A Guide to Application Deployment Tools

Summary of key application deployment tools concepts

Beginner maturity level tools

At the beginner stage, the primary goal is to automate code compilation, configure build triggers on commits, and conduct initial testing while keeping monitoring reactive. Because they’re so fundamental, tools picked at this stage tend to stick around because changing them later means rewriting pipelines and retraining people.

Build automation

Build automation tools handle compilation, packaging, and containerization. They integrate with repositories, testing frameworks, and artifact registries to create your pipeline foundation. Jenkins, Github Actions, and Gitlab CI are very popular choices.

Jenkins gives you complete control but requires infrastructure management. You'll handle setup, maintenance, and scaling yourself. The plugin ecosystem that comes with it connects to nearly any tool you need, and customization runs deep, but you'll maintain all of it.

GitHub Actions lives inside your repositories and lets you run YAML-based workflows in the cloud. Here’s an example:

name: Build and Test
on: [push]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: '20'
      - run: npm install
      - run: npm test
      - run: npm run build

The marketplace offers pre-built actions for common tasks.

GitLab CI works with both cloud-hosted and self-hosted environments. Security scanning and compliance features are shipped out of the box, which is valuable for regulated industries.

Artifact storage

With repeatable builds in place, you need reliable storage for build outputs. Artifact registries provide versioning and lifecycle management, and they integrate with build, testing, and deployment tools.

Example build pipeline flow showing: code → build (Jenkins/Actions/GitLab) → artifacts (Docker Hub/ECR/Harbor) → testing → deployment (source)

Docker Hub works globally and supports both public and private repositories. Automated builds are triggered from Git repositories, and their image scanning provides a baseline level of security. Use Docker Hub for open source projects or when you're just starting with containers.

Cloud provider registries (Google Artifact Registry, Amazon ECR, and Azure ACR) integrate tightly with their respective cloud platforms. They’re designed to connect seamlessly with identity and access management (IAM), networking, and deployment services. Choose these when you're already deep in a single-cloud ecosystem.

Harbor is an alternative that provides complete control through self-hosting with governance policies, vulnerability scanning, and air-gapped deployment support. It is a good choice when compliance mandates prevent the use of public or cloud-hosted registries.

Basic testing

With consistent builds and artifact storage, add automated testing to catch bugs before production. Start with three testing types: unit tests that run fast, integration tests against real infrastructure, and API contract tests.

Unit testing frameworks (e.g., Jest for JavaScript or pytest for Python) integrate through YAML configuration in your CI pipeline. You can use a GitHub Actions workflow to install dependencies, run tests, and report results in under 10 lines while setting up unit tests to catch bugs early in the process, when they're cheap to fix.

Testcontainers is a library that handles integration testing against real infrastructure. Here is an example for testing database connections:

from testcontainers.postgres import PostgresContainer

def test_database_integration():
    with PostgresContainer("postgres:15") as postgres:
        # Your test gets a real PostgreSQL instance
        connection_url = postgres.get_connection_url()
        # Run tests against actual database
        assert query_database(connection_url) == expected_result

Testing against an actual database beats mocking because you catch connection issues, query performance problems, and schema mismatches that mocks hide. Testcontainers can spin up ephemeral containers, run your tests, then tear everything down.

As a final step, you can use Postman and Newman to test API contracts through collections of requests: Build collections in Postman's UI with different scenarios (happy paths, error cases, edge conditions, etc.), then run them in CI using Newman. This is critical when your service exposes APIs that downstream consumers depend on.

Simple monitoring

As deployments increase, you need visibility to debug issues and catch production problems. To set up simple monitoring, build a few dashboards and create some basic alerts based on the metrics you’re watching. The table below shows popular tools you could use.

Grafana connects to various data sources, including Prometheus, InfluxDB, and cloud-native monitoring systems. Start with Grafana when you want open-source flexibility and already have metrics sources. Begin simple, then evolve into a full Grafana stack (Mimir for metrics, Loki for logs, Tempo for traces) as needs grow.

Datadog and Dynatrace are full-stack platforms using agents to discover and monitor infrastructure. Both offer AI-based alerting and extensive integrations at premium prices. Dynatrace automates more with a more straightforward agent installation but requires less customization; Datadog requires more setup but lets you tune exactly what you want.

Splunk is good at log search and correlation across multiple sources. When incidents span microservices and you need to correlate logs from dozens of different components, Splunk aggregates them into searchable timelines.

Advanced maturity level tools

With orchestrated deployments running smoothly, the next step is making them “intelligent.” Advanced maturity is when deployments monitor themselves and respond automatically to metrics. For example, when canary deployments show elevated error rates, a rollback would happen without human intervention.

