Executive summary
Organizations that have legacy data platforms like Teradata, Informatica, and Snowflake are increasingly challenged by rising costs, operational inefficiencies, and performance limitations:
- These vendors are now requiring multi-year renewals to promote vendor lock in.
- These companies continue to invest less and less into platform improvements.
- These platforms are proprietary and provide little transparency and connectivity.
We have data-driven proof that supports Databricks as the most price-performant data and AI platform on the market today.
Simplicity is hard to build, easy to use, and hard to charge for. Complexity is easy to build, hard to use, and easy to charge for.
Chris Sacca, Investor
Why Databricks leads in price-performance
Databricks delivers industry-leading performance through capabilities like the Photon engine for SQL acceleration, auto-scaling clusters for efficient compute elasticity, and Delta Lake and Iceberg for optimized data storage and access. Its unified architecture eliminates the need for multiple point solutions, allowing enterprises to consolidate data engineering, BI, ML, and streaming pipelines into a single runtime. It also scales with a flatter cost curve than most other platforms allowing for more predictable cost increases as the data platform matures and grows.
Organizations need to quantify the true cost and performance tradeoffs between platforms, understand the hidden inefficiencies in legacy stacks, and calculate the potential ROI from migrating to Databricks. Data leadership now has a data-backed analysis into cost-per-TB metrics, compute efficiency, and storage optimization, enabling clear justification for consolidating to a unified Lakehouse. If you’ve already chosen Databricks as a part of your data estate, the data is pretty compelling. Here are some examples:
Teradata to Databricks:
Teradata migrations can be significantly complex. The level of complexity typically comes from the legacy patterns that have been established over the last decade by trying to integrate Teradata with legacy architectures. By migrating only 70% of workloads to Databricks, a typical enterprise can reduce total platform TCO by 50% or more, lower licensing and support fees and cut compute costs by 40%, while increasing data processing capacity by a minimum of 3x. These optimizations can lead to a projected annual savings of $5M or more for large-scale deployments while also improving job throughput and reducing operational overhead.
Informatica to Databricks:
While Informatica environments can behave similarly to Teradata environments, Legacy ETL pipelines in Informatica can often times be higher-cost, slower-to-adapt, and are often manually governed. Each of these aspects make traditional Informatica environments more complex to migrate. The shift to Databricks can not only reduce pipeline complexity as well as deliver a 45% or more reduction in data transformation costs, faster pipeline completion times, and significantly improved fault tolerance and traceability. Typcial migrations can produce an annual savings of $2 to 4M and improve SLA compliance by over 60%.
Snowflake to Databricks:
While Snowflake provides ease of use for traditional BI workloads, it often struggles to deliver cost-effective compute for ML pipelines and complex ELT workloads. We find that companies that have over-invested in Snowflake have created another legacy-system paradigm similar to investing in Teradata, Informatica, Oracle, etc. because Snowflake environments are proprietary and do not integrate well with the rest of the systems in typical modern data estate. Our customers have seen up to 3x better cost-per-TB efficiency on Databricks, particularly for feature engineering, model scoring, and data preparation pipelines. Post-migration, companies reduced cloud spend by 40 to 60%, decommissioned duplicate environments, and centralized governance using Unity Catalog.
Every company is a software company. You have to start thinking and operating like a digital company. It’s no longer just about procuring one solution and deploying one. It’s not about one simple software solution. It’s really you yourself thinking of your own future as a digital company.
Satya Nadella, CEO of Microsoft
The hidden complexity of measuring true price-performance
Understanding true price-performance goes beyond just looking at how much compute costs or how fast a platform claims to be. It requires a full picture of how workloads behave within a given cloud setup factoring in infrastructure, orchestration, and transparency around costs.
Things like whether resources are right-sized, how well autoscaling works, how often jobs fail and need to restart, delays from cold starts, or how storage choices affect data access all impact real performance and cost. Old patterns from legacy systems often don’t hold up, especially when managing large-scale modern data environments.
This becomes even more complex when comparing platforms like Databricks and Snowflake, or when workloads span across clouds like AWS, Azure, and GCP. Pricing varies depending on compute type, location, storage, and data transfer rules and if your systems aren’t tightly connected, comparing cost vs. performance is nearly impossible. Even within one cloud provider, things like burstable capacity or inconsistent telemetry make fair comparisons hard. Most teams end up guessing, using outdated logs or dashboards that aren’t aligned across finance, engineering, and architecture.
Lakehouse Optimizer (LHO) solves this with deep Databricks integration and multi-faceted data collection; both cost and telemetry data. LHO captures job-level details from Databricks and the Cloud Provider, links them with accurate billing info, and tracks how jobs run across different pipelines. This gives a clear $/TB processed metric, along with context like job duration, concurrency, data scanned, and downstream effects. Teams can see where bottlenecks are, how config changes affect cost and performance, and predict spend more accurately. It also helps map usage patterns to the goals of the CDO, guiding decisions on platform consolidation.
Success story
Large-scale self-storage rental company
A large retailer used LHO to bring stability and control to its Databricks environment after struggling with inconsistent job performance, unpredictable costs, and scaling challenges across regions. By leveraging LHO’s deep telemetry and cost correlation capabilities, the retailer was able to right-size compute resources, optimize autoscaling settings, and eliminate inefficiencies caused by cold starts and job retries. This resulted in a 10x increase in data processed across its analytics pipelines, while only increasing its overall Databricks and cloud spend by 17%. With clearer visibility into workload behavior and cost-performance trade-offs, the retailer was able to confidently expand its data estate and accelerate insights without runaway costs or performance bottlenecks.
This level of cost-performance optimization is only possible on Databricks because of its open and transparent architecture that exposes detailed telemetry across compute execution, job runtime, and Spark internals. Unlike black-box platforms that hide operational metrics, Databricks allows full observability and control, making it possible to connect cost directly to performance at a granular level. Its flexible orchestration, modular compute layers, and real-time autoscaling capabilities can be deeply instrumented by LHO, enabling continuous feedback loops between engineering, FinOps, and data teams. This transparency allows organizations to trace spend to specific workloads, simulate what-if scenarios, and benchmark cost-efficiency at scale, delivering insights and control that closed platforms simply cannot provide.
