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BevOps Philosophy

“BevOps applies DevOps principles to brewing to improve repeatability, observability, and continuous improvement while preserving the creativity and experimentation that make homebrewing enjoyable.”

Brew → Iterate → Improve → Enjoy

The BevOps philosophy is relatively simple. Beer is brewed under a wide range of conditions, so those conditions should be documented to allow for explicit repeatability of any given batch of beer. Large breweries even struggle with this to some degree. Maybe a batch of hops in a given year are particularly more bitter than previously. Maybe switching vendors for malt changed the flavor profile subtly but enough to notice. BevOps captures those changes to a specific “version” of your beer to the point where you can replicate it reliably.

The purpose of BevOps is not to eliminate creativity from brewing. The purpose of BevOps is to document this creativity so it can be repeated, improved, and shared.

BevOps may not be for everyone but the philosophy is rooted in structure, predictability, and reliability and can help any brewer make the best beer they wish to make. Readers should take the principles documented here as guidelines and not hard, fast rules. The goal of BevOps is to establish principles at a high level but leave room for flexibility in how they are implemented.

Beer Development Life Cycle (BDLC)

Beer has a lifecycle, and so it stands to reason that beer development has a lifecycle as well. BevOps seeks to incorporate the repeatability and “deployment” of beer in the same manner that DevOps does so with software.

But what are these cycles? Beer is as complex as software so let’s dig into what this lifecycle looks like.

Ideation → Recipe Architecture → Development Brew → Testing & Validation → Deployment → Monitoring & Observability → Iteration

Ideation

This is where beer takes root. It’s an idea. It’s drinking a beer and thinking, “This would taste better with different hops”, or even “I think I can make a better wit by adding a bit more coriander”. Ideation is the playground of amazing beer and absolutely where your favorite beer started.

Recipe Architecture

Once you have an idea the next step is to formulate the recipe. What is actually going to comprise this beer idea? Documenting your recipe is a critical component to creating a specific batch of beer to a specific set of expectations.

Development Brew

Development Brew is the execution phase of the Beer Development Life Cycle. During this stage the recipe architecture is transformed into a physical batch of beer through mashing, boiling, fermentation, and conditioning.

The goal of a Development Brew is not necessarily to produce a perfect beer, but to generate a real-world implementation of the recipe design that can be evaluated through Testing & Validation.

Testing & Validation

Testing & Validation is the process of determining whether a beer successfully met its intended design goals. While brewing and fermentation produce the beer, testing & validation confirms whether the resulting product aligns with the recipe specification, quality objectives, and brewer expectations.

Deployment

Deployment is the process of preparing and releasing a validated beer for consumption. Once a batch has successfully completed Testing & Validation, it enters Deployment where it is packaged, conditioned, labeled, and made available for serving. This is transferring your fermented, finished beer to bottles, kegs, growlers, or whatever you intend to distribute to your audience.

Monitoring & Observability

Monitoring & Observability is the practice of collecting, reviewing, and analyzing brewing data throughout the lifecycle of a beer in order to evaluate performance, identify trends, and inform future improvements. That’s a fancy way of saying that you track feedback from people who drink your beer (yes, even yourself!), demand for your beer, what its shelf-life is, does it get better or worse over time, etc.

Monitoring focuses on tracking known metrics and conditions. Observability expands upon monitoring by enabling brewers to understand the relationships between those metrics and the resulting quality of the finished beer.

The goal of Monitoring & Observability is not simply to collect data, but to generate actionable insights that improve future batches.

Monitoring answers:

What happened?

Examples:

Observability answers:

Why did it happen?

Examples:

Iteration

Once you monitor and observe a batch, collect metrics, and determine the final result, there is the choice to iterate on the batch. Ideally iteration is improvement, or at minimum change. Perhaps feedback from others was negative and you want to make improvements. Maybe you decide to take a beer in a different direction. Any change from a specific batch of beer should be viewed as an iteration.

Iteration is the heart of BDLC. Iteration is taking all of the other BDLC concepts and learning from them. Beer evolves but iteration isn’t even always about evolving. It’s about learning and adapting. Iteration is about change.

Every batch, successful or otherwise, produces knowledge. BevOps treats that knowledge as an asset to be documented, preserved, and applied to future releases.

The goal of iteration is not perfection. The goal of iteration is continuous improvement.

Brew → Iterate → Improve → Enjoy

Semantic Fermentioning (SemFer)

Good brewers keep detailed brew logs for their batches to review like baseball box scores. This data helps understand and improve the process over time. In BevOps, recipe versions should be defined using semantic versioning guidelines to help the brewer identify the batch and quickly determine where it is in the Beer Development Life Cycle (BDLC). BevOps defines this as “Semantic Fermentioning” or “SemFer”.