Advanced deployment orchestration

Progressive delivery automates canary and blue/green deployments with continuous monitoring of error rates, latency, and resource usage. Healthy metrics trigger automatic progression, while degraded metrics trigger automatic rollbacks. A couple of popular tools include Argo Rollouts and Flagger.

Argo Rollouts provides progressive delivery for Kubernetes applications. Just like Argo CD, Argo Rollouts uses YAML based configurations, as shown here:

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: checkout-service
spec:
  strategy:
    canary:
      steps:
      - setWeight: 20
      - pause: {duration: 5m}
      - setWeight: 50
      - pause: {duration: 5m}
      - setWeight: 80
      - pause: {duration: 5m}
      analysis:
        templates:
        - templateName: success-rate

In the code above, Argo Rollouts is set to query your monitoring system during each pause. If error rates exceed thresholds, it aborts and rolls back automatically. Argo Rollouts is ideal for Kubernetes-native progressive delivery with GitOps integration.

Flagger provides similar capabilities inside the Flux ecosystem. If you already use FluxCD, Flagger integrates without introducing another tool set.

Comprehensive testing

Advanced testing is supposed to validate applications across browsers, devices, and failure scenarios. Teams implement this type of testing to simulate varying traffic patterns and intentionally inject failures to verify resilience.

BrowserStack provides access to thousands of real browser and device combinations. You can run automated tests (Selenium, Playwright, Cypress) across BrowserStack's infrastructure through CI/CD pipeline integration.

Another practice that can be used to simulate failure is chaos engineering, which is typically used to inject failures into production to test system resilience. A popular tool for doing chaos engineering is Chaos Monkey; an example is shown below that is configured to terminate a target group of servers and inject latency:

chaos_config = {
    "terminate_instances": {
        "probability": 0.01,
        "target_groups": ["web-servers"]
    },
    "inject_latency": {
        "delay_ms": 1000,
        "probability": 0.05
    }
}

Release orchestration

Release orchestration coordinates multiple interdependent deployments as a single release. For instance, this could involve deploying database migrations before dependent services and rolling out infrastructure changes before application updates.

Octopus Deploy orchestrates complex enterprise deployments with approval workflows and audit logs:

steps:
  - name: Deploy database migration
    action: run-script
  - name: Wait for DBA approval
    action: manual-intervention
    required_approvers: ["dba-team"]
  - name: Deploy API service
    depends_on: ["Deploy database migration"]
  - name: Deploy frontend
    depends_on: ["Deploy API service"]

In the example above, each step waits for the previous one to complete, with manual intervention gates requiring team approval.

Another tool is GitLab Auto DevOps, which can detect your application type (Node.js, Python, Ruby) and automatically create pipelines that build, test, scan for security vulnerabilities, and deploy to Kubernetes. Use Auto DevOps when you want convention over configuration and your application fits standard patterns.

Pipeline observability

Pipeline observability monitors the delivery machinery itself: build times, failure rates, deployment frequency, and bottlenecks. Typically, CI/CD tools tightly integrate such features.

GitHub Actions Analytics and GitLab CI Analytics provide built-in dashboards showing:

• Workflow run times by branch
• Success vs. failure rates
• Deployment frequency and change lead time
• Bottleneck identification in multi-step workflows

Both platforms track DORA metrics automatically when you configure deployment jobs.

Jenkins monitoring exposes pipeline metrics to Prometheus for Grafana visualization. A typical query looks like this: sum(rate(jenkins_job_duration_seconds[5m])) by (job_name)

A good practice is to correlate pipeline performance with deployment outcomes. For example, when deployment frequency drops from 5 per day to 2, pipeline observability would reveal whether the cause is slower builds, higher failure rates, or bottlenecks in specific stages.

Expert maturity level tools

At advanced maturity, you have progressive deployments with automated rollbacks. Expert maturity goes further: SLOs become governance tools that control deployments automatically and error budgets gate releases. When reliability degrades and the budget depletes, the system halts new deployments until recovery.

SLO platforms and error budget enforcement

When teams work independently with different monitoring tools, a tool like Nobl9 provides the integration layer, as shown below.

Nobl9 integrates with all monitoring sources to calculate unified SLOs:

apiVersion: n9/v1alpha
kind: SLO
metadata:
  name: checkout-availability
  project: ecommerce
spec:
  service: checkout-system
  budgetingMethod: Occurrences
  objective: 99.9
  timeWindows:
    - unit: Day
      count: 30
  indicator:
    metricSource:
      name: prometheus-prod
    spec:
      prometheus:
        query: |
          sum(rate(checkout_requests_total{status="success"}[5m]))
          / sum(rate(checkout_requests_total[5m]))

In the example above, with a 99.9% SLO, you have a 0.1% error budget over 30 days. When error rates spike and burn through 80% of the monthly budget in three days, Nobl9 signals deployments to halt.

