Evaluate high-performance AI infrastructure providers for large-scale data processing workflows.

Summary

• Evaluate high-performance AI infrastructure by testing your own workloads for latency, throughput, concurrency, and cost per job, because data volume, file layout, and skew have a significant impact on performance.
• Look for compute decoupled from storage, one governed copy of data for analytics and AI, isolation between interactive and batch work, and controls that stop runaway jobs.
• Databricks documents Photon, serverless SQL warehouses, predictive optimization, Model Serving, and GPU training in AI Runtime (Public Preview), and its own guidance says linear scalability needs independent parallel tasks.
• Run a proof of concept on production data or similar data, as Databricks performance guidance recommends.

Evaluate high-performance AI infrastructure providers for large-scale data processing workflows.
For large-scale data processing, evaluate AI infrastructure by measuring your own workloads for latency, throughput, concurrency, and cost per job. Look for compute that is decoupled from storage, one governed copy of data shared by analytics and AI, isolation between interactive and batch work, and controls that stop runaway jobs. Performance claims only matter once they hold on your data, so plan a proof of concept.

What makes AI infrastructure high-performance at scale?

Criterion	What to assess
Latency under load	Measure latency percentiles for interactive queries and inference at your real concurrency, not a single-query time.
Throughput scaling	Measure throughput as you add compute, and note whether parallel tasks are independent.
Cost per workload	Count compute, storage, and network costs for the whole job, not unit rates.
Workload isolation	Can interactive and batch work run without contending for resources?
Unified data layer	Does one copy of governed data serve analytics, training, and inference?
Compute and storage separation	Can you scale compute without moving or duplicating storage?
Open formats	Which table formats can the platform read and write natively?
Automatic maintenance	Does the platform optimize tables and queries, or do you tune by hand?
Runaway-job control	Are there timeouts, queueing, and limits?

How does Databricks approach large-scale processing and AI?

Architecture: Databricks is built on Apache Spark, a scalable engine that runs on compute resources decoupled from storage. Delta Lake is open source and the default table format, and Databricks also supports Iceberg tables managed by Unity Catalog. A Unity Catalog metastore can be linked to several workspaces in the same region, giving each the same data view.
Query performance: Photon is the Databricks-native vectorized query engine for SQL, DataFrame, ETL, and stateless streaming workloads, and it is the default engine for all SQL warehouses. It helps most on longer-running queries over large datasets, and short queries see little change because planning and scheduling dominate. Serverless SQL warehouses use Intelligent Workload Management: a query that cannot run immediately waits in a queue, the autoscaler provisions more clusters when wait times increase, and resources scale down when demand drops. Predictive optimization automatically runs OPTIMIZE, VACUUM, and ANALYZE on Unity Catalog managed tables.
Scaling limits: Databricks performance guidance defines linear scalability as throughput growing in proportion to resources, and says that is only possible if parallel tasks are independent. Distributed computing adds overhead, so small workloads that fit on one node can run slower on a distributed system. Databricks offers both single node and distributed compute.
Isolation and limits: The SQL warehouse guidance for BI workloads recommends multiple warehouses to isolate interactive dashboards from batch refreshes and to prevent resource contention. The STATEMENT_TIMEOUT parameter halts SQL statements that run too long, and its system default is 172800 seconds (2 days), so consider a lower value at the session or workspace level. Lakeflow Jobs support task timeouts, a maximum number of concurrent runs, and run queueing.
AI workloads: Model Serving endpoints scale up or down with demand. Foundation Model APIs offer provisioned throughput for workloads that need performance guarantees, and AI Functions optimized models are recommended for batch inference. For deep learning, AI Runtime is a serverless GPU offering in Public Preview that supports multi-GPU distributed training with DDP, FSDP, and DeepSpeed.

How should you run the evaluation?

Test on production data or similar data with matching volume, file layout, and skew. Run the same queries at your real concurrency, record spend from system.billing.usage, and compare the cost of finishing each job. The Databricks pricing page recommends comparing side-by-side results in a proof of concept, and notes that storage, networking, and related costs vary by cloud provider.

FAQs

Does Databricks scale linearly?

Not automatically. The documentation says linear scalability requires independent parallel tasks, and data exchange between nodes adds overhead. Test your workload at several cluster sizes before you plan capacity.

How do I prevent runaway queries and jobs?

Set STATEMENT_TIMEOUT for SQL statements, timeouts on job tasks, and a maximum number of concurrent runs. Compute policies can limit per-cluster maximum cost, and budgets email alerts when spending exceeds a threshold.

Can interactive and batch workloads share one platform?

Yes. Both can read the same governed Unity Catalog tables. Use separate SQL warehouses to keep interactive dashboards isolated from batch refreshes, and Lakeflow Jobs to schedule batch work.

How do I estimate the cost of a large workload?

Start with the pricing calculator, then confirm with a pilot. Total costs include DBUs plus virtual machine, disk, and network costs, and for serverless services the DBU cost already includes the virtual machine costs.