Technologies
Tools we work with
Opinionated picks, not a logo wall. Every tool here earns its place by multiplying your team's output.
Infrastructure as Code
Every resource reviewable, every change auditable, every environment reproducible — IaC is the foundation that makes every other multiplier possible.
Terraform
The lingua franca of infrastructure definitions, and for good reason — the provider ecosystem is unmatched. We use it as the default when a team needs to manage multi-cloud or third-party resources because nothing else has the same breadth of coverage and community knowledge behind it.
Pairs with: Terragrunt, Infracost
OpenTofu
Same HCL, same workflow, no licensing uncertainty. For teams starting fresh or wary of vendor lock-in after the BSL change, OpenTofu is the drop-in choice — and the migration path from Terraform is trivial.
Pairs with: Terragrunt, Infracost
Terragrunt
Terraform gets messy the moment you have more than three environments. Terragrunt keeps your configurations DRY, wires up remote state automatically, and enforces a sane directory structure — it turns a sprawl of copy-pasted .tf files into something a team can actually maintain.
Pairs with: Terraform, OpenTofu
Ansible
When you need to configure what’s inside the machine — not just provision it — Ansible is the pragmatic choice. Agentless, SSH-based, readable YAML playbooks. We reach for it when cloud-init isn’t enough and a full control plane would be overkill.
Pairs with: Terraform, OpenTofu
Infracost
Shift cost visibility left into the pull request, before anything gets provisioned. Engineers see the dollar impact of their infrastructure changes at review time — no surprises on the monthly bill, and cost becomes a design constraint rather than an afterthought.
Pairs with: Terraform, FinOps
FinOps
Cloud spend is an engineering problem, not a finance problem. We build cost awareness into the development workflow — tagging, budgets, anomaly alerts, right-sizing recommendations — so your team makes cost-informed architecture decisions before the bill arrives, not after.
Pairs with: Infracost, Terraform
CI/CD & Delivery
A fast, reliable pipeline multiplies every engineer on your team — no deploy rituals, no war rooms, just push and move on.
GitHub Actions
The default for most teams, and for good reason — it lives where your code already lives. We wire up workflows that run tests, build containers, and deploy on merge with zero context-switching. When the pipeline is two clicks from the diff, engineers actually read the logs.
Pairs with: ArgoCD, GitOps
GitLab CI/CD
When your team runs GitLab, keeping CI/CD in the same platform eliminates context-switching entirely — merge requests, pipelines, container registry, and environments all in one place. The built-in Auto DevOps and review apps make it especially strong for teams that want convention over configuration.
Pairs with: GitOps, ArgoCD
Cloud Build
If you’re on GCP, Cloud Build keeps your CI inside the same trust boundary as your runtime — no extra secrets management, no cross-cloud auth chains. We use it when the deploy target is Cloud Run, GKE, or Artifact Registry and the simplicity of staying in-network outweighs GitHub Actions’ ecosystem.
Pairs with: GitOps, ArgoCD
ArgoCD
ArgoCD turns your Git repo into the single source of truth for what’s running in Kubernetes. It watches, diffs, and syncs — so deploys are a commit, rollbacks are a revert, and drift gets corrected before anyone pages you at 2 a.m.
Pairs with: GitOps, GitHub Actions
Nx
Monorepos only work if your tooling keeps up. Nx gives you incremental builds, affected-only test runs, and dependency-aware task orchestration — so a repository with 20 projects doesn’t mean 20x the CI time. The multiplier effect is real: one repo, one PR, one deploy pipeline, and engineers stop waiting on builds.
Pairs with: GitHub Actions, GitLab CI/CD
GitOps
GitOps is the pattern underneath everything else here: the desired state of your system lives in version control, and automation reconciles reality to match. No more SSH-and-pray deploys, no tribal knowledge about what’s running where. Every change is auditable, reversible, and reviewable in a pull request.
Pairs with: ArgoCD, GitHub Actions
Observability & Monitoring
You can’t improve what you can’t see — observability turns 4-hour debugging sessions into 20-minute fixes, and that time compounds across every engineer on your team.
OpenTelemetry
The instrumentation layer we standardize on. Vendor-neutral traces, metrics, and logs mean you own your telemetry data and can swap backends without rewriting application code. It’s the one dependency in the observability stack that actually reduces lock-in.
Pairs with: Cloud Trace, Grafana
Cloud Trace
If you’re on GCP, this is the first tracing backend we reach for. Zero additional infrastructure, native integration with Cloud Run, GKE, and Cloud Functions — you get distributed traces without running collectors or managing another vendor relationship.
Pairs with: OpenTelemetry, Cloud Logging
Cloud Logging
GCP’s native logging works out of the box for anything running on the platform — structured logs, log-based metrics, and alerts without bolting on a third-party agent. We use it as the default and only look elsewhere when query complexity or retention requirements outgrow it.
