Our team of 10 engineers recently completed a full migration from CircleCI to Buildkite, and I wanted to share a structured look at the process, the pain points, and the outcomes. The primary drivers were cost predictability and the need for more control over our build environment, especially for some specialized data science workloads.
**Timeline & High-Level Process**
* **Days 1-15:** Evaluation & Proof of Concept. We ran parallel builds for a non-critical service.
* **Days 16-45:** Incremental Pipeline Translation & Secrets Migration. We moved service-by-service.
* **Days 46-75:** Full Cutover & Decommissioning of CircleCI. A two-week "trial by fire" with all builds on Buildkite.
* **Days 76-90:** Optimization & Documentation. Tuning performance and writing internal runbooks.
**Key Decisions and Translation Pain**
The biggest conceptual shift was moving from a SaaS-only model to a hybrid model with self-hosted agents. Buildkite's pipeline configuration felt more declarative and was surprisingly straightforward to translate. The main friction came from:
* Re-imagining our caching strategy. We moved from CircleCI's built-in caching to a more explicit S3-based approach.
* Secrets migration required a one-time effort to move from CircleCI contexts to AWS Secrets Manager, integrated via Buildkite's agent hooks.
* Recreating "orbs" functionality. We built a small library of reusable pipeline steps defined in our own YAML files, which turned out to be more transparent and version-controlled.
**The Good, The Bad, and The Cost**
* **Control:** The ability to choose our agent instance types (GPU-enabled for ML jobs) was a game-changer.
* **Cost:** Our Buildkite bill (including EC2 costs for agents) is roughly 60% of our former CircleCI bill for comparable workload. The predictability is a major plus.
* **Operational Overhead:** This is the trade-off. We now own the agent infrastructure. Setting up monitoring and auto-scaling required about 5 engineer-days.
**Our Current Verdict**
For a team of our size and with our specific needs for environmental control, the migration has been a net positive. I wouldn't recommend it for a team that wants a purely hands-off, SaaS-only experience. The process required a dedicated "pilot" engineer for about 50% of their time over the 90 days. If you're considering a similar move, my top advice is to start with a robust proof of concept that includes your most complex pipeline.
I'm an infrastructure lead at a 50-person fintech, managing our AWS estate and CI/CD for ~15 microservices using Terraform and Terragrunt, and we've run both CircleCI and Buildkite in production over the last three years.
1. **Cost Predictability vs. Cost Efficiency**: Buildkite's model is predictable (flat $15/seat/month for core features), but your real spend is on the compute. For a 10-person team, your EC2 or GCE bill for persistent agents will likely match or exceed the Buildkite subscription. CircleCI's consumption pricing can be cheaper for small, bursty workloads but becomes punishingly opaque at scale - we saw bills fluctuate by 40% month-to-month with similar commit volume.
2. **Configuration and Control**: Buildkite's pipeline-as-code is a clear win for control. You define everything in a `.buildkite/pipeline.yml` file, and it can call out to scripts, Docker, or Terraform modules. CircleCI's config feels more like a DSL within their walled garden. The specific detail: Buildkite lets you SSH into any running build agent for direct debugging, which is invaluable for flaky integration tests.
3. **Stateful Workload and Caching Headache**: You identified the caching shift. Buildkite has no built-in cache mechanism, forcing you to implement your own (S3, GCS, etc.). This is extra work but results in a faster, more transparent system. Our S3-backed cache layer for Python and Node modules cut average build time from 12 minutes to ~4 minutes after the warm-up.
4. **Operational Overhead**: This is Buildkite's main trade-off. You are responsible for your agent fleet - scaling, patching, and monitoring. We spent roughly one engineer-day per month on agent maintenance. CircleCI is truly hands-off. If you don't have the infra skills in-house, this overhead can sink the Buildkite value proposition.
I'd recommend Buildkite for teams that already have strong infrastructure-as-code practice and need fine-grained control over their build environment, especially for data-heavy or GPU-dependent pipelines. If your team is purely application-focused and values a fully managed service above all, stick with CircleCI. To make the call clean, tell us your team's comfort level managing EC2/GCE instances and your monthly build minute volume on CircleCI.
The timeline breakdown is useful. Most teams underestimate the optimization phase.
> Re-imagining our caching strategy
That's the real time sink. CircleCI's cache is basically a black box. With Buildkite/S3, you see every miss and can actually tune it. Our build times dropped 30% after we stopped caching useless node_modules directories.
Curious if your data science workloads used GPU agents and how you handled that scaling. Spot instances?
Show me the numbers.