DevOps consulting that turns day-long deployments into an eight-minute pipeline.
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, containerization, 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
Containerized 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
DevOps · SaaS platform
4 hrs → 8 min
Replaced a 4-hour manual deployment with a full CI/CD pipeline across 3 environments. Team ships daily with zero manual steps.
Infrastructure as code · FinTech startup
3 environments, 0 drift
Built Terraform-managed AWS infrastructure for a payments platform. Environments fully reproducible from code in under 20 minutes.
Monitoring · Healthcare platform
12 weeks to production
Deployed full observability stack with alerting. HIPAA-compliant logging. Team detects incidents before users report them.
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?
Worried a DevOps consultancy will sell you Kubernetes you don't actually need?
Short answer
DevOps consulting is hiring an outside team to build and hand over the automation, infrastructure, and monitoring that make software deployments fast and reliable, rather than manual and risky. RaftLabs builds DevOps infrastructure for engineering teams across the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia. CI/CD pipelines, Kubernetes, Terraform, and full observability stack. Most teams move from manual deployments to automated pipelines in 6-12 weeks at a fixed price starting at $8,000.
Key takeaways
Most engineering teams move from manual deployments to fully automated pipelines in 6-12 weeks at a fixed price.
A CI/CD pipeline setup for a single application typically takes 2-4 weeks from engagement start.
A focused DevOps engagement covering CI/CD, containerization, and infrastructure as code starts at $8,000.
Elite DevOps performers deploy on demand and recover from a failed deployment in under an hour; low performers can take a week to a month (DORA, 2024 State of DevOps Report).
One client replaced a 4-hour manual deployment with a full CI/CD pipeline, reducing deploy time to 8 minutes with zero manual steps.
All infrastructure is delivered as Terraform code so environments are reproducible from code in under 20 minutes.
Trusted by
The deploy that needed a person to sit and watch it.
Picture the version you actually want. Deploy on demand, without dread, the same pipeline running the same way whether it's Tuesday morning or Friday at 5pm. If something's wrong, the pipeline stops and tells you exactly why, before it reaches production, not after.
Now the version most teams are living with instead. The deployment takes half a day, runs through steps that live in one engineer's head, and the rollback is scarier than the bug it fixes. Staging stopped matching production months ago, so a chunk of every QA cycle goes to chasing environment-specific ghosts.
None of this is the engineers' fault. The process needs a human at every step, and every human step is a step that can fail unpredictably.
What is DevOps consulting
DevOps consulting is hiring an outside team to build the automation, infrastructure, and monitoring that make software deployments fast, reliable, and repeatable, instead of manual and risky. It typically covers CI/CD pipeline setup, containerization, infrastructure as code, and monitoring and alerting, delivered as a scoped engagement, not a headcount hire, with the goal of leaving your own engineers able to run and extend the system after handover.
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 that process systematic, automated, and reliable. The engineering team ships features. The pipeline handles the rest.
Why elite and low-performing teams keep pulling further apart
The odds today
127x
faster change lead time for elite DevOps performers vs. low performers
DORA (Google Cloud), 2024 State of DevOps Report
<1hr
to recover from a failed deployment for elite performers; low performers can take a week to a month
DORA (Google Cloud), 2024 State of DevOps Report
22%
of teams now in the elite-performer cluster, down from 31% the year before, the gap is widening
DORA (Google Cloud), 2024 State of DevOps Report
The cost of the gap is rarely abstract. On July 19, 2024, CrowdStrike pushed a routine content update to its Falcon sensor. Sensor binary updates went through staged rollout and testing at CrowdStrike; this class of content update didn't, and it shipped to every subscribed customer simultaneously, with no canary phase. A bug in the update triggered a crash on every Windows machine that received it: roughly 8.5 million devices, airlines grounded, hospitals disrupted, banks offline. Cyber-risk analytics firm Parametrix estimated $5.4 billion in direct losses to Fortune 500 companies alone. The technology wasn't new or exotic. The discipline that should have staged the rollout was the thing missing.
