Snowflake is designed to handle structured data. It’s powerful for numbers, rows, and tables. But conversations don’t look like that. They’re unstructured. They hold nuance, context, and the details that matter most when understanding customers, spotting churn risks, improving sales, or coaching teams.
That raises an important question: what happens when you try to run conversation analytics in Snowflake? Can it deliver conversation insights with the same efficiency as it does with structured data?
To find out, we ran the same workflow in two places: Snowflake and MaestroQA. Using 200 sales call transcripts, we tested three common use cases — sentiment analysis, call classification, and coaching recommendations. The results show why a purpose-built conversation analytics platform makes all the difference.
The Use Case We Tested
We worked with a dataset of about 200 sales calls pulled from Gong. Each transcript included the full conversation plus call metadata.
We set up three tasks to run across those calls:
- Sentiment analysis – measure the tone of each call.
- Call type classification – bucket calls into categories like discovery, demo, pricing, or trial.
- Coaching recommendation – prompt the AI to surface one coaching opportunity for the account executive.
These are the kinds of tasks teams actually care about. They go beyond counting calls or tracking basic metrics. They help answer questions like: How are customers feeling? What types of conversations are driving pipeline? Where does an AE need coaching?
The Snowflake Attempt
We started in Snowflake. The raw data was there — a table with all the Gong sales calls, including the full transcript for each one. To analyze it, we had to create a notebook, connect to that table, and import Python packages to access Cortex, Snowflake’s built-in AI functions. This setup required SQL and Python just to get started.
Once the workflow was ready, here’s what happened:
- Sentiment analysis ran successfully. Each transcript was tagged as positive, negative, or neutral.
- Call type classification worked, but slower. It took 59 seconds to run across 200 calls.
- Coaching recommendations broke down entirely. Running the prompt across 200 calls took more than 22 minutes before the process had to be killed. Even limiting it to 10 calls failed after 10 minutes. The only way to make it work was one call at a time, which took about 10 seconds each — because each transcript triggered its own full API call.
Other challenges quickly showed up:
- Usability: Results stayed inside a notebook. To make them usable, someone would need to build an app on top of it — requiring more engineering work.
- Cost: Every run added to compute spend. With Snowpark container services, costs continued as long as the session was active, creating the risk of ballooning spend. And cost reporting made it hard to see which workloads were responsible.
The takeaway: conversation analytics in Snowflake is technically possible, but it’s slow, expensive, and inaccessible for business teams.
The MaestroQA Experience
We ran the same test in MaestroQA with the dataset of 200 AE sales calls. Setting it up was simple: we created a worksheet, pulled in the transcripts with their metadata, and added three analyses — sentiment, call type classification, and a coaching recommendation for each call.
The difference from Snowflake was immediate. All three analyses ran in parallel, and results began streaming in within minutes. There was no need to limit the dataset, no stalled processes, and no single-call workarounds.
What made this test stand out wasn’t just speed, but usability. Worksheets are designed so anyone can work with conversation data — not just engineers. Once the data is in MaestroQA, you can filter, segment, and ask AI ad hoc questions across all conversations, or just a specific slice. The answers come back in real time, and the results can be pushed directly into dashboards.
The same workflow that broke down in Snowflake ran smoothly in MaestroQA. It scaled without issue, delivered results teams could actually use, and did so in a way that’s accessible across the company.
Key Takeaways
The same dataset and tasks led to very different results depending on the platform.
Speed & Scale- Snowflake: Couldn’t handle more than a handful of calls at once. Running 200 calls stalled or failed.
- MaestroQA: Ran hundreds of calls in parallel with results streaming in within minutes.
Usability- Snowflake: Required SQL, Python, and notebooks. Only an engineer could run it.
- MaestroQA: No code required. Anyone across the company can filter, segment, and query conversation data.
Cost- Snowflake: Continuous compute charges with little visibility into what was driving spend.
- MaestroQA: Predictable SaaS pricing designed for conversation analytics workloads.
Actionability- Snowflake: Outputs stayed locked in a notebook, making them unusable for business teams.
- MaestroQA: Insights flowed directly into dashboards, QA programs, and coaching workflows.
Snowflake struggled to deliver conversation analytics at scale. Workflows stalled, required engineers to maintain, and left outputs trapped in notebooks. MaestroQA handled the same dataset seamlessly — running hundreds of calls in parallel, giving teams across the company the ability to explore conversations, and making insights immediately actionable in dashboards and coaching workflows.
For a side-by-side comparison of the two approaches, see our full breakdown:
🔗 Conversation Analytics: MaestroQA vs. Snowflake
The Case for Purpose-Built Conversation Analytics
The test made the limitations obvious. In Snowflake, workflows stalled, prompts had to be run individually, and results stayed locked in a notebook that only engineers could access. It wasn’t usable at scale, and it wasn’t usable by the teams who actually need the insights.
In MaestroQA, the same dataset ran without friction. Analyses streamed back in minutes, could be run across hundreds of conversations at once, and were immediately usable. The process didn’t require engineering — any team could filter, segment, and query conversations directly.
Here’s why that matters: conversations are where critical signals first appear — churn risk, product feedback, compliance issues, coaching opportunities, and more. Ignoring them doesn’t just mean missing signals, it means missed revenue, weaker teams, and stalled business growth.
Snowflake was built for structured data. MaestroQA was built to bring structured and unstructured data together for conversation analytics. That’s why we can turn conversations into strategic insights that every team across the business can use.
Conclusion
The same workflow produced two very different outcomes. In Snowflake, the process was slow, costly, and failed at scale. In MaestroQA, it ran smoothly, delivered results in minutes, and made insights usable across the business.
That’s the difference between a data warehouse and a purpose-built conversation analytics platform.
If your team is relying on Snowflake or another data warehouse to analyze conversations, you’re leaving critical insights on the table. Reach out to see how MaestroQA makes conversation analytics fast, scalable, and actionable.
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