A reliable Salesforce release process starts with the basics: changes are integrated early, tested automatically, and deployed in a repeatable way. But what works for a small team and a single org rarely holds up as teams grow, release frequency increases, and org architectures become more complex. Scaling your CI/CD pipeline means evolving those foundations — without losing the stability you’ve already built.
This post walks through that journey, from the fundamentals of continuous integration and continuous delivery to global multi-org pipelines, and explores what it takes to scale without leaving anyone behind.
Why scaling CI/CD is challenging for Salesforce teams
Salesforce isn’t like other CRMs, and the challenges of scaling your CI/CD pipeline reflect that. Teams who try to apply generic DevOps tools and practices often hit friction that slows down their development process — instead of accelerating it.
The metadata model complicates everything
Salesforce’s metadata model introduces complexity that most software development platforms don’t have. Org-level dependencies, the order of execution for triggers and automation, and the sheer variety of metadata types all create deployment risks that standard CI/CD tools aren’t built to handle.
A custom field might reference a record type that doesn’t exist in your target org. A Flow might depend on an Apex class that hasn’t been deployed yet. Profile permissions might reference objects from managed packages. These interrelated dependencies mean that a release failing isn’t a question of if — it’s a question of how often, and how quickly you can recover.
The declarative vs programmatic governance gap
As teams scale, the gap between declarative admins and programmatic developers widens. Admins may be building Flows and page layouts directly in a sandbox while developers are working in VS Code and committing Apex to version control. Both are creating valuable changes, but they’re often governed by different rules, tools, and workflows.
Without a shared deployment process, teams lose consistent review, traceability, and control over what moves between environments. This governance gap causes much more friction at scale. A small team can coordinate through conversation on Slack or Teams, but a team of 20 folks working across time zones will need access controls, approval gates, and pipeline configurations that enforce consistency without creating bottlenecks.
What good looks like: DORA benchmarks
Before diving into solutions, it’s worth understanding what high-performing Salesforce DevOps teams look like. The DORA (DevOps Research and Assessment) metrics provide a useful, industry-recognized benchmark by measuring deployment frequency, lead time for changes, change failure rate, and failed deployment recovery time. These metrics aren’t just theoretical — they align with organizational performance. Teams that do well on DORA metrics ship features faster, experience fewer outages, and report higher job satisfaction. We’ll explore how to measure and use these metrics later in this post.
Build vs buy for scalable Salesforce CI/CD
When teams decide to scale their CI/CD capabilities, they face a fundamental question: should they build their own tooling or invest in a purpose-built DevOps platform? We’ve written extensively about this decision in our build vs buy guide.
DIY tooling: the hidden costs
Standard automation tools like GitHub Actions, Azure Pipelines, Jenkins, or Bitbucket Pipelines can orchestrate Salesforce deployments. They’re familiar to developers, often already in use for other platforms, and the basic setup is well-documented.
Beyond that starting point, teams taking the DIY approach can find themselves facing significant hidden costs:
- Maintenance burden: Salesforce releases three major updates per year, each potentially introducing new metadata types and API changes. Someone has to keep your deployment scripts current — and that someone is usually a developer who could be building features instead.
- Deployment reliability: 80% of Salesforce teams say their CI builds regularly fail. When deployments fail, developers must manually investigate and unblock automation — defeating the purpose of CI/CD in the first place.
- Operational overhead: What takes 90 hours to build with generic tools can take 30 minutes with a purpose-built solution — and that’s before accounting for ongoing maintenance, troubleshooting, and the knowledge debt when the engineer who built your pipeline moves on.
- Limited Salesforce awareness: Generic tools don’t understand Salesforce metadata. They won’t catch missing dependencies, suggest fixes for common deployment blockers, or handle the complexity of deploying Profiles and Permission Sets.
DevOps solutions: avoiding tool sprawl
The alternative is a unified platform built specifically for the nuances of Salesforce DevOps. Purpose-built solutions offer pre-built integrations with version control systems like GitHub, GitLab, Bitbucket, and Azure DevOps, along with intelligent dependency analysis that catches issues before deployment. Some will even offer support to both declarative admins and pro-code developers in the same workflow, with built-in static code analysis, automated tests, and compliance checks — plus complete visibility across your entire release pipeline.
