DevOps as a Service | CI/CD & Kubernetes
Manual deployments are slow, brittle, and expensive. Every deployment that requires human steps to complete is a deployment that can go wrong in unpredictable ways. Rollbacks are worse than the original deployment. Environment configuration lives in someone's head. The staging environment stopped matching production three months ago and nobody knows why.
We build DevOps infrastructure that makes deployments fast, reliable, and automatic. CI/CD pipelines, containerisation, infrastructure as code, monitoring and observability. Engineering teams that spend their time building features instead of managing deployments.
CI/CD pipelines that test, build, and deploy automatically on every merge to main
Containerised application environments using Docker and Kubernetes that are identical across dev, staging, and production
Infrastructure as code using Terraform so your environments are reproducible and version-controlled
Monitoring and alerting configured from day one so you know about production issues before your customers do
Recent outcomes
Voice AI · Research
Text-based interviews converted to automated phone calls
6× deeper insightsAI Automation · Ops
Manual invoice OCR across 40+ gas stations
20k+ txns day oneLoyalty · Retail
SuperValu & Centra loyalty platform with receipt validation
1,062 users in 4 weeksSaaS · Logistics
Multi-carrier shipping hub for Indonesian eCommerce
2,000+ shipments yr 1Recognition
How long does your team spend on each production deployment, and how often do they go wrong?
When staging does not match production, how much of your QA effort is wasted finding environment-specific bugs?
In short
RaftLabs provides DevOps as a Service including CI/CD pipeline setup on GitHub Actions, GitLab CI, or CircleCI, Docker containerisation and Kubernetes orchestration, infrastructure as code with Terraform, monitoring and observability with Datadog or Grafana, and security scanning in pipelines. Most teams go from manual deployments to automated pipelines in 6 to 12 weeks. All engagements are scoped at a fixed price after an assessment of your current deployment process.
Trusted by
Engineering teams that spend significant time on deployments are not slow because of the engineers, they are slow because the deployment process requires human coordination, manual steps, and tribal knowledge. Every step that requires a human decision is a step that can fail unpredictably.
DevOps is the discipline of making the deployment process systematic, automated, and reliable. The engineering team ships features. The pipeline handles the rest.
Capabilities
Automated build, test, and deployment pipelines using GitHub Actions, GitLab CI, CircleCI, or your preferred tool. Every merge triggers the full pipeline: automated tests, linting, security scanning, artifact build, and environment deployment. Deployment approval gates for production. Branch-based deployment rules: feature branches deploy to dev, main deploys to staging, tagged releases deploy to production. Pipeline notifications to Slack or Teams with deployment status and links to deployment logs.
Pipeline architecture optimised for speed without compromising safety. Parallel test execution using job matrices splits a 20-minute sequential test suite into 5 parallel jobs completing in 5 minutes. Test result caching and dependency caching (npm/pip/Maven/Gradle) eliminates repeated work across pipeline runs. Docker layer caching in multi-stage builds reduces image build time from minutes to seconds for incremental code changes. Deployment strategies configured per environment: blue-green deployments for zero-downtime production releases (new version starts alongside old, load balancer shifts traffic after health checks pass, old version kept for immediate rollback for 15 minutes), canary releases for risk-sensitive changes (1% traffic routed to new version, automated rollback if error rate or latency exceeds threshold), and rolling updates for stateless services. DORA metrics, deployment frequency, lead time for changes, change failure rate, mean time to recovery, instrumented from day one so your team has the baseline to measure improvement from. Feature flags via LaunchDarkly or Unleash decouple deployment from release: code ships to production before the feature is visible to users, reducing deployment risk.
Application containerisation using Docker. Multi-stage Dockerfile builds that produce minimal, secure production images. Consistent environments from developer laptop to production: no more "works on my machine" incidents. Docker Compose for local development environments that mirror production. Container image scanning for known vulnerabilities before deployment. Image tagging strategy aligned to your deployment process. The foundation that makes your application environment-agnostic and portable across cloud providers.
Kubernetes cluster setup on AWS EKS, Azure AKS, or Google GKE. Deployment configurations with replica counts, resource limits, health checks, and rolling update strategies. Horizontal Pod Autoscaler configuration for traffic-based scaling. Service mesh setup (Istio or Linkerd) for service-to-service communication, traffic management, and observability in microservices architectures. Kubernetes RBAC configuration with least-privilege access. Persistent storage configuration for stateful workloads. Operations runbooks for common Kubernetes tasks your team will need to perform independently after delivery.
All infrastructure defined in Terraform. Networking (VPC, subnets, security groups), compute (EC2, ECS, Lambda), databases (RDS, ElastiCache), load balancers, IAM roles, and DNS, all as version-controlled code. Remote state management in S3 with DynamoDB locking. Module structure for reusable components across environments. Terraform CI/CD integration: plan output on pull requests, apply on merge.
IaC security scanning runs in CI on every Terraform change: tfsec and Checkov identify misconfigured security groups (0.0.0.0/0 ingress on non-public ports), S3 buckets missing public access blocks, RDS instances without encryption at rest, and 200+ other AWS/Azure/GCP security policy violations, reported as PR comments before infrastructure is applied. Infracost cost annotations on every PR show the monthly cost delta of each Terraform change: adding a NAT gateway or changing an RDS instance class shows the dollar impact before the apply. Drift detection scheduled daily using terraform plan --detailed-exitcode against production state, infrastructure changes made outside of Terraform are detected and flagged within 24 hours so your state never silently diverges from reality. Sentinel policies (Terraform Cloud/Enterprise) or Open Policy Agent (self-hosted) enforce organisational guardrails: production environments require multi-AZ RDS, no SSH access from public IPs, all S3 buckets must have versioning enabled. Policy-as-code means guardrails are applied consistently across every engineer and every deploy, not just when someone remembers to check.
