Cloud Native consultancy · since 2019

Cloud infrastructure that doesn't hold you back when the business accelerates.

Specialist consultancy in AWS, Kubernetes and multi-cloud for companies that need to scale platform - including AI, LLM and GPU workloads - without inflating cost, headcount or operational risk. Structured migrations, application modernization and IaOps operations with end-to-end observability, auditable SLOs and high availability designed for four-nines SLA.

Talk to engineering See cases

~/shiftcore/migration · plan.tf

01# target: us-east-1 / 3 AZ / multi-tenant EKS

02module "eks" {

03 source = "./modules/eks-hardened"

04 cluster_version = "1.36"

05 cks_baseline = true # pod-security, runtime, audit

06 karpenter = true

07 cost_target_usd = 42000 # -38% vs current

08}

10$ terraform plan -out=core.plan

11✓ 142 to add · 0 to destroy · 0 drift

cluster · prd-core-01 ● healthy · p99 84ms · 0 alerts

+120workloads

migrated to cloud

38%

average cost reduction

99,98%

average cluster SLA

6wks

typical time-to-prod

Platform pillars · 02

Six fronts every consultancy promises. We measure them.

SLA 99,98% active contracts · last 24 months

IaOps

100% IaC

Infrastructure as Code

Terraform, Pulumi and Crossplane with reproducible pipelines. No change goes through the console - everything in PR, with reviewed plan/diff.

Observability

3pillars

Logs, metrics, traces

Unified OpenTelemetry stack. SLOs tied to error budget, per-domain dashboards and alerts that wake someone only when they need to.

FinOps

−38%

Cost under governance

Karpenter, savings plans, continuous right-sizing and per-team showback. Every workload has an owner, tag and spending rationale.

HA / DR

99,99%

High availability

Multi-AZ architectures by default, multi-region when RPO/RTO demands. Disaster recovery tested in quarterly GameDays, not in slides.

Security

0incidents

Continuous security

Workload identity, image signing, runtime detection and audited least-privilege. Compliant with PCI-DSS, ISO 27001, GDPR and SOC 2.

AI & Op.

6fronts

AI in operations

Infra, cost, code, deploy, security and scale - AI touches all six at once. We treat it as platform engineering, not as a model demo.

Services · 03

What we deliver.

We work as an extension of your platform team. Every project starts by understanding context, constraints and goals - before proposing scope - and ends with runbooks, automation and the in-house team ready to operate, not depend on us.

S/01

On-Premise → Cloud migration AWS · primary

For the business — leave the datacenter without downtime, with predictable cost and a real timeline.

Automated discovery, wave planning and cutover without a dark window. Stateful databases, mainframe-adjacent apps and heavy network dependencies are our bread and butter.

6R assessment