AI ERP with agents inside the systems you run
AI phone and voice agents for real calls
LLM.coPrivate, self-hosted LLM deployments
Legal AI infrastructure for firms
AI RFP discovery and response drafting
Automatic.coBusiness process automation
CyberAttack.aiThreat detection and defense
Secure AI virtual data rooms
99.95%
Uptime target we design to
With alerting that proves it
30–40%
Typical cloud cost reduction
Found in the first audit
<15 min
Deploy to rollback
When a staged rollout catches a bad build
100%
Infrastructure as code
In your cloud account, in Terraform
Compute
Kubernetes vs managed containers, decided honestly
This is the single most over-adopted technology decision in infrastructure, and reversing it later is expensive. A cluster gets stood up because it's what a senior hire knows, or because a vendor's sales deck made it sound inevitable, and eighteen months later three services depend on primitives nobody can name a reason for. Here's how we actually decide.
Most teams should not run Kubernetes
It solves problems you have at a scale you probably haven't reached: multi-region failover, hundreds of services, a platform team whose job is operating the cluster. Below that scale it's mostly a tax paid in YAML and on-call pages for a control plane that has to be patched whether you use its capabilities or not. Say this plainly to a client who's already committed budget to Kubernetes because it will save them money, even when it's not what they hoped to hear.
Managed container services cover most real workloads
AWS ECS/Fargate, Google Cloud Run, and Azure Container Apps run containers without the orchestration layer you'd otherwise operate yourself. For a team running a few dozen services or fewer, this is very often the cheaper and more reliable choice, not just the simpler one, and it comes with fewer moving parts for an on-call engineer to reason about at 3am.
Kubernetes earns its cost at specific thresholds
Genuinely heterogeneous workloads, real multi-cloud portability rather than an insurance policy against it, or an internal platform team whose actual job is running it — that's when the flexibility is worth the operational overhead. Adopting it because it's what the job postings ask for is not a reason, and it's the one we hear most often when we ask why a cluster exists.
The migration path runs one way more easily than the other
Moving from managed containers to Kubernetes later, once you've proven you need it, is a bounded project with a clear scope. Migrating off an over-built cluster you never needed is not — by then three teams have written deployment YAML that assumes it exists, and the config-map dependencies nobody documented are the ones that turn a two-week migration into a two-month one.
Managed Kubernetes is the real middle ground
If you're already committed to Kubernetes, let the cloud provider run the control plane — EKS, GKE, or AKS. Self-hosting your own control plane is rarely justified outside specific regulatory or air-gapped requirements, and it's the part of Kubernetes operations most likely to page you overnight.
Serverless functions solve a narrower problem than either
Lambda, Cloud Functions, and their equivalents are the right shape for short, event-triggered work — a webhook handler, a nightly job, an image resize — where paying per invocation beats keeping a container warm. They are the wrong shape for a long-running API with steady traffic, where the cold-start and duration limits fight the workload instead of fitting it.
What it costs
DevOps consulting pricing
Real ranges for the engagements we're actually asked to run. The variable that moves a quote is not headcount or server count — it's how much of the environment is currently undocumented click-ops nobody can explain.
| Engagement | Commitment | Timeline | What's included |
|---|---|---|---|
| Infrastructure audit | Fixed scope | 1 – 3 weeks | Cost breakdown, security review, single points of failure, and a prioritized fix list. Yours to act on with or without us. |
| Terraform migration | Fixed scope | 4 – 10 weeks | Existing click-ops infrastructure rebuilt as versioned, reviewable Terraform, with remote state, a real plan/apply workflow, and a module structure your team can extend without our involvement. |
| CI/CD and staged rollouts | Fixed scope | 3 – 8 weeks | Automated pipelines, canary or staged deploys, and a rollback that works in minutes rather than an incident-review action item. |
| Observability build-out | Fixed scope | 4 – 8 weeks | Metrics, logs, and traces wired to alerts that reach a person, plus dashboards built around the questions you actually ask mid-incident, and a documented alerting philosophy so the thresholds don't drift back into noise. |
| Ongoing DevOps practice | Ongoing retainer | Ongoing | Standing ownership of infrastructure, deploys, and on-call, sized to your environment rather than sold as a fixed headcount. |
Ranges assume US-based senior engineers and include the security review and documentation a cheaper quote usually strips out. A number well under these bands is often a team that skipped the audit and started provisioning before anyone agreed what 'done' means. That gap tends to surface later as an unreviewable pile of manual AWS console changes nobody wants to touch, discovered during the next hire's first week when they ask where the infrastructure is actually defined.