What you've probably already tried, and why it stalled
Made one engineer the unofficial DevOps person
A senior engineer absorbs deployment and infrastructure as a side duty on top of their actual job. Works until they're on vacation, leave, or simply can't keep two jobs going, and the tribal knowledge they were carrying has no backup.
Adopted Kubernetes because that's what real engineering teams use
Cargo-cult infrastructure, driven by what a senior hire's previous, much larger company used, not by your actual scale or traffic pattern. The operational burden lands on people who never asked for the complexity.
Hired a DevOps consultancy on retainer
Money goes out every month. Visibility into what's actually improving does not come back. No breakdown of hours or deliverables, a black-box invoice, sometimes locked into a 12-24 month contract regardless of results.
Kept deploying manually because it works fine, until it doesn't
The status quo persists because nothing has gone catastrophically wrong yet. "It's worked so far" and "it's safe" are not the same claim, and the gap between them is exactly where a bad Friday deploy lives.
How we close the gap
Your app runs fine. It just can't deploy without a person babysitting it, can't scale when traffic spikes, and can't tell you when something breaks. That's an infrastructure gap, not a rewrite. We audit your current deployment process and infrastructure before recommending anything, then build the pipelines, containers, and infrastructure as code your team inherits and can actually run. We do not default to Kubernetes for every project: if a load balancer and two instances solve your traffic pattern, that's what we recommend, and we'll tell you so even though the more complex build costs more. Fixed price, no multi-year lock-in, knowledge-transfer sessions before we're done.
DevOps pays off when a real product is already fighting its own deployment process.
Everything on the left should already be true for your team. Even one thing on the right, and a lighter setup is the smarter first step right now.
A fit
01
An application already in production, deployed today through manual steps that eat engineering time every sprint.
02
A staging environment that has drifted from production, or infrastructure configuration that lives in one person's head.
03
Budget for an engagement from $8,000, and a team ready to inherit and operate the pipelines after handover.
Not a fit
No application in production yet, with requirements still forming.
A single static site that deploys fine as it is.
Shopping for the cheapest hourly contractor, not a fixed-scope engagement.
What we build
What we build
01
CI/CD pipeline setup
Automated build, test, and deployment where every merge runs tests, linting, security scanning, and deployment, with approval gates for production. Blue-green and canary strategies give zero-downtime releases with instant rollback, and DORA metrics are instrumented from day one so your team can measure improvement. Built in GitHub Actions, GitLab CI, or CircleCI.
02
Docker containerization
Application containerization with multi-stage Docker builds that produce minimal, secure production images and consistent environments from developer laptop to production, no more "works on my machine" incidents. Image scanning catches known vulnerabilities before deployment, and the foundation makes your application portable across cloud providers.
03
Kubernetes orchestration
Kubernetes cluster setup on AWS EKS, Azure AKS, or Google GKE, with deployment configurations, health checks, and rolling update strategies, plus Horizontal Pod Autoscaler for traffic-based scaling and a service mesh (Istio or Linkerd) for service-to-service communication and observability, when your traffic pattern actually calls for it. We hand over operations runbooks for the common tasks your team will perform independently after delivery.
Security scanning integrated into your CI/CD pipeline as a blocking gate, not a reporting-only tool: dependency vulnerability scanning with Snyk, Dependabot, or Trivy, static application security testing, container image scanning, secret scanning, and infrastructure configuration checks with Checkov or Terrascan. Each finding is categorized by severity with blocking thresholds your team defines, making security a development discipline rather than a post-deployment audit.
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'll scope the DevOps infrastructure that removes it.
How it works
From scope to shipped
Every DevOps engagement follows the same four phases. Scope is locked and price is fixed before any work starts.
Week 1
01
Audit and scope
We review your current deployment process, infrastructure state, and team workflows. You leave week 1 with a written scope document identifying the gaps and a fixed-price quote. No work starts without your sign-off.
