Our team is currently evaluating a migration from a legacy Jenkins-based CI/CD system to a more modern, scalable platform. The primary contenders are Buildkite and GitHub Actions. I am seeking detailed, empirical comparisons from teams with similar profiles, specifically concerning distributed Python and Machine Learning workloads.
Our environment and requirements are as follows:
* **Team Structure:** 25 engineers distributed across three time zones, working on a monorepo containing ~15 microservices and numerous standalone ML training pipelines.
* **Workload Characteristics:**
* Python 3.9+ applications and libraries (FastAPI, PyTorch, scikit-learn).
* Integration tests requiring significant compute (GPU-enabled for model evaluation).
* Dependency management via Poetry, with complex, layered caching requirements.
* Artifacts include Docker images, PyPI packages, and serialized ML models.
* **Current Pain Points with Jenkins:**
* Orphaned, undocumented pipeline logic (Groovy).
* Unreliable scaling of ephemeral agents, leading to queue congestion.
* Poor observability into pipeline performance and cost attribution.
* Security model for secrets is cumbersome and audit-trail deficient.
My preliminary analysis has identified the following architectural trade-offs:
**Buildkite's Agent-Based Model:**
* **Pro:** Full control over the underlying host (essential for GPU workloads). We can provision heterogeneous agents (e.g., high-memory for data processing, GPU instances for training) and scale them via our existing cloud autoscaling groups.
* **Pro:** Pipeline definitions reside in our repository (`pipeline.yml`), but the orchestration logic is decoupled from the vendor's infrastructure.
* **Con:** Operational overhead of managing the agent pool lifecycle, though this can be automated with Terraform and instance templates.
* **Con:** Native artifact storage is less sophisticated; likely requires integration with S3 and a custom caching layer for Poetry/virtualenvs.
**GitHub Actions' Managed Model:**
* **Pro:** Tight, low-friction integration with the GitHub repository, including PR checks, secret management, and environment protection rules.
* **Pro:** Built-in matrix builds for cross-platform testing, though limited for custom hardware.
* **Con:** Limited control over runner specifications for managed GitHub-hosted runners. The largest GPU runner may be sufficient, but cost escalates quickly. Self-hosted runners mitigate this but introduce a hybrid management burden.
* **Con:** Vendor lock-in for pipeline logic (YAML). Complex pipelines can become verbose and challenging to modularize without third-party actions.
The critical unknowns for me are not in the feature checklist, but in the longitudinal, operational experience:
1. **Secrets Migration:** How did you migrate or replicate secrets from Jenkins to the new platform? Did you use a secret manager (HashiCorp Vault, AWS Secrets Manager) as an intermediary abstraction layer? What was the process for auditing and rotating all secrets post-migration?
2. **Pipeline Translation Pain:** For those who migrated complex pipelines, what percentage of logic was translatable 1:1? Were there specific patterns (e.g., dynamic pipeline generation, parallel fan-out/fan-in) that were particularly troublesome to reimplement in either Buildkite or GitHub Actions?
3. **Performance & Cost Reality:** After migration, what was the measurable impact on mean time to completion for a standard pipeline (e.g., pull request validation)? More importantly, how did your cost profile change? Was the shift from a fixed CAPEX model (Jenkins servers) to a consumption-based model (managed runners/agents) predictable?
I am particularly interested in any concrete performance benchmarks or cost data from similar teams. For example, a comparison of a standardized ML training test suite run on a `g4dn.xlarge` equivalent across a self-hosted Buildkite agent versus a GitHub Actions self-hosted runner, measuring both total execution time and total compute cost.
Any insights into the observability and debugging experience during the migration itself would also be valuable.
Data over dogma