Inventiple designs and builds cloud-native infrastructure for AI products and SaaS platforms that need to auto-scale, self-heal, and ship continuously. Every line of infrastructure code is written by a senior engineer averaging 10+ years of production experience — in your AWS account, in your GitHub repo, from day one.
Your product is gaining traction. The MVP that handled 100 users is struggling at 10,000. Deployments take 45 minutes and break things. Nobody knows why the AWS bill doubled last month. Your engineers spend more time fighting infrastructure than building features. Sound familiar?
Most startups defer infrastructure until it becomes a crisis. The founding team ships on a single EC2 instance or a Vercel hobby plan, then wakes up one morning to a production outage, a $15,000 surprise cloud bill, or a security audit they can't pass. By then, fixing it costs 3x what doing it right the first time would have.
DevOps consultancies sell complexity, not outcomes. They recommend Kubernetes when ECS would work, service meshes when you have two services, and 47 Terraform modules when you need five. You end up with infrastructure your team can't operate, maintained by consultants on a retainer you can't escape.
Lift-and-shift migrations waste money without fixing architecture. Moving a monolith to EC2 instances in the cloud doesn't make it cloud-native. You get the same deployment pain, the same scaling limits, and a higher bill — plus the cost of the migration itself. Real cloud-native means containerized services, automated pipelines, and observability baked in from the start.
Observability is treated as an afterthought. Teams deploy to production with printf debugging and hope. When something breaks at 3 AM, there's no tracing, no structured logs, no alerting — just a pager and a prayer. For AI workloads, this is worse: without LLM cost tracking and latency monitoring, a runaway agent loop can burn through your API budget overnight.
Multi-region and compliance get deferred until an enterprise deal forces the issue.Your first big customer asks for SOC 2, data residency in the EU, or a 99.9% uptime SLA — and your single-region, unmonitored, manually-deployed stack can't deliver. Retrofitting compliance and multi-region architecture costs 3–5x what building it in from the start would have. The enterprise deal slips, or you sign it and spend the next quarter firefighting infrastructure instead of building product.
The result: fragile infrastructure that blocks product velocity, unpredictable cloud costs, and an engineering team that dreads deploy day. We've rebuilt dozens of these setups. We exist because cloud infrastructure should accelerate your product, not hold it hostage.
Inventiple approaches cloud-native development with the same conviction we bring to product engineering: senior engineers, right-sized architecture, and infrastructure your team can own and operate after we leave.
We don't reach for Kubernetes because it's trendy. We pick the simplest architecture that meets your scale, compliance, and team requirements — ECS for most startups, EKS when you need multi-tenancy or GPU workloads, serverless for event-driven pipelines. Every architectural decision comes with a written rationale you can share with your board or your next VP of Engineering. We design for multi-region from the architecture level — stateless services, externalized session storage, database replication strategies — so adding a EU region when your first European enterprise customer requires GDPR compliance is a configuration change, not a six-month rewrite.
Every resource — VPC, clusters, databases, IAM roles, DNS, monitoring — is defined in Terraform and stored in your GitHub repository. No click-ops in the AWS console that nobody can reproduce. No infrastructure that lives on a consultant's laptop. You can tear down and rebuild your entire environment from a git commit.
Logging, metrics, tracing, and alerting are part of week-one infrastructure — not a phase-two afterthought. We deploy OpenTelemetry collectors, Grafana dashboards, and PagerDuty alerting before your application code ships. For AI workloads, we add LLM-specific cost and latency tracking so you always know what your agents are spending.
GitHub Actions or ArgoCD pipelines that run tests, build containers, push to ECR, and deploy to staging automatically on every merge. Production deploys are one approval click away, with automatic rollback if health checks fail. Your engineers should never SSH into a server to deploy code.
We tag every resource, set budget alerts, configure autoscaling with sane limits, and use spot instances for non-critical workloads. Cloud cost isn't someone else's problem — it's designed into the architecture. Our clients typically see 25–40% lower cloud bills than equivalent setups built without cost discipline. Read our architecture comparison guide for when containers, serverless, or edge make sense at your stage.
Here's how a standard cloud-native platform engagement runs. Timelines adjust based on whether we're building greenfield infrastructure or migrating an existing system. Migration engagements typically add 2–4 weeks for parallel running, data validation, and traffic cutover. Greenfield builds for new products on a clean AWS account are the fastest path.
One-week assessment of your current architecture, cloud spend, deployment process, and scaling bottlenecks. Output: a written migration or build plan, fixed-price quote, and target architecture diagram. You're free to walk away here. (15–20 hours)
Terraform modules for VPC, subnets, security groups, IAM roles, and state management. Container registry (ECR), secrets management (AWS Secrets Manager or Vault), and base monitoring stack deployed. End of week: empty but production-ready cloud environment. (60–80 hours)
EKS or ECS cluster with node groups, autoscaling policies, ingress controller (ALB or NGINX), cert-manager for TLS, and network policies. Service mesh (if needed) and internal DNS. End of week 3: cluster accepting deployments. (120–160 hours)
GitHub Actions or ArgoCD pipelines for build, test, containerize, and deploy. First application services migrated or deployed. Staging and production environments with promotion gates. Database migration strategy executed if applicable. (120–160 hours)
Full observability stack: structured logging, Prometheus metrics, Grafana dashboards, distributed tracing, error tracking, and on-call alerting. Runbooks for common operational scenarios. LLM cost tracking for AI workloads. (60–80 hours)
Load testing, chaos engineering basics, security review (IAM audit, network segmentation, secrets rotation), backup and disaster recovery validation. Penetration test coordination if compliance requires it. (50–70 hours)
Multi-region setup if scoped, production traffic cutover from legacy infrastructure, DNS migration, documentation review, and knowledge transfer to your team. 30 days of post-launch operational support included. (40–60 hours)
Cloud infrastructure pricing depends heavily on scope — greenfield vs. migration, single-region vs. multi-region, number of services. Here are the ranges we quote most often in 2026.