Observability
Self-hosted or managed observability, and what actually decides it
The tool matters less than most teams think. The decision that matters is volume, retention, and whether anyone has actually defined what a good alert looks like. Teams that get this wrong tend to over-invest in the dashboard and under-invest in the on-call discipline that makes a dashboard worth having.
Managed is the right default
Datadog, Grafana Cloud, or Honeycomb get you real dashboards, alerting, and tracing in days rather than the months it takes to run Prometheus and Loki reliably yourself. The subscription is usually cheaper than the engineer-hours it replaces.
Self-hosted earns its cost at real volume
Once metric and log volume gets large enough that a managed vendor's bill starts to rival a headcount, or when data-residency rules forbid third-party retention of your logs, self-hosting the stack starts to pay for itself. Below that point, the engineering hours spent keeping Prometheus and Loki reliable are hours not spent on the product.
The stack matters less than the discipline
Prometheus, Grafana, and Loki, or their managed equivalents, all fail the same way: nobody defines what a good alert looks like, so the team drowns in noise and starts ignoring pages. Fix the alerting philosophy before picking the tool, because a well-designed alert on a mediocre stack still beats a noisy one on the best platform money can buy.
Traces are the piece most teams skip, then need most
Metrics tell you something is slow. Traces tell you which service and which downstream call. Adding tracing after the incident that needed it arrives a week later than you wanted it.
Cost caps belong in the contract, not the invoice
Log and metric volume grows with the business whether anyone budgeted for it or not. Set retention and sampling policy deliberately, or find out what unsampled logging costs at ten times today's traffic.
Synthetic monitoring catches what real traffic hasn't yet
A scheduled check that hits your login flow and checkout path every minute finds an outage before a customer does, which matters most for the paths that don't get exercised constantly by real users. It's cheap to set up and consistently the highest-leverage alert most teams don't have.
Incident response
On-call that works at 3am, not just in the runbook
Most incident-response failures aren't technical. They're a page nobody saw, a runbook nobody trusts, or a postmortem that punished the wrong thing. The fix for all three is process, decided calmly in advance, not tooling bought during the next fire.
Pages need to reach a person, not a channel
A Slack message nobody is required to read is not alerting — it's a record for the postmortem. PagerDuty or Opsgenie, with an escalation policy that actually escalates, is the baseline, and the escalation chain needs to be tested with a real page occasionally, not just documented.
Runbooks are written before the incident, not during it
The five minutes spent finding the right runbook while a service is down is five minutes a written, tested runbook would have saved. If nobody has read it since it was written, assume it's wrong, and schedule a game day to find out before an actual incident does.
Rollback has to be faster than root-causing
The first response to most incidents should be reverting to the last known-good state, not debugging in production. Staged rollouts exist specifically to make that revert cheap and fast, and a deploy pipeline that can't roll back in minutes is a design gap, not a rare edge case worth accepting.
Blameless postmortems are the only kind that improve anything
A postmortem that assigns fault teaches people to hide problems, not to prevent them. The output that matters is the list of system changes, not the paragraph about who missed what, and that list needs an owner and a date, not just a shared doc nobody revisits.
On-call load is a staffing signal, not a personality trait
A rotation that pages the same two engineers every week isn't a culture problem to fix with better attitudes. It's understaffing, or unfixed root causes, dressed up as a schedule.
The same incident recurring is a process failure, not bad luck
If a postmortem's action items keep getting deprioritized against the next feature, the same root cause will page someone again, usually within a quarter. Tracking whether postmortem action items actually ship is a smaller job than it sounds and one of the highest-leverage things a platform lead can own.
How DevOps gets sold wrong
DevOps is a practice engineers share, not a team you hire instead of adopting it
"DevOps team" is the most common anti-pattern in this field, and it's usually sold with good intentions. It just doesn't fix what it's meant to fix, and it tends to be the most expensive way to arrive back at the same bottleneck a year later with a bigger org chart.
A separate team often just relocates the problem
If deploys, infrastructure, and monitoring belong to a team apart from the developers writing the code, those developers optimize for throwing code over a wall and the DevOps team becomes a queue. The practice — owning what you ship, in production, with the tools to do it — has to live inside engineering, not beside it, or the org chart just moves the bottleneck without removing it.
What a DevOps engagement should actually leave behind
Infrastructure your own engineers can read and change, pipelines they trust enough not to work around, and alerting they don't mute. If we leave and the system only a consultant understood breaks, the engagement failed regardless of the uptime number during it — a good uptime record with no transferred understanding is a liability with a delay on it.