The goal is to avoid the sprawl that comes from stitching together different tools for CI/CD, testing, backup, and monitoring. Each additional tool adds integration complexity, learning curves, and potential points of failure across the whole stack.
A proven setup that scales
Whether you’re just starting with DevOps or you’re ready to expand to multi-org deployments, the same core pipeline practices apply. Getting these right early — following Salesforce CI/CD best practices from the start — creates a foundation that grows with your team.
Source-driven development and repo structure
The first principle is source control as your single source of truth. Every change — whether it originates in a sandbox or in an IDE — should flow through version control before reaching production. For teams still working org-first, adopting a source-driven approach transforms how you collaborate and track changes.
Once source control becomes the system of record, how you organize your repository starts to matter. For larger teams, consider organizing metadata by feature or functional area rather than metadata type. This makes it easier to understand what a feature branch contains and reduces the risk of merge conflicts when multiple projects are in flight.
Multi-org and multi-team pipelines
Simple pipelines flow linearly: sandbox → staging → production. That works well for smaller teams, but as teams scale, adding in more people and environments, pipelines become tangled. You might have a UK team and a US team contributing to the same codebase, long-term projects running alongside business-as-usual development, external partners or consultants working in their own sandboxes, or multiple production orgs serving different regions or business units. Distributed Salesforce teams face particular challenges that require careful planning.
At this point, your pipeline needs flexibility. You need to handle multiple sources of change, keep environments in sync, and back-propagate hotfixes from production without chaos. Gearset Pipelines addresses this by visualizing your entire development process, automating complex deployment workflows, and enforcing review standards across environments. Each team can operate independently while the pipeline keeps orgs synchronized and prevents overwrites. For teams with multiple production orgs, this becomes essential infrastructure.
For major implementations that run alongside your main pipeline — Data Cloud rollouts, partner-led projects, or large customization efforts — you’ll need a strategy to keep them in sync without blocking everyday releases. The key is to keep these projects isolated but fed with regular updates from production, so they don’t drift out of sync and create painful merges at release time. Managing long-term projects within your Salesforce CI/CD pipeline requires deliberate planning but prevents significant pain later.
Environment strategy
Your sandbox strategy has cascading effects throughout your CI/CD pipeline. Most teams need:
- Developer sandboxes: Ideally one per developer, refreshed regularly. This is where individual work happens before it enters the shared pipeline.
- Integration environment: Where multiple features come together for initial testing. Conflicts surface here rather than in UAT.
- UAT/staging: Where stakeholders validate changes before production. Should mirror production as closely as possible.
- Production: Your live environment. Changes reach here only after passing all quality gates.
Scratch orgs offer an alternative for source-driven development, providing fresh environments that can be spun up on demand. The choice between scratch orgs and sandboxes depends on your team’s workflow and technical requirements.
For each environment, establish clear conventions: naming standards, refresh cadences, who has access, and what triggers a deployment. Consistency here prevents the drift that leads to failed deployments and manual intervention.
Environment variables and approvals
As pipelines mature, security becomes increasingly complicated. Credentials should never live in code or deployment scripts. Instead, use environment variables for org-specific configuration, secure credential storage for API keys and tokens, approval gates that require designated reviewers before production deployments, and audit trails that log who deployed what, when, and why. These access controls aren’t just good practice — they’re often required for compliance checks in regulated industries. Building them into your pipeline from the start is far easier than retrofitting later.
Branching strategies: which one is right for you?
It might not be obvious straight away, but your branching strategy also has to scale with your team. Choosing the right Git branching strategy depends on your team’s size, release cadence, and tolerance for complexity. Let’s unpack some of the most popular options.
Trunk-based development
Trunk-based development keeps things simple: all developers work from a single main branch, creating short-lived feature branches that merge back quickly — often multiple times per day.
Pros: Continuous integration happens naturally, merge conflicts stay small and manageable, and the main branch is always close to production-ready. This approach also works well with automated tests and quality gates.
Cons: It requires discipline and reliable CI/CD, unfinished features need feature flags, and you have less control over what ships when.
Release branches
Release branches decouple feature development from the release schedule. Teams merge completed features into a release branch, stabilize it, then ship to production.
Pros: You get clear control over what’s included in each release, time for dedicated QA and bug fixes, and easier coordination of releases across teams.