Full observability stack setup: metrics, logs, and traces. Infrastructure and application metrics in Datadog, Grafana with Prometheus, or CloudWatch. Centralised log aggregation with structured logging and searchable log streams. Distributed tracing for multi-service architectures. Synthetic monitoring for critical user journeys. Dashboard creation for the metrics your engineering and operations teams need during incidents. Alerting with defined severity levels, on-call routing, and escalation policies. Incident response runbooks for the most common failure modes. The visibility layer that moves from reactive fire-fighting to proactive issue detection.
Security scanning integrated into your CI/CD pipeline as a blocking gate, not a reporting-only tool. Dependency vulnerability scanning using Snyk, Dependabot, or Trivy. SAST (static application security testing) for code-level vulnerability patterns. Container image scanning before deployment. Secret scanning to detect credentials accidentally committed to source control. Infrastructure configuration security checks using Checkov or Terrascan. Each finding categorised by severity with blocking thresholds your team defines. Security as a development discipline, not an audit that happens after deployment.
Tell us how your team currently deploys and where the friction is. We will scope the DevOps infrastructure that removes it.
Cloud Migration
moving to cloud infrastructure that DevOps tooling runs on.
Quality Assurance
automated testing that runs in the CI/CD pipeline we build.
Software Modernisation
modernising applications alongside improving deployment processes.
Product Engineering
full-stack product development with DevOps built in.
API Development
API development with CI/CD and containerisation from the start.
A production CI/CD pipeline has four stages. Continuous Integration is triggered by every code push: automated tests run (unit, integration, end-to-end), linting and static analysis check code quality, and security scanning identifies known vulnerabilities in dependencies. If any check fails, the pipeline fails and the merge is blocked. Continuous Delivery builds a deployable artifact from the passing code: a Docker image, a compiled binary, or a packaged application. It pushes that artifact to a container registry or artifact store and tags it with the commit reference. Continuous Deployment promotes the artifact through environments automatically: to staging on merge to the main branch, with approval gates before production. Each stage runs the same artifact through the same configuration, eliminating environment-specific surprises. The result is a deployment pipeline that takes 10-15 minutes from merge to production rather than a half-day manual process, runs without human intervention for routine deployments, and produces an audit trail of every deployment with the exact code version and who triggered it.
Kubernetes is often overkill for smaller applications and the right choice for others. Kubernetes solves specific problems: running multiple service instances across multiple nodes, automatic failover when a node or container fails, rolling deployments that update containers without downtime, and auto-scaling compute based on load. If your application is a single service that runs on one or two servers and traffic is relatively stable, Kubernetes adds operational complexity without meaningful benefit. A simpler setup, a load balancer in front of two EC2 instances or a managed container service like AWS ECS or Google Cloud Run, is easier to operate and cheaper to run. If your application is a set of microservices, has variable traffic that needs auto-scaling, or needs the kind of resilience that requires multiple replicas across availability zones, Kubernetes is the right foundation. We assess your application architecture, traffic patterns, and team operational capacity before recommending. We do not default to Kubernetes for every project.
Infrastructure as code (IaC) means your cloud infrastructure, servers, databases, load balancers, networking, IAM policies, DNS records, is defined in configuration files that are checked into version control, rather than created manually through the AWS or Azure console. The practical benefits are reproducibility (you can create an identical environment from the code in 20 minutes), auditability (every infrastructure change is a code change with a review and commit history), and reliability (environments do not drift apart over time because they are all created from the same source). When someone creates a database by clicking through the console and does not document it, that database exists until someone deletes it and nobody knows why it is there. When a database is defined in Terraform, it is a code resource with a history, an owner, and a clear reason to exist. We deliver all infrastructure as Terraform code so your team inherits infrastructure they can modify, review, and rebuild.
We configure monitoring across three layers. Infrastructure monitoring covers compute utilisation, memory, disk I/O, and network on your servers and containers. Application performance monitoring tracks request rates, response times, error rates, and database query performance. Business metrics monitoring tracks the signals that matter to your business: successful transactions, user sign-ups, checkout completions. Alerting is configured to page the right person for the right severity: a brief spike in error rate might log a warning, a sustained spike pages the on-call engineer, a full service outage pages the team lead. We configure alert thresholds based on your baseline traffic patterns rather than generic defaults, write runbooks for the most common alert types so on-call engineers know what to check first, and integrate with your existing communication tools (PagerDuty, Slack, OpsGenie). The goal is detecting problems before your customers do and giving the on-call engineer the context to respond quickly.
A focused DevOps engagement — CI/CD pipeline setup, containerisation, and infrastructure as code for a single application — typically runs $8,000--$20,000. A full DevOps infrastructure build covering multiple services, environments, monitoring, security scanning, and team onboarding runs $20,000--$60,000. Ongoing DevOps support retainers for maintenance, incident response, and infrastructure evolution run $3,000--$8,000 per month. We scope every engagement after an assessment of your current deployment process and infrastructure state.
A CI/CD pipeline for a single application, covering automated tests, security scanning, and environment promotion, typically takes 2--4 weeks. A full DevOps infrastructure build across multiple services and environments takes 6--12 weeks. The work is structured around your team: we set up the pipelines, run sessions to transfer knowledge, and leave your engineers with systems they understand and can maintain.
Work with us
We scope DevOps as a Service in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.