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Nextflow vs. Snakemake

· 7 min read

If you're writing a bioinformatics pipeline, chances are you're reaching for either Nextflow or Snakemake as your workflow manager. Just like tabs vs. spaces or iOS vs. Android, the debate between Nextflow and Snakemake is common across teams. Both Snakemake and Nextflow have grown in popularity, and they now comprise the majority of new pipelines written.

Chart showing Nextflow and Snakemake GitHub stars increasing rapidly over time

At a glance, Nextflow's advantages are a broad network of integrations and its "top-down" approach – which is more intuitive for some. Others turn away from Nextflow due to its Groovy base, a Java dialect, or other quirks. Snakemake attracts those familiar with Python and GNU make, but lacks the broad scope of Nextflow's ecosystem.

There are two dealbreakers that can make your choice of pipelining language simple:

  1. Do you love Python or hate Java or Groovy?

If so, choose Snakemake. It's a Python project, whereas Nextflow is built on top of Groovy – a Java dialect.

  1. Can the order of the pipeline be defined by the files each step produces?

If not, choose Nextflow. Snakemake effectively requires a pipeline step definition to include its input files and output files, which are used to determine when to run the step. In comparison, Nextflow steps are dynamically ordered by the results of previous steps, and the steps can return raw data.

If neither of the above apply, read on.

Common benefits and limitations

Both Nextflow and Snakemake help write reproducible pipelines, especially when the alternative is a collection of scripts that have been cobbled together by pure Python, R, or shell.

Both also have limitations, in part because of how much flexibility they lend towards a bioinformatics use-case – which often deviates from standard software engineering best practices.

Benefits of using Nextflow or Snakemake

  • You can easily turn a wide variety of code into a more robust pipeline. That includes Python, R, and shell scripts – or even Jupyter notebooks.
  • Support for reproducible environments through containerization (e.g. using Docker images) or Conda.
  • Retrying failed pipelines is faster with built-in caching of succeeded steps.
  • They provide guardrails for good pipelining practices.
  • Both are open-source with active communities, and have free basic options.

Limitations of using Nextflow and Snakemake

  • Both have built-in support for offloading tasks to the cloud, but the robustness is dependent on your scale and deployment mechanism.
  • Template pipelines, like Nextflow's nf-core or Snakemake Workflows, aren't plug-and-play; they still require a deep understanding of the pipeline and the unique needs of your analysis.
  • Those trained as software engineers usally find both Nextflow and Snakemake more frustrating than traditional workflow managers like Airflow, Luigi, or Prefect.
  • Both stem from an academic background, which led to some design decisions that are suboptimal for use in industry.
  • Built-in observability and developer tooling leaves room for improvement.


Dealbreakers, slowdowns, and unique benefits

Nextflow has a popular set of template pipelines (nf-core) and a larger number of employees working on maintaining their core – but it's a polarizing workflow language.

Common dealbreakers

  • It's built with Groovy, a dialect of Java, which has a steep learning curve and complex syntax. It's rare to see Groovy used anywhere else in this industry, so the learning the language isn't a transferable skill.


  • How Nextflow defines pipeline I/O – channels – seems to have a steeper learning curve than Snakemake.
  • While there are many integrations, most are buggy.
  • There's a lot of boilerplate.

Unique benefits of Nextflow

  • How Nextflow defines pipeline I/O – channels – supports more than just files as the output of a step, and pipelines can be dynamically re-ordered.
  • It's easier to get commercial support. That's probably because they're a venture-funded company that values revenue, while Snakemake is maintained by an academic lab.
  • nf-core pipelines have better documentation.
  • Fully-managed cloud support is native to Nextflow Tower (with many features behind a paywall).


  • It creates many temporary work directories with an unintuitive set of symlinks and temporary files.
  • The venture-funded company that runs Nextflow self-promotes heavily (e.g. on Reddit and Wikipedia), which is uncommon in the bioinformatics community.


Dealbreakers, slowdowns, and unique benefits

Snakemake is the spiritual child of Python and GNU make (hence the name "snakemake"). It works well with vanilla Python, and pipelines are defined in a way similar to GNU make's makefiles.

Common dealbreakers

  • The structure of the pipeline is based on output files for each step, which is clunky for steps which don't produce files.
  • Built-in execution support is more polished for cluster execution than cloud execution. There's no native support for running pipelines in a managed service in the cloud (which we patch with FlowDeploy).
  • No enterprise support.

An aside: cloud and cluster support with Snakemake

A few years ago, cloud and cluster support was much better in Nextflow, and many chose Nextflow over Snakemake solely for that reason. The ecosystem around Snakemake has caught up since then, and it's no longer a clear dealbreaker.


  • Documentation lacks short and straightforward examples.

Unique benefits of Snakemake

  • It's Python! You can use Snakemake directly in Python or through the command-line.
  • Using output files to define a pipeline's structure is easier to understand for some.
  • The dry run feature helps test pipelines, which is possible due to how Snakemake structures pipelines. To do the same in Nextflow is an experimental feature that requires separate manual "stubbing" of pipeline steps.


  • It's maintained by an academic group.


Making Nextflow and Snakemake more capable

FlowDeploy makes both Nextflow and Snakemake cloud native. It's agnostic to your choice of workflow language, but adds custom-tailored compute, data management, and observability features to the two workflow managers.

Disclosure: this section promotes our paid product, but I genuinely believe it changes the Nextflow vs. Snakemake calculus.

Eliminates some deal breakers

  • Adds cloud deployments at scale, even for Snakemake. (This is the only fully managed way to run Snakemake in the cloud, as of this article.)
  • Support, with an SLA.

Removes slowdowns

  • Adds better observability and monitoring, including live log streaming and Slack status alerts.
  • Adds a GitHub integration to import pipelines and their versions.
  • Adds templates to quickly run pipelines and share the configuration with your teammates.

Added benefits

  • A UI that you can access from anywhere.
  • A Python client and API for programmatic pipeline deploys.
  • Default data architecture scales well without sacrificing flexibility or usability.


  • It only works with cloud computing. That means it doesn't work for an on-prem SLURM cluster (yet).
  • It's a paid tool, unless you manage your own infrastructure under the hood and are in academia.


The debate between Nextflow and Snakemake is more about preference and specific use-case than one being better than the other. Both have carved out their own community within the bioinformatic community, and either might be best for you.

The best way to decide what to use is to try them out!