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?
Deployments that take a full day, break things, and require someone to babysit them are an engineering tax you pay every sprint.
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
RaftLabs provides DevOps as a Service including CI/CD pipeline setup using GitHub Actions, GitLab CI, or CircleCI, Docker containerisation and Kubernetes orchestration, infrastructure as code using Terraform, cloud cost optimisation, monitoring and observability using Datadog, Grafana, or similar, security scanning in pipelines, and on-call runbook development. DevOps engagements are scoped at a fixed price after an assessment of your current deployment process, infrastructure, and engineering team workflows.
The deployment problem is a systems problem
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.
What we build
CI/CD pipeline setup
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. From merge to production in under 15 minutes without human intervention for routine deployments.
Docker containerisation
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 orchestration
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.
Infrastructure as code
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. Environment variable management through Terraform workspaces or separate state files. Infrastructure that is reproducible, reviewable, and diffable like application code.
Monitoring and observability
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 in the pipeline
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.
How much engineering time goes into deployments that should run without human involvement?
Tell us how your team currently deploys and where the friction is. We will scope the DevOps infrastructure that removes it.
Related services
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
Frequently asked questions
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.