As a fun historical sidebar and an illustration that there are no new names in tech these days, Metaflow was also the name of the company that first introduced out-of-order speculative execution of CISC architectures using micro-ops. [1]
A big deal is that they get packaged automatically for remote execution. And you can attach them on the command line without touching code, which makes it easy to build pipelines with pluggable functionality - think e.g. switching an LLM provider on the fly.
I went to the GitHub page. The descriptions of the service seem redundant to what cloud providers offer today. I looked at the documentation and it lacks concrete examples for implementation flows.
Seems like something new to learn, an added layer on top of existing workflows, with no obvious benefit.
I've been curious about this project for a while...
If you squint a bit it's sort of like an Airflow that can run on AWS Step Functions.
Step Functions sort of gives you fully serverless orchestration, which feels like a thing that should exist. But the process for authoring them is very cumbersome - they are crying out for a nice language level library i.e. for Python something that creates steps via decorator syntax.
And it looks like Metaflow basically provides that (as well as for other backends).
The main thing holding me back is lack of ecosystem. A big chunk of what I want to run on an orchestrator are things like dbt and dlt jobs, both of which have strong integrations for both Airflow and Dagster. Whereas Metaflow feels like not really on the radar, not widely used.
Possibly I have got the wrong end of the stick a bit because Metaflow also provides an Airflow backend, which I sort of wonder in that case why bother with Metaflow?
Have been looking for an orchestrator for AI workflows including agentic workflows and this seemed to be the most promising (open source, free, can self-host, and supports dynamic workflows).
But have not seen anyone talk about it in that context. What do people use for AI workflow orchestration (aside from langchain)?
I've used Metaflow for the past 4 years or so on different ML teams. It's really great!
Straightforward for data/ML scientists to pick up, familiar python class API for defining DAGs, and simplifies scaling out parallel jobs on AWS Batch (or k8s). The UI is pretty nice. Been happy to see the active development on it too.
Currently using it at our small biotech startup to run thousands of protein engineering computations (including models like RFDiffusion, ProteinMPNN, boltz, AlphaFold, ESM, etc.).
Data engineering focused DAG tools like Airflow are awkward for doing these kinds of ML computations, where we don't need the complexity of schedules, etc. Metaflow, imho, is also a step up from orchestration tools that were born out of bioinformatics groups, like Snakemake or Nextflow.
Netflix used to release so much good opensource software a decade ago. Now it seems to have fallen out of developer mindshare. Seems like the odd one out in FAANG in terms of tech and AI.
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[ 1.8 ms ] story [ 30.3 ms ] thread[1] https://en.wikipedia.org/wiki/Metaflow_Technologies
A big deal is that they get packaged automatically for remote execution. And you can attach them on the command line without touching code, which makes it easy to build pipelines with pluggable functionality - think e.g. switching an LLM provider on the fly.
If you haven't looked into Metaflow recently, configuration management is another big feature that was contributed by the team at Netflix: https://netflixtechblog.com/introducing-configurable-metaflo...
Many folks love the new native support for uv too: https://docs.metaflow.org/scaling/dependencies/uv
I'm happy to answer any questions here
Seems like something new to learn, an added layer on top of existing workflows, with no obvious benefit.
If you squint a bit it's sort of like an Airflow that can run on AWS Step Functions.
Step Functions sort of gives you fully serverless orchestration, which feels like a thing that should exist. But the process for authoring them is very cumbersome - they are crying out for a nice language level library i.e. for Python something that creates steps via decorator syntax.
And it looks like Metaflow basically provides that (as well as for other backends).
The main thing holding me back is lack of ecosystem. A big chunk of what I want to run on an orchestrator are things like dbt and dlt jobs, both of which have strong integrations for both Airflow and Dagster. Whereas Metaflow feels like not really on the radar, not widely used.
Possibly I have got the wrong end of the stick a bit because Metaflow also provides an Airflow backend, which I sort of wonder in that case why bother with Metaflow?
But have not seen anyone talk about it in that context. What do people use for AI workflow orchestration (aside from langchain)?
Straightforward for data/ML scientists to pick up, familiar python class API for defining DAGs, and simplifies scaling out parallel jobs on AWS Batch (or k8s). The UI is pretty nice. Been happy to see the active development on it too.
Currently using it at our small biotech startup to run thousands of protein engineering computations (including models like RFDiffusion, ProteinMPNN, boltz, AlphaFold, ESM, etc.).
Data engineering focused DAG tools like Airflow are awkward for doing these kinds of ML computations, where we don't need the complexity of schedules, etc. Metaflow, imho, is also a step up from orchestration tools that were born out of bioinformatics groups, like Snakemake or Nextflow.
Just a satisfied customer of Metaflow here. thx
They call it a DREAM stack (Daft, Ray Engine or Ray and Poetry, Argo and Metaflow)