Pairs with: Cloud Trace, Cloud Monitoring
Cloud Monitoring
Custom dashboards, SLO tracking, and uptime checks — all wired into every managed service already. One less tool to provision, one less bill to manage, one less context switch for your on-call engineer.
Pairs with: Cloud Logging, Cloud Trace
Grafana
When you need dashboards that pull from multiple data sources or you’re running multi-cloud, Grafana earns its place. We use it to unify metrics across providers and self-hosted systems — but if you’re single-cloud GCP, the native dashboards usually get you there with less overhead.
Pairs with: OpenTelemetry, Datadog
Datadog
The right call when your observability needs outgrow native tooling — complex APM, cross-service dependency maps, and anomaly detection across hundreds of services. Powerful, but it’s a real line item. We scope it tightly and make sure you’re using what you’re paying for.
Pairs with: OpenTelemetry, Sentry
PagerDuty
Alerting is only useful if it reaches the right person at the right time. PagerDuty handles escalation, on-call schedules, and incident routing so your team can trust that critical alerts won’t get buried in a Slack channel at 3 a.m.
Pairs with: Cloud Monitoring, Datadog
Sentry
Error tracking that actually gets used. Sentry catches the exceptions your logs bury — with stack traces, breadcrumbs, and release correlation that make reproducing bugs straightforward instead of archaeological.
Pairs with: Datadog, Cloud Logging
Security & Zero Trust
Security that adds friction is security that gets bypassed — we wire it into the platform so it’s invisible, automatic, and on by default.
IAM-as-Code
IAM policies defined in Terraform, reviewed in PRs, applied through CI. No one clicks through a console to grant permissions — every role and binding is versioned, auditable, and reproducible across environments.
Pairs with: Workload Identity
Workload Identity
Services authenticate with cloud APIs using their own identity — no service account keys, no shared secrets, no rotation schedules to forget about. The credential that can’t leak is the one that never existed.
Pairs with: IAM-as-Code, Cloud IAP
Cloud IAP
Put internal tools behind identity-aware access instead of a VPN. Users authenticate once, policies enforce who sees what, and you stop pretending network perimeter equals trust.
Pairs with: Workload Identity, IAM-as-Code
Cloud Armor (WAF)
Rate limiting, geo-blocking, and OWASP rule sets applied at the edge before traffic hits your services. We codify the policies in Terraform so your WAF config is reviewed and versioned like everything else.
Pairs with: Cloud IAP, IAM-as-Code
DMARC / SPF / DKIM
Email authentication that stops domain spoofing and phishing before it reaches your customers. Most teams set it up halfway and never enforce — we take it to p=reject and monitor the reports so legitimate mail keeps flowing.
Pairs with: IAM-as-Code
Architecture & Edge
The right architecture decisions early are multipliers that compound forever — most teams never make them because they haven’t felt the pain yet.
Kubernetes
The standard for container orchestration when your workloads outgrow serverless. We run GKE and EKS, but we’re honest about when you need it — most startups are better served by Cloud Run until they hit the complexity threshold where K8s earns its operational overhead.
Pairs with: ArgoCD, Horizontal scaling
Event-driven architecture
Pub/Sub, Cloud Tasks, async workers — decouple your services so a spike in one doesn’t cascade through the rest. Most teams bolt this on after their first outage. We wire it in from the start because retrofitting event-driven patterns into a synchronous codebase is a months-long project nobody budgets for.
Pairs with: Horizontal scaling, Database scaling patterns
Horizontal scaling
Vertical scaling buys you time. Horizontal scaling buys you a future. We design services to be stateless and horizontally scalable from day one — autoscaling groups, container orchestration, load-balanced workers — so your architecture doesn’t hit a ceiling the moment traffic doubles.
Pairs with: Event-driven architecture, Database scaling patterns
CDN / Edge networks
The fastest request is the one that never reaches your origin. We push static assets, API responses, and rendered pages to the edge with Cloud CDN or Cloudflare — cutting latency for every user, everywhere, while taking real load off your infrastructure.
Pairs with: Compression, Horizontal scaling
Compression
Brotli on static assets, gzip on API responses, protocol-level negotiation handled correctly — small configs that shave 60–80% off payload sizes. We set it up once, across the stack, and every response your system sends benefits forever. Pure compound interest.
Pairs with: CDN / Edge networks, Horizontal scaling
Caching & page speed
The cheapest request is the one you never process. We layer caching at every level — browser cache headers, CDN edge caching, application-level caching (Redis, Memorystore), and static pre-rendering. Combined with image optimization, lazy loading, and code splitting, the result is pages that load fast under any traffic and backends that breathe easy during spikes.
Pairs with: CDN / Edge networks, Compression
Database scaling patterns
Read replicas, connection pooling, eventual consistency — the patterns that keep your database from becoming the bottleneck that chokes everything else. Most teams don’t implement these until queries start timing out in production.
Pairs with: Event-driven architecture, Horizontal scaling