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Prefect is praised for its robustness in managing complex data workflows, especially at scale, which is beneficial for teams handling large datasets. However, there is some concern about long-running jobs taking significant time when processed on a single machine, indicating potential issues with efficiency or resource allocation. The pricing sentiment is not explicitly mentioned in the available data. Overall, Prefect maintains a solid reputation among users, particularly for its capability to efficiently orchestrate data pipelines in machine learning projects.
Mentions (30d)
0
Reviews
0
Platforms
2
Sentiment
17%
1 positive
Prefect is praised for its robustness in managing complex data workflows, especially at scale, which is beneficial for teams handling large datasets. However, there is some concern about long-running jobs taking significant time when processed on a single machine, indicating potential issues with efficiency or resource allocation. The pricing sentiment is not explicitly mentioned in the available data. Overall, Prefect maintains a solid reputation among users, particularly for its capability to efficiently orchestrate data pipelines in machine learning projects.
Features
Use Cases
Industry
information technology & services
Employees
97
Funding Stage
Series B
Total Funding
$46.0M
20
npm packages
Pricing found: $100 /mo, $100 / user, $100 /mo, $100 / user
[R] How are you managing long-running preprocessing jobs at scale? Curious what's actually working
We're a small ML team for a project and we keep running into the same wall: large preprocessing jobs (think 50–100GB datasets) running on a single machine take hours, and when something fails halfway through, it's painful. We've looked at Prefect, Temporal, and a few others — but they all feel like they require a full-time DevOps person to set up and maintain properly. And most of our team is focused on the models, not the infrastructure. Curious how other teams are handling this: - Are you distributing these jobs across multiple workers, or still running on single machines? - If you are distributing — what are you using and is it actually worth the setup overhead? - Has anyone built something internal to handle this, and was it worth it? - What's the biggest failure point in your current setup? Trying to figure out if we're solving this the wrong way or if this is just a painful problem everyone deals with. Would love to hear what's actually working for people. submitted by /u/krishnatamakuwala [link] [comments]
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Deep analysis of PrefectHQ/prefect — architecture, costs, security, dependencies & more
Yes, Prefect offers a free tier. Pricing found: $100 /mo, $100 / user, $100 /mo, $100 / user
Key features include: Prefect, FastMCP, Prefect Cloud, Prefect Horizon.
Prefect is commonly used for: Automating data pipelines for ETL processes, Scheduling machine learning model training workflows, Monitoring and managing data quality checks, Integrating real-time data processing with batch workflows, Creating reproducible research workflows for data science projects, Orchestrating complex multi-step workflows across different services.
Prefect integrates with: AWS S3, Google Cloud Storage, Azure Blob Storage, PostgreSQL, MySQL, Snowflake, Databricks, Kubernetes, Docker, Slack.
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