Weeks 2-3
02
Design and architecture
Pipeline architecture, environment topology, and infrastructure design decided before a single resource is created. Decisions made here cost ten times less than the same decisions made in week 8.
Weeks 4-10
03
Build, integrate, and test
CI/CD pipelines, containerization, infrastructure as code, and monitoring built in parallel. Working pipelines on a staging environment by the end of sprint one. Bi-weekly demos. Security scanning integrated from the first sprint.
Weeks 10+
04
Handover and post-launch support
Production deployment with monitoring activated on go-live day. Knowledge transfer sessions so your team inherits systems they understand and can operate. 8 weeks of post-launch support included in every engagement.
Proof it works
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin, across AI, SaaS, mobile, automation, and enterprise platforms.
Infrastructure we've run
uptime since launch on a loyalty platform we rebuilt onto managed cloud infrastructure
99.9%
Energia Rewards, Ireland
requests sustained in load testing with zero backend failures and CPU under 5%
10,000/s
Energia Rewards, pre-campaign load test
user records migrated to new cloud infrastructure with zero downtime and no forced password resets
300K+
Energia Rewards zero-downtime migration
One SaaS client replaced a 4-hour manual deployment with a full CI/CD pipeline across three environments and now ships daily with zero manual steps, from 4 hours to 8 minutes. A FinTech payments platform got Terraform-managed AWS infrastructure reproducible from code in under 20 minutes, across 3 environments with zero drift. A healthcare platform got a full observability stack with HIPAA-compliant logging in 12 weeks, catching incidents before users report them.
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Charles E.
USA
Entrepreneur at Aggie Technologies
“
All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
01 / 02
Fair questions, straight answers
"You'll push Kubernetes and microservices on us even though we don't need that much complexity."
We assess your traffic pattern and team capacity before recommending anything. If a load balancer and two instances solve it, that's what we build, even though the more complex setup costs more.
"I'll pay a retainer and have no idea what's actually improving month to month."
Fixed price, defined scope, agreed before work starts. Not an hourly black box.
"I'll get locked into a long contract regardless of results."
Engagement-based, not a multi-year retainer. Everything ships as Terraform code with runbooks, so your team can walk away and still run what we built.
"You'll set up tools my team can't maintain once you leave."
Knowledge-transfer sessions and runbooks are part of every engagement, not an optional add-on. Your engineers inherit systems they understand.
"Is this even for a company our size, or are we too small for 'real' DevOps?"
What matters is whether you have a real application in production losing engineering time to manual deployments, not headcount. A 5-person team benefits from the same discipline a 50-person team does.
"This will take months and disrupt what's already working."
A focused engagement is 2-4 weeks for a single application. Your current deployment process keeps working throughout, we don't touch production until the new pipeline is proven.
What DevOps engagements cost
We price by engagement, not by the hour. After an assessment of your current deployment process and infrastructure state, you get a fixed quote with a defined scope, timeline, and price, so you know the number before any work starts. Where you land depends on scope, not negotiation:
Focused DevOps engagement, starts at $8,000
CI/CD pipeline setup, containerization, and infrastructure as code for a single application, up to $20,000.
Full DevOps infrastructure build, $20,000-$60,000
Multiple services and environments, monitoring, security scanning, and team onboarding.
Ongoing DevOps support retainer, $12,000-$15,000 per month
Maintenance, incident response, and infrastructure evolution after handover.
What pushes cost up: multi-service Kubernetes architectures, strict compliance requirements such as HIPAA, SOC 2, and GDPR, and deep integration with legacy or on-premise systems. What keeps it down: a single application scope, managed cloud services rather than self-hosted infrastructure, and a team ready to absorb knowledge transfer. We scope every engagement after an assessment of your current setup.
What it costs
DevOps engagements, starting at $8,000.
An assessment of your current deployment process and infrastructure, then a defined scope, timeline, and price, agreed before we touch a resource.
Starts at $8,000
Priced by engagement, not by the hour. Ongoing support retainers start at $12,000 per month. We scope everything after a review of your current setup, then start small and expand as the pipeline matures.
Start with the assessment and one pipeline. Once it's running clean, we scope the next environment or service as a follow-on engagement.
No hourly billing
Once we scope the engagement, that price is locked in writing. No hourly billing, no surprise invoices, no scope change slipped into the final bill.
Yours to operate
Everything ships as Terraform code with runbooks and knowledge-transfer sessions, so your engineers inherit systems they can run, modify, and rebuild. No proprietary tooling, no lock-in.
What you actually get
The unglamorous decisions that decide whether a pipeline holds up at 2am, not just in the demo.
01
A rollback plan that's actually been tested
Not just "we have a rollback." Tested against a real failure before it's the thing standing between you and an outage.
02
Drift detection that actually runs on a schedule
Console changes get caught within 24 hours, not discovered six months later when staging and production quietly diverge again.
03
Alert thresholds calibrated to your real baseline
Not generic defaults that page someone for normal traffic, or stay silent through a real incident because the threshold was never tuned.
04
Runbooks a new on-call engineer can follow without calling us
Written for the person who joins after we're gone, not for someone who already knows the system.
05
The honest answer on Kubernetes, even when it costs us the bigger invoice
If your traffic pattern doesn't need it, we say so. The simpler, cheaper build is the right build more often than the industry admits.
06
Infrastructure and accounts in your name from day one
Terraform code, cloud accounts, CI/CD config, all in your name from the start. Nothing to hand off, because nothing was ever ours to hold.
The stack we build DevOps on
We are not tied to one vendor or tool. We pick the CI/CD system, orchestration layer, and cloud that fit your team's existing skills and your scale, then document every choice so your engineers inherit systems they can operate and extend. The technologies we reach for most often:
Layer
Technologies we use
Where it fits
CI/CD
GitHub Actions, GitLab CI, Jenkins, ArgoCD
Automated build, test, deploy, and GitOps-driven promotion
Containers and orchestration
Docker, Kubernetes, Helm
Portable, environment-agnostic services and rolling deployments
Metrics, logs, traces, alerting, and distributed tracing
Cloud
AWS, GCP, Azure
Compute, networking, managed databases, and IAM
Security in the pipeline
Snyk, Trivy, Checkov, tfsec
Dependency, container, and IaC scanning as a blocking gate
The rule holds at every layer: no proprietary tooling that locks you in, and no system we cannot hand to your team on go-live day.
The pipeline we build runs your automated test suite on every merge, and it pairs naturally with legacy modernization when the deployment pain and the aging codebase underneath it turn out to be the same problem.
DevOps services we offer
01
CI/CD pipeline engineering
Automated build, test, and deployment pipelines in GitHub Actions, GitLab CI, or Jenkins, with approval gates, parallel test execution, and DORA metrics instrumented from day one.
DevOps consulting is hiring an outside team to build the automation, infrastructure, and monitoring that make software deployments fast, reliable, and repeatable, instead of manual and risky. It typically covers CI/CD pipeline setup, containerization, infrastructure as code, and monitoring and alerting, delivered as a scoped engagement rather than a headcount hire. The goal is a system your own engineers can run and extend after handover, not an ongoing dependency on the consultant.
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 utilization, 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, containerization, and infrastructure as code for a single application, typically starts at $8,000 and runs to $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 $12,000-$15,000 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.
No. The engagement scales to a single application with a small team just as well as multiple services with a large one. What matters is whether you have a real application already in production and manual deployment steps costing engineering time every sprint, not headcount. A 5-person team shipping daily benefits from the same automation discipline a 50-person team does, usually at the lower end of the pricing bands.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope DevOps Consulting Services | RaftLabs in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
Scope and cost agreed before work starts. No surprises. No obligation.
Working prototype within 3 weeks of kickoff.
Pay by milestone. You see progress before each invoice.
60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
