolated, short-lived interactive queries |
| Very large event analytics with heavy ingest and engineering control | ClickHouse Cloud | Strong performance and efficiency at large row counts, with richer service-isolation options than older comparisons assume |
| Enterprise BI across many internal stakeholders with governance and sharing needs | Snowflake | Mature warehouse abstractions, governance, and multi-team operations |
| Elastic ad hoc analytics and exploratory querying at large scale | Google BigQuery | Minimal ops and strong elasticity, especially when dashboard economics are not the dominant concern |
| A unified SQL + ETL + ML/AI lakehouse stack | Databricks SQL Serverless | Best when SQL analytics is part of a larger Databricks platform choice |
| AWS-native managed analytics | Amazon Redshift Serverless | Strong alignment with teams already standardized on AWS |
Conclusion
High concurrency is not the deciding factor by itself. The real question is whether a warehouse can deliver predictable latency and predictable cost for the exact workload you have.
For embedded analytics at gigabyte to terabyte scale, the most important variables are usually billing granularity, compute isolation, warm-start behavior, and how much tuning is required to keep scan volume under control. Systems built for enormous shared analytical estates can still be technically excellent while being a less natural fit for this narrower workload.
If your workload is bursty, interactive, customer-facing, and modest in data size, optimize for billing granularity, isolation, and predictable latency before raw benchmark scale.
We designed MotherDuck for embedded analytics, aligning compute isolation and billing more closely with how product analytics traffic actually behaves, while still offering a path to larger data footprints through Managed DuckLake as requirements grow.
Try MotherDuck for free to see how it handles this traffic pattern in practice.
Start using MotherDuck now!
FAQS
What are the key architectural trade-offs for optimizing P95 query latency and cost under spiky traffic?
Key trade-offs include decoupled storage cold starts versus expensive warm pools, index-driven versus scan-driven query execution, and compute isolation. Two distinct cost drivers affect interactive workloads: cold-start latency (delay before a query executes) and billing minimums (minimum charge per query regardless of actual execution time). These are independent problems. Legacy distributed MPP systems suffer from noisy neighbor latency spikes and 60-second billing minimums. MotherDuck's Per-User Tenancy and 1-second billing eliminate resource contention, ensuring cost-effective, sub-second analytics for spiky traffic.
Which architecture is better for delivering sub-second latency for concurrent dashboards without downstream data marts?
A serverless platform running on an in-process query engine like MotherDuck is substantially more effective for sub-terabyte interactive workloads. Traditional cloud data warehouses suffer from distributed Massively Parallel Processing (MPP) queuing delays and demand complex caching layers to approach sub-second response times.
MotherDuck's scale-up architecture and Per-User Tenancy provide isolated compute for sub-second dashboard latency, eliminating the need for sluggish downstream data marts for teams operating at gigabyte to terabyte scale.
