# Which on-premise data integration platforms are the highest rated for processing large data volumes efficiently when the job sizes exceed what a typical cloud ETL tool is optimized for?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Cloud ETL tools are built around assumptions about job size and data volume that don't always match what mid-market and enterprise teams are actually running. When a batch job exceeds those assumptions, whether in record count, file size, or transformation complexity, the performance gap tends to show up in ways that are expensive to work around. The question of which <a class="a a--md" elv="true" href="https://www.g2.com/categories/on-premise-data-integration">on-premise data integration</a> platforms actually hold up at high volume is one where segment-specific review data tells a more useful story than general ratings.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">On the mid-market side, tools with strong performance reviews at scale:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/microsoft-sql-server/reviews"><strong>Microsoft SQL Server</strong></a>: Enterprise and mid-market reviewers describe it handling concurrent, large-volume batch workloads through columnstore indexes, partitioning strategies, and query optimization tooling that cloud-native tools rarely match for on-premise scale. Resource hunger is a consistent flag, but reviewers say proper configuration mostly addresses it.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/fme-platform/reviews"><strong>FME Platform</strong></a>: Engineers processing large spatial and multi-format datasets describe FME as faster than SQL or Python equivalents for the same transformation tasks. Very large workspaces can become resource-intensive, but the underlying processing performance is consistently noted as a strength.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/cleo-integration-cloud/reviews"><strong>Cleo Integration Cloud</strong></a>: Mid-market reviewers in high-volume supply chain and logistics environments describe its multithreaded processing as a practical performance advantage, with concurrent processing helping large-volume integrations run without the queuing problems that simpler platforms hit.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews"><strong>SnapLogic Intelligent Integration Platform (IIP)</strong></a>: Reviewers processing large datasets from multiple cloud and on-premise sources note that SnapLogic handles high volume through its elastic architecture, though very large data volumes can cause performance slowdowns that require additional tuning.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">At what job size does your current tooling start to show strain? And has any team successfully moved large-volume workloads off a cloud ETL tool onto an on-premise platform specifically for the performance difference?</p>

##### Post Metadata
- Posted at: 14 days ago
- Author title: SEO Content Specialist
- Net upvotes: 1


## Comments
### Comment 1

Worth poking at the premise a little. When people say a job &quot;exceeds what cloud ETL can handle,&quot; nine times out of ten it isn&#39;t that the cloud tool physically can&#39;t, it&#39;s that it gets absurdly expensive or slow at that size without heavy tuning. That&#39;s a totally valid reason to go on-prem, but it changes what you&#39;re actually optimizing for. What&#39;s breaking at your volume, is it wall-clock time, is it the bill, or is it that the data legally can&#39;t leave the building? Because those three point at completely different answers.

##### Comment Metadata
- Posted at: 14 days ago





## Related discussions
- [How well does Trello scale into a larger team?](https://www.g2.com/discussions/1-how-well-does-trello-scale-into-a-larger-team)
  - Posted at: about 13 years ago
  - Comments: 6
- [Can we please add a new section](https://www.g2.com/discussions/2-can-we-please-add-a-new-section)
  - Posted at: about 13 years ago
  - Comments: 0
- [Quantifiable benefits from implementing your CRM](https://www.g2.com/discussions/quantifiable-benefits-from-implementing-your-crm)
  - Posted at: about 13 years ago
  - Comments: 4