Cons: Longer-lived branches increase merge conflict risk, can slow down delivery of urgent fixes, and mean more branches to manage and track.
Gitflow
Gitflow adds structure with dedicated branches for features, releases, hotfixes, and development. It’s comprehensive but complex.
Pros: Clear separation of concerns, support for parallel development streams, and a well-documented approach that’s widely understood.
Cons: High process overhead, potential delays to integration and feedback, and often overkill for Salesforce projects.
How Gearset Pipelines supports your branching strategy
Gearset Pipelines supports different branching strategies, so you can implement the approach that best fits how your team works.
By default, Pipelines uses an expanded branching model designed to balance DevOps best practice with the realities of Salesforce development. This means feature branches for individual work items, long-lived environment branches that represent each stage of your pipeline, and promotion branches that handle the transition between environments. Each environment has its own CI job monitoring for changes, and pull requests enforce review policies before changes can progress.
For enterprise teams managing large-scale releases, Pipelines also offers native Gitflow support as a first-class option. You can bundle multiple features into a single release branch, then move that release through environments — from dev to test to staging to production — as a single unit of change. This is particularly valuable for teams with a set release cadence who need to combine features, run comprehensive testing, and release in larger, fully validated chunks.
Whichever model you choose, your releases benefit from Gearset’s usual seamless validations, automation, and governance.
PR policies and quality gates
Quality gates are how teams make sure those standards are applied consistently. This means requiring at least one approval before merging, validating that changes can deploy to the target org, running static code analysis to catch code quality and security issues before they enter the pipeline, enforcing minimum test coverage for Apex changes, and ensuring security scans pass before production. These gates shouldn’t slow things down — they should catch issues early when they’re cheapest to fix. Automated scanning and validation mean developers get feedback in minutes, not days.
Testing strategy for speed and safety
Automated tests are the foundation of reliable deployments, especially as Salesforce CI/CD pipelines scale. But testing in Salesforce presents unique challenges that require a layered approach — and understanding Salesforce testing best practices alongside effective test automation strategies will help you build a robust safety net as your team scales. Here are the non-negotiable tests you and your team should be carrying out:
Unit tests
Salesforce requires 75% code coverage for Apex, but coverage doesn’t necessarily equal quality. Well-written unit tests verify business logic rather than just lines of code, use meaningful assertions that catch actual bugs, run fast enough to execute on every deployment, and don’t depend on specific data in the org. Gearset’s automated unit testing runs your Apex tests on a schedule, alerting you when tests start failing — before those failures block a deployment.
Static code analysis
Static code analysis catches issues that tests might miss: security vulnerabilities, code complexity, naming convention violations, and performance concerns. This is a crucial step for ensuring all your contributors are releasing changes that are consistent with your own standards and not building up technical debt.
Gearset Code Reviews automatically scans every pull request, acting as built-in quality gates and blocking non-compliant code from entering your pipeline. It doesn’t just scan Apex, but also Flows, LWCs, Agentforce, Visualforce and metadata configurations. This deep understanding of Salesforce provides much more accurate results than Apex-only tools, and is particularly valuable when reviewing AI-generated code that often misunderstands the quirks of Salesforce’s architecture.
Integration and automated testing
Unit tests verify components in isolation. Integration tests verify that components work together in a real Salesforce environment — that data triggers correctly cascade through related objects, Flows and Apex work together as expected, and external integrations behave correctly. These tests typically run against a dedicated sandbox as part of CI jobs.
End-to-end UI tests validate complete business processes through the user interface. They’re slower and can be more brittle than unit tests, but they catch issues that other test levels miss. Gearset integrates with UI testing platforms like ACCELQ, Provar, and Eggplant, triggering test suites after successful deployments. Results flow back into Gearset for complete visibility across your testing strategy.
What to avoid when scaling your CI/CD pipeline
Scaling your CI/CD practices requires discipline. Some common antipatterns undermine even well-designed pipelines.
Long-lived feature branches that persist for weeks or months accumulate merge conflicts, drift from main, and become increasingly difficult to integrate. When they finally merge, they bring risk and disruption.
The fix: keep feature branches short-lived, break large features into smaller increments, and merge to main frequently using feature flags to hide incomplete work.
Skipping environments under pressure — deploying directly to production without going through staging or UAT — might save time in the moment, but it wastes far more time once a bug reaches production.
