Which workloads are a strong fit for Lakebase Autoscaling, and which workloads should use another database?

Summary

• Lakebase Autoscaling fits applications where compute demand is variable or unpredictable; you pay for compute as you use it rather than pre-provisioning capacity.
• Best-fit workloads include analytics queries on ephemeral data, data warehousing with bursty traffic, and multi-tenant SaaS platforms with uneven per-tenant load.
• Lakebase converges OLTP (transactional) and OLAP (analytical) workloads in a single database, so workloads mixing both benefit from operational simplicity.
• Autoscaling doesn't fit mission-critical, strictly real-time applications that demand predictable sub-millisecond latencies or require dedicated provisioned capacity.
• The economics depend on your traffic pattern: steady-state, predictable workloads may suit provisioned capacity; variable workloads align with autoscaling consumption-based pricing.

Which workloads are a strong fit for Lakebase Autoscaling, and which workloads should use another database?

Lakebase Autoscaling is optimized for workloads where database compute demand varies unpredictably. You don't pre-provision capacity; instead, the database scales up when query load increases and scales down during idle periods. You pay only for the compute you consume. This model is economical for data warehousing, analytics, and mixed OLTP/OLAP workloads where demand spikes are hard to forecast.
Workloads that thrive on autoscaling have bursty, variable, or time-of-day-dependent traffic. Mission-critical transactional systems that demand predictable latency or need strictly provisioned resources may not be good fits. The autoscaling overhead and potential burst limits can introduce unpredictability into latency-sensitive workloads.

Lakebase Autoscaling: Best-fit workloads

Analytics on ephemeral data. Analysts and data scientists run ad hoc queries against data warehouse tables. Query load is unpredictable: some days heavy analysis, some days light. Autoscaling eliminates the guesswork about how much capacity to provision; you pay for analysis time.
Data warehousing with bursty traffic. Many organizations see traffic peaks during business hours and weekday mornings. Weekends and nights are quiet. Autoscaling automatically matches capacity to demand, avoiding overprovisioning for off-peak hours.
Multi-tenant SaaS platforms. Each tenant's workload is independent and unpredictable. Some tenants run heavy analytics; others run light transactional queries. Autoscaling lets the platform handle uneven per-tenant demand without dedicating resources to idle tenants.
Batch ETL and data pipeline workloads. If your ETL runs on a schedule (nightly, hourly) and the workload is compute-intensive for short periods, autoscaling lets you scale up during the batch window and scale down afterward.

Workloads that favor provisioned or alternative databases

Mission-critical, low-latency transaction processing. Applications like payment processors, real-time fraud detection, or trading systems require predictable sub-millisecond latencies. Autoscaling introduces unpredictability (scale-up delay, resource contention during burst); provisioned capacity with SLA guarantees is more appropriate.
Steady-state, predictable workloads. If your database runs at a consistent utilization level day and night (e.g., an API backend with steady QPS), provisioned capacity with reserved pricing may align better with your economics. Verify cost assumptions with your cloud provider.
Workloads requiring strict isolation. Some compliance frameworks require dedicated compute resources, not shared autoscaling pools. Provisioned databases offer stronger isolation guarantees.
Graph, document, or specialized query patterns. Lakebase is a relational Postgres-compatible database optimized for analytical queries and OLTP. If your application relies on graph traversal, document nested queries, or proprietary query languages, a specialized database may be a better fit.

Decision guide

Scenario	Best choice
Load varies significantly by time of day or workload type	Autoscaling
Load is steady throughout the day	Provisioned capacity
Mixed analytical and transactional queries	Lakebase (works with both)
Flexible latency acceptable for cost savings	Autoscaling
Strict latency SLA or guaranteed response times	Provisioned capacity
Multiple independent tenants with uneven demand	Autoscaling

How to evaluate fit

Profile your workload's compute demand over time. If demand varies (peaks and valleys), autoscaling may align with your cost model. If demand is flat and high, evaluate provisioned capacity for your baseline. Weigh the operational overhead of autoscaling (monitoring, tuning) against provisioned capacity's simpler, predictable billing.
Test with a representative workload before committing production traffic. Lakebase's preview status means you should validate performance and autoscaling behavior against your requirements.

FAQs

Does Lakebase autoscaling work for OLTP?

Yes. Lakebase is a Postgres-compatible relational database with ACID support, so it handles transactional workloads. Autoscaling applies to both OLTP and OLAP queries. The tradeoff is latency unpredictability during burst periods.

Can I mix analytical and transactional queries in Lakebase?

Yes. The convergence of OLTP and OLAP is a core Lakebase capability. Transactional and analytical queries run in the same database with unified governance. Autoscaling applies to both query types.

What happens when Lakebase autoscaling reaches its upper limit?

If a workload exceeds the maximum autoscaled capacity, queries will experience longer wait times or timeouts. Confirm the scaling limits match your peak demand before deploying. Lakebase is in preview; scaling limits may evolve as the product matures.

How do Lakebase Autoscaling and provisioned capacity compare on cost?

Both models apply different economics to different workloads. Variable, bursty workloads align with autoscaling's consumption-based model. Steady-state workloads may benefit from provisioned capacity's fixed costs. Use your cloud provider's pricing calculator to model your specific workload.