Millau – self-configuring ingress proxy for Docker Swarm

Hacker News

I still use Docker Swarm. It's simple, fast to set up, and easy to teach. But I got tired of managing routing to microservices. Nginx and HAProxy need config redeploys, Traefik is powerful, but its configuration via labels quickly becomes unmaintainable. So I built Millau — a free ingress proxy and load balancer for Docker Swarm microservices. Add a few labels to the service, and traffic gets routed. No proxy restarts, no config files. Example: ``` # service deploy: labels: - "millau.enabled=true" - "millau.port=9000" ``` Millau listens to Docker events, discovers labeled services, and routes traffic using host and path matching. Unlike Traefik, Millau supports load balancing across services. I use it to deploy different versions e.g. Blue, Green, Red of the same microservice. If Red crashes, Millau routes to Blue or Green. If Blue slows down, Millau marks it inactive for 60s and routes to Green. Millau supports TLS termination and mTLS. It exposes Prometheus metrics and ships with a prebuilt Grafana dashboard. It's in production, serving its own site and several side projects. Let me know what you think, especially if you're still using Docker ecosystem in prod. Thanks.

Visit product View on Hacker NewsMay 26, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: dock, using · Missing: mac, agents, macos

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AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: nginx, ide, 000 · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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