Greenfield infra for a single app. ECS or EKS, CI/CD, basic observability.
Microservices decomposition, staging + prod environments, migration from legacy.
Multi-region, compliance (SOC 2/HIPAA), GPU workloads, or service mesh.
Ongoing platform engineering retainers: $15,000–$40,000/month for cluster operations, incident response, and cost optimization. See our FinOps guide for cloud cost management strategies.
We use proven, widely-adopted tools that your team can hire for and operate long after our engagement ends. No proprietary platforms, no vendor lock-in beyond what your cloud provider requires.
For teams already on GCP or Azure, we adapt the stack while keeping the same patterns — Terraform modules, container orchestration, GitOps deployment, and observability. The principles don't change across clouds; only the managed service names do. See our microservices guide for when service decomposition makes sense at your scale.
Cloud-native infrastructure is an investment — not something every startup needs on day one. Here's how to know if you're at the inflection point where professional platform engineering pays for itself.
A typical cloud-native engagement from Inventiple ranges from $40,000 to $80,000 for greenfield infrastructure (Kubernetes cluster, CI/CD, observability) over 8–10 weeks. Full platform builds with microservices decomposition, multi-region deployment, and compliance scaffolding range from $80,000 to $120,000 over 10–14 weeks. Ongoing platform engineering retainers after delivery typically run $15,000–$40,000/month depending on cluster size and operational scope.
For most funded startups in 2026, we recommend starting with managed containers (AWS ECS or EKS) rather than pure serverless or raw Kubernetes. ECS gives you container portability without the operational overhead of running your own control plane. We move clients to EKS when they need multi-tenant isolation, custom networking, or GPU workloads for AI inference. Pure Lambda/serverless works for event-driven, spiky workloads but becomes expensive and hard to debug at scale.
A phased migration from a monolith or legacy VPS setup to cloud-native infrastructure typically takes 8–12 weeks using the strangler fig pattern — new services deploy alongside legacy components, traffic shifts incrementally, and nothing goes dark. Simple lift-and-shift to managed containers takes 4–6 weeks. Complex migrations with database splitting, multi-region cutover, or compliance requirements need 12–16 weeks. We always start with a one-week infrastructure audit before quoting.
AWS is our primary platform — EKS, ECS, RDS, Lambda, S3, CloudFront, and IAM. We also deploy on GCP (GKE) and Azure (AKS) when clients have existing commitments or regulatory requirements. For early-stage startups, we often recommend AWS or Vercel + Supabase for speed, then migrate to full Kubernetes when scale demands it. We're cloud-agnostic in architecture but opinionated about picking one primary provider per environment.
Terraform is our default for production infrastructure — modules for VPC, EKS, RDS, IAM, and monitoring, stored in your GitHub repo with state in Terraform Cloud or S3. We use Pulumi when teams are TypeScript-native and want infrastructure defined in the same language as their application code. Either way, you own the IaC repo from day one. Nothing lives only in a consultant's laptop.
Every engagement includes: structured logging (CloudWatch or Grafana Loki), metrics and dashboards (Prometheus + Grafana or Datadog), distributed tracing (OpenTelemetry + Tempo or Jaeger), error tracking (Sentry), uptime monitoring, and on-call alerting with PagerDuty or Opsgenie integration. For AI workloads, we add LLM-specific observability — token usage, latency per model, and cost tracking via Helicone or LangFuse.
Yes. Our FinOps audits typically find 25–40% savings through right-sizing instances, reserved capacity planning, spot instance strategies for non-critical workloads, eliminating orphaned resources, and tuning autoscaling policies. We've cut monthly AWS bills from $45,000 to $28,000 for a Series B SaaS client without any performance degradation. Audits start at $8,000 for a two-week engagement with a written savings report.
Yes. We design multi-region architectures for clients who need low latency globally (US + EU is most common), data residency compliance (GDPR, data sovereignty), or high availability beyond a single region. This includes active-passive or active-active database replication, global load balancing via Route 53 or CloudFront, and runbooks for regional failover. Multi-region adds 3–4 weeks and $20,000–$35,000 to a standard engagement.
Three options. (1) Full handoff — we deliver runbooks, documentation, and train your team over 2–3 sessions. (2) Platform engineering retainer — we handle cluster upgrades, incident response, and cost optimization on a monthly basis. (3) Hybrid — we stay on for 90 days post-launch while you hire your first DevOps engineer. About 60% of our cloud-native clients choose option 2 or 3 for the first year.
Yes. About half our cloud-native engagements augment an existing team rather than replacing one. We embed with your engineers, follow your conventions where they make sense, and focus on the specific gaps — usually Kubernetes hardening, observability, CI/CD maturity, or migration execution. We're contractors accelerating your team, not a black-box agency that disappears after delivery.