As noted previously, beer changes. That’s a natural given in brewing. It’s rare that a beer stays 100% the same forever. That’s just a natural evolution of the craft. BevOps factors that in by “versioning” batches and carefully documenting what changed to allow for repeatability. Imagine brewing a hefeweizen that you loved and then a year later brewing a slightly different batch. Maybe you didn’t particularly care for the new batch and really wanted to brew the original one you loved. Traditionally you’d have to rely on detailed brew logs and memory but with documented Semantic Fermentioning you can easily retrieve all of the ingredients, brew steps, and nuances that made a specific batch unique.

Guidelines for SemFer are as follows:

Status Designations for batches should be as follows:

Example:

beer: Agave Drift

version: 0.3.0

status: Experimental

Release Notes

One of the core BevOps principles is to keep release notes for versions of beer. Similar to software, these release notes help identify what changed to make the batch the version that is it. Release notes should be clear and concise as to what changed if this is an iteration, or what the batch is composed of if it is a new release.

Release notes should include the following at minimum:

Optionally:

Example:

Trailhead Wit v1.1.0

Summary: Increase coriander from 0.5 oz to 0.75 oz

Motivation: Enhance citrus aroma

Expected Outcome: More expressive nose

Observed Results: Pending

Service Level Indicators and Objectives (SLI/SLO)

Pre-Boil Gravity

Purpose: Did the mash perform as expected before the boil?

Example:

Target Pre-Boil Gravity: 1.040

Original Gravity

Purpose: Did the mash and boil process produce the intended wort?

Example:

Final Gravity

Purpose: Did fermentation complete successfully?

Example:

1.010–1.012

Mash Efficiency

Purpose: How effectively sugars were extracted from the grain.

Example:

Attenuation

Purpose: Fermentation effectiveness

Example:

75–80%

Fermentation Duration

Purpose: Time to production readiness.

Example:

7-10 Days

Temperature Stability

Purpose: Process consistency.

Example:

Carbonation Accuracy

Purpose: Did packaging perform correctly?

Example:

2.5 vols CO₂

Yield

Purpose: How much finished beer actually made it to packaging?

Example:

Packaging Loss

Purpose: Beer lost to trub, spills, transfer, samples

Example:

Enjoyment Rate

Purpose: Percentage of batches shared with friends, family, or the community.

Because BevOps exists to make better beer, but beer exists to be enjoyed.

Brewing Operations Reliability Assessment (BORA)

Operational frameworks like BevOps depend heavily on assessing the reliability of it. Applying a framework like BevOps is pointless without a way to determine if it’s working as expected.

In the same way that SLI and SLO metrics help track specifics of a batch like gravity, clarity, attenuation, BORA establishes metrics to help track the operational aspects of a batch.

Deployment Frequency

Purpose: How often do you brew?

Example:

3 batches/month

Lead Time for Changes

Purpose: How long from idea to product?

Example:

Lead Time = 31 Days

Change Failure Rate

Purpose: Percentage of recipe or process changes that produce an undesirable outcome.

Examples:

Mean Time To Recovery (MTTR)

Purpose: How long it takes to recover from a brewing incident.

Examples:

Recipe Stability Index (RSI)

Purpose: How frequently a recipe changes. Helps to see which recipes are stable and which are still evolving.

Example:

Trailhead Wit:

4 changes in 12 months

Rebrew Rate

Purpose: How often a recipe is brewed again.

Example:

El Pescador:

Operational Consistency Score

Purpose: How consistently you hit your targets,

Example:

Target Metrics: 10 Met Metrics: 9 Consistency Score: 90%

Documentation Coverage

Purpose: Percentage of batches that have complete documentation.

Example:

Batches Brewed: 20 Fully Documented: 18 Coverage: 90%

Beer Satisfaction Score (BSS)

Purpose: Would I brew this again?

Example:

Scale: 1-5 1 = Never again 3 = Worth revisiting 5 = Immediate rebrew

Incident Reports

Things can happen when brewing beer that you don’t intend that can have a direct impact on the batch. These incidents should always be reported and documented to help prevent them from happening again in the future.

Example:

Incident #: BEV-2026-001

Summary: Tilt telemetry pipeline failed.

Impact: Lost 12 hours of fermentation data.

Root Cause: Brewer accidentally unplugged Tilt Pico while cleaning.

Resolution: Reconnected Pico.

Preventive Action: Do not unplug production monitoring systems while drinking production monitoring outputs. 🍺

Infrastructure Roadmap

TODO: Planned for v0.2.0.

Observability & Metrics

TODO: Planned for v0.2.0.

Glossary

BDLC

Beer Development Life Cycle

BevOps

Beverage Operations. Application of DevOps principles to brewing

SemFer

Semantic Fermentioning

SLI

Service Level Indicator

SLO

Service Level Objective

BORA

Brewing Operations Reliability Assessment