Voter IQ political engagement app
- 347ms
- p95 latency at 1,000 concurrent users in load testing
Dedicated DevOps & Cloud Team
Manual deployments, inconsistent environments, and no observability are engineering taxes your team pays every sprint. We embed senior DevOps engineers, AWS, GCP, Docker, Kubernetes, Terraform, GitHub Actions, directly into your team. CI/CD pipelines that test, build, and deploy automatically. Infrastructure as code so environments are reproducible. Monitoring that tells you about production problems before your customers do.
CI/CD pipelines on GitHub Actions, GitLab CI, or CircleCI, automatic on every merge to main
Docker containerisation and Kubernetes orchestration with identical dev, staging, and production environments
Infrastructure as code using Terraform, reproducible, version-controlled, auditable
Monitoring and alerting with Datadog, Grafana, or CloudWatch configured from day one
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
The problem
How many production incidents last quarter were caused by environment configuration differences?
How long does your current deployment process take, and how often does it require manual intervention?
Short answer
RaftLabs embeds dedicated DevOps and cloud engineers skilled in AWS, GCP, Docker, Kubernetes, Terraform, and GitHub Actions. They build CI/CD pipelines, containerise applications, define infrastructure as code, and set up monitoring against the four DORA keys: deployment frequency, lead time, change failure rate, and time to restore service. Engagements start within one week at a fixed monthly rate.
Key takeaways
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Infrastructure problems are disproportionately expensive because they compound: a manual deployment process means deployments are infrequent and risky, infrequent deployments mean large diffs, large diffs mean harder rollbacks, and harder rollbacks mean longer outages. A DevOps engineer who fixes the pipeline isn't just saving deployment time, they're changing the risk profile of every future release.
The DORA research program (Google Cloud) measures delivery performance across four keys, and elite teams beat low performers on every one. According to Google's 2024 DORA State of DevOps Report, elite teams deploy on demand multiple times per day with lead times under one hour, while low performers deploy less than once per month. The gap is not talent, it is process: CI/CD automation, infrastructure as code, and monitoring are the structural differences separating a 15-minute pipeline from a half-day manual deployment.
We instrument and work to move all four DORA keys, not just deployment speed. Shipping fast means little if every third release breaks production.
The engineers we embed are senior enough to know the infrastructure decisions that are cheap to make upfront and expensive to retrofit: stateless application servers, secret rotation processes, environment parity, and the monitoring that tells you what broke before your customers start the support ticket.
What we deliver
CI/CD pipelines that test, build, and deploy automatically on every push, with layer caching cutting a 3-minute install to 15 seconds and tagged releases hitting production behind a manual approval gate. Security scanning is wired into the pipeline and runs in parallel, so checks add no sequential time.
Infrastructure defined in code and committed to version control, so every VPC, database, and service is reproducible from a single apply and every change is a reviewed pull request with a plan showing exactly what will change. Misconfigurations are caught before production, each PR is annotated with its monthly cost impact, and drift detection flags resources changed outside the pipeline.
Docker containerisation with multi-stage builds that shrink a 1.2GB development image to a 180MB production image with a smaller attack surface. Kubernetes deployment with charts parameterised per environment, autoscaling matched to the workload, and resource limits that come from profiling, not guesses.
Observability at three levels: infrastructure metrics, application metrics, and business metrics like payment success rate that surface failures infrastructure monitoring cannot see. Distributed tracing turns a 2-hour latency hunt into a 2-minute diagnosis, alerts fire on sustained conditions rather than single events, and runbooks document the most likely failure scenarios before an incident.
| Dedicated DevOps team (RaftLabs) | Staff augmentation | Managed service | |
|---|---|---|---|
| Who owns delivery | Engineers embed and own the outcome with your team | You direct each contractor task by task | Vendor runs it behind an SLA, off your board |
| Context retention | Learns your stack and stays across the engagement | Ramp-up cost repeats with every new contractor | Limited to what the runbook already covers |
| Where the knowledge lives | In your team, documented in Terraform and runbooks | Leaves when the contract ends | Held by the vendor, not you |
| Best fit | Building CI/CD, IaC, and observability you keep | Filling a short-term seat | Hands-off ops once the platform is stable |
| Pricing | Fixed monthly rate per engineer | Hourly or daily | Monthly SLA fee |
Most infrastructure damage is self-inflicted. These are the failure modes we design against from day one, not after they page someone at 2am.
Tell us what your current deployment process looks like, where infrastructure is causing pain, and what cloud environment you're running on. We'll match you with the right engineers and get them started within a week.
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.

All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
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Read moreKubernetes solves specific problems: running multiple service instances, automatic failover, rolling deployments without downtime, and auto-scaling based on load. If your application is a single service running on one or two servers with stable traffic, Kubernetes adds operational complexity without meaningful benefit. AWS ECS, Google Cloud Run, or Railway is simpler and cheaper. If you have microservices, variable traffic, or need multi-region resilience, Kubernetes is the right foundation. We assess your architecture, traffic patterns, and team before recommending.
Infrastructure as code means your cloud environments (VPCs, subnets, security groups, databases, load balancers, compute instances) are defined in Terraform files committed to version control. The practical outcomes: you can recreate any environment in minutes, not days. Every infrastructure change is a reviewed pull request with a plan output showing exactly what will change. New environments (staging, a new region, a client-specific deployment) are spun up from the same config. No more 'I think I set that up six months ago and I'm not sure what it is.' We use Terraform with remote state in S3 or GCS and workspace separation between environments.
We set up monitoring at three levels: infrastructure metrics (CPU, memory, disk, network), application metrics (request rate, error rate, latency, the RED method), and business metrics (orders processed, payments succeeded, jobs completed). For alerting: PagerDuty or OpsGenie integration with sensible thresholds, not an alert for every 5xx, but an alert when error rate crosses a threshold for a sustained period. We document runbooks for the five most likely failure scenarios so your team knows how to respond before an incident happens.
A senior DevOps engineer typically runs $4,000 to $7,000 per month depending on the cloud platform, tooling depth (Terraform, Kubernetes, security automation), and engagement scope. Project-based engagements (pipeline setup, Kubernetes migration, IaC build) are scoped at a fixed cost. Ongoing embedded support retainers are priced by the week. We quote after a call to assess your current infrastructure and what the engagement needs to deliver.
Yes. Most engagements start with an audit of what exists, what's defined in code, what was created manually, what's undocumented. We then decide together what to migrate to IaC, what to leave as-is, and what to replace. We don't insist on a full rebuild. If your EC2 setup is working and the only issue is a missing CI/CD pipeline, we fix the CI/CD pipeline.
We build compliance-aligned infrastructure from the start rather than retroactively mapping controls to an existing setup. For SOC 2: encryption at rest and in transit, IAM least-privilege policies, audit logging (CloudTrail, AWS Config), automated evidence collection for change management controls, and network segmentation. For HIPAA: PHI encryption at rest (AES-256), encrypted backups with access controls, audit trails for all PHI access, and a BAA in place with cloud providers. We document the controls we put in place so your compliance team has the evidence they need for audit.
Work with us
We scope Dedicated DevOps & Cloud Team in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.