Cost optimization has one of the fastest payback periods in software
Rightsizing instances, fixing storage lifecycle policies, and catching orphaned resources — unattached volumes, idle load balancers, forgotten snapshots — routinely recovers 20 to 40 percent of a cloud bill in the first month, often paying for the audit outright. It's also one of the few engagements where the client can verify the result themselves, on next month's invoice, without taking our word for it.
Committed-use discounts are worth it only after the audit
Reserved instances or savings plans locked in against last year's over-provisioned footprint just make the waste contractual for another year. Right-size first, commit second.
The org-chart question comes before the tooling question
Whether you need a platform team, a rotation inside product engineering, or an outside partner depends on headcount and how many services you run — not on which tool is trending this year. Answer that before buying anything.
A platform team is a product team with internal customers
Once you're large enough to justify one, it succeeds or fails the same way any product does — by whether the engineers using it would choose it if they had another option. A platform team that ships mandates instead of a product other teams actually want to build on becomes exactly the bottleneck it was created to remove.
How an engagement runs
From audit to a system your team owns
Infrastructure work fails when it ships as a black box. The sequence below is built so ownership transfers before we leave, not after something breaks and nobody remembers why it was built that way. Each stage produces something your engineers can read and question, not a deliverable that only makes sense to the people who wrote it.
Related
Related services
DevOps work usually sits next to one of these — the platform you're deploying, the tests gating it, or the observability behind it.
Questions
Common questions about DevOps consulting
What teams ask before a first call.
An infrastructure audit is the smallest engagement here, and most clients start there rather than committing to a larger scope up front. A Terraform migration or a CI/CD and staged-rollout build is a larger piece of work.
What moves the number is how much of the current environment was built by hand in a console and has to be reverse-engineered before it can be described in code. The audit exists to answer exactly that before anyone quotes the rest.
Probably not yet. Most teams are well served by a managed container service — ECS/Fargate, Cloud Run, Container Apps — until they hit a specific threshold: real multi-cloud portability, dozens of genuinely heterogeneous services, or a platform team whose job is running the cluster. Reaching that threshold is usually obvious in hindsight and rarely obvious in advance, which is why we push back on adopting it speculatively.
If you're already on Kubernetes and it's working, managed Kubernetes (EKS, GKE, AKS) is almost always the right way to run it — let the cloud provider operate the control plane rather than doing it yourself. If it's not working, the fix is more often a smaller cluster and better defaults than a migration off it entirely.
Infrastructure defined in Terraform under your own cloud account, CI/CD pipelines with staged or canary rollouts, observability wired to alerts that reach a person, and an incident-response process with runbooks somebody has actually read. Documentation and a knowledge-transfer session are part of the scope, not an add-on billed separately at the end.
What it should not include is a black box only the consultant understands. The engagement is done when your own engineers can read the infrastructure, trust the pipeline, and aren't muting the alerts — not when the invoice is paid.
A separate team that owns deploys and infrastructure while developers ship code over the wall usually relocates the problem instead of solving it. DevOps works best as a practice — engineers owning what they ship into production — supported by tooling and, where needed, a platform function once the org is large enough to need one.
Where you're too small for a dedicated platform hire, an outside partner building the practice and handing it off is often a better first step than hiring a team around a function nobody in-house yet knows how to direct. The handoff, not the build, is the part worth being deliberate about.
Managed — Datadog, Grafana Cloud, Honeycomb — for the large majority of teams. It gets you real alerting and tracing in days, and the subscription almost always costs less than the engineering time it takes to run Prometheus and Loki reliably yourselves, especially once someone has to be on-call for the observability stack itself.
Self-hosting earns its cost at real log and metric volume, or when data-residency rules forbid a third party from retaining your logs. Below that threshold, self-hosting is usually a hobby project wearing an infrastructure-savings argument, and the hours it consumes rarely show up on the same spreadsheet as the subscription it was meant to avoid.
Fast. Rightsizing instances, fixing storage lifecycle rules, and eliminating orphaned resources — unattached volumes, idle load balancers, forgotten snapshots — routinely cuts 20 to 40 percent off a cloud bill within the first month, which usually covers the cost of the audit itself before the second invoice arrives.
Committed-use discounts should come after that cleanup, not before it. Locking in a reserved-instance plan against an over-provisioned footprint just makes the waste contractual for another year, and undoing a bad multi-year commitment is far more painful than the rightsizing work it should have followed.