The fix: trust your pipeline. If environments are too slow, invest in making them faster rather than bypassing them.
Manual interventions accumulate technical debt. Every time someone manually fixes a deployment or edits metadata directly in an org, those changes don’t get tracked, can’t be rolled back, and create drift between environments.
The fix: if your pipeline can’t handle something, extend the pipeline. Don’t work around it.
Neglecting quality gates often starts as a shortcut — skipping tests or reviews to move faster under pressure. That speed disappears as soon as a bad deployment reaches production and teams are forced into firefighting.
The fix: implement automated tests, security scans, and static code analysis from the start. Make quality non-negotiable.
How Gearset helps teams scale their CI/CD pipelines
Gearset is built specifically to handle the complexities of Salesforce deployments at scale. While generic DevOps tools require extensive customization and ongoing maintenance, Gearset provides reliable deployments out of the box — over 100 problem analyzers catch issues before they cause failures, and teams using Gearset achieve a 99% deployment success rate compared to roughly 50% with native tools.
Complete pipeline visibility
Gearset Pipelines gives you complete visibility across your development process, tracking pull requests, automating promotions, and keeping environments synchronized. Everyone — from admins to developers to release managers — can see what’s in flight and what’s blocking progress. Whether developers are working in VS Code or admins are building in sandboxes, Gearset provides workflows that meet people where they are. No one gets left behind as your DevOps maturity grows.
Flexible branching that evolves with your team
Your branching strategy needs to scale with your team. Gearset Pipelines supports multiple approaches — from the expanded branching model, to native Gitflow support for enterprises managing large-scale, coordinated releases. You can start simple and evolve your approach over time without reworking your entire pipeline.
Long-term project support
Major implementations don’t fit neatly into your day-to-day release cycle. Gearset’s long-term project functionality lets you run parallel workstreams — like a Data Cloud rollout or a partner-led implementation — without blocking your BAU pipeline. Projects stay isolated but receive regular updates from production, so they don’t drift out of sync and create painful merges at release time.
Continuous Delivery Rules
Once your pipeline is mature, you’ll want to automate even more. Continuous Delivery Rules let you define trusted conditions — such as “validations passed”, “tests succeeded”, or “code reviewed” — and Gearset automatically promotes changes once those conditions are met. This eliminates manual steps that slow down releases while still respecting your compliance and approval processes. No more waiting for someone to click “Promote” once all checks have passed.
Multi-org at scale
For teams managing multiple production environments, Gearset’s layered modules separate shared metadata from org-specific customizations. Define a global core baseline, then grant flexibility to regional or departmental teams. When changes are approved in the core layer, they cascade automatically to dependent layers — maintaining consistency without sacrificing local autonomy.
Built-in quality and security
Gearset Code Reviews shifts quality and security left — catching issues earlier before they escalate. Every deployment is automatically scanned for bugs or code quality issues, ensuring they meet the latest Salesforce guidance, OWASP standards, and Well-Architected principles. Combined with autofix functionality, which reduces review bottlenecks, teams can achieve consistency and governance, at scale.
DORA metrics and reporting
With DORA metrics built into the platform, you can track your DevOps performance over time, demonstrate ROI to stakeholders, and identify bottlenecks as they emerge. See deployment frequency, lead time for changes, and more — all without building custom dashboards.
To see how all of Gearset’s platform works in practice, watch how Ergon scaled their Salesforce DevOps with Gearset, or check out our webinar on succeeding at scale: how large teams can implement CI/CD and move faster.
Ready to scale? Start your journey today
The path from your first CI job to enterprise-grade release automation doesn’t mean a complete overhaul — it requires sustainable steps forward. By embedding good DevOps principles early, you create a foundation that grows with your team and adapts to whatever challenges come next.
Gearset is built to meet you wherever you are on that journey. Whether you’re automating your first deployment, implementing Pipelines for your growing team, or orchestrating releases across global multi-org architectures, you’ll have the tooling, visibility, and support to succeed.
Ready to see what scalable Salesforce CI/CD looks like? Start your free 30-day trial of Gearset — no credit card required, nothing to install in your orgs. You’ll get full access to Pipelines, automated testing, code reviews, and everything else covered in this post.
Prefer a guided walkthrough? Book a tailored demo with our DevOps experts to discuss your specific requirements and see how Gearset can transform your release process.