Nobl9 SLO view showing burn rate summary, showing targets, reliability percentage, burn rate status, and remaining budget percentage.

For a more robust monitoring system, you can (and should) integrate error budgets with deployment pipelines, for example, here in GitHub Actions:

# GitHub Actions deployment gate
BUDGET=$(curl -H "Authorization: Bearer $TOKEN" \
  "https://api.nobl9.com/v1/slos/checkout-availability/error-budget" \
  | jq '.remaining_percentage')

if [ $(echo "$BUDGET < 25" | bc) -eq 1 ]; then
  echo "Error budget below 25% - blocking deployment"
  exit 1
fi

Your orchestration tools can query the Nobl9 API and check error budgets before proceeding, enabling automatic governance. Deployments occur freely when reliability is good, and pause automatically when reliability degrades.

Nobl9 can also aggregate multi-source SLOs by calculating composite SLOs across services:

apiVersion: n9/v1alpha
kind: SLO
metadata:
  name: checkout-composite
spec:
  service: checkout-system
  objective: 99.5
  composite:
    operator: AND
    components:
      - project: ecommerce
        slo: payment-service-availability
        weight: 40
      - project: ecommerce
        slo: inventory-service-availability
        weight: 30
      - project: ecommerce
        slo: frontend-availability
        weight: 30

The composite SLO calculates overall checkout reliability from component SLOs. When any component degrades significantly, error budget enforcement applies across the entire flow.

Nobl9 composite SLO view showing how component SLOs roll up to system-wide reliability

Advanced observability

Advanced observability is about active governance. At this level, observability platforms feed data into SLO tools for unified reliability calculation, and traces validate whether experiments stay within error budgets.

OpenTelemetry collects traces and metrics from all services, sending them to your observability backend (Jaeger, Prometheus, Datadog). Nobl9 supports OpenTelemetry and pulls from these backends to calculate SLOs, creating closed-loop reliability management where observability data directly drives deployment decisions.

For example, during canary deployments, trace data can be used to determine whether the new version increases latency. Similarly, when a feature flag enables a new recommendation algorithm and traces reveal that it adds 200 ms to the p99 latency (violating your SLO), the system automatically disables the flag. Distributed tracing tools like Jaeger and Zipkin feed trace data into Nobl9, validating whether canary deployments and feature flag experiments stay within error budgets.

Production testing

Production testing validates changes in the live environment before exposing them to all users. Teams can use feature flags to progressively deploy new features while measuring the impact on SLOs and can use synthetic monitoring to track real user behavior.

LaunchDarkly manages feature flags with automatic kill switches based on SLO violations. LaunchDarkly integrates with Nobl9 to continuously monitor error budgets. For example, when an error budget drops below 20%, the flag is disabled automatically, reverting all users to the legacy flow without redeployment.

Synthetic monitoring (Datadog Synthetic, Dynatrace Synthetic, New Relic Synthetics) simulates user journeys continuously from multiple global locations. Tests can be run at a fixed interval (ex, 5 minutes), executing the full checkout flow. When a deployment breaks the payment form or increases response time beyond thresholds, synthetic monitoring alerts you before customers notice.

Business integration

When you connect metrics to business outcomes, you can, for example, demonstrate that maintaining 99.9% SLO compliance increases customer retention by 3%, or show that error budget depletion correlates with $50K per day in revenue impact during incidents. Connect technical metrics directly to business outcomes to demonstrate that your reliability investments protect revenue.

Nobl9 business intelligence integration connects SLO data with business intelligence platforms by letting you export your data to analyze later:

apiVersion: n9/v1alpha
kind: DataExport
metadata:
  name: executive-dashboard
spec:
  exportType: BigQuery
  destination:
    datasetId: reliability_metrics
  schedule: "0 */6 * * *"

You can query SLO and business data together:

SELECT slo.date, slo.error_budget_remaining, revenue.daily_revenue
FROM reliability_metrics.slo_data slo
JOIN revenue_data.daily_summary revenue ON slo.date = revenue.date

You can then build role-specific dashboards that speak directly to your stakeholders, as shown below.

At expert maturity, where deployments need automated control, manual gates create bottlenecks as deployment frequency increases. On the other hand, automated gates without reliability context create outages by allowing deployments during degraded states. Nobl9, by providing the right SLO based visibility, helps you control your deployment: Release when reliability is good, halt when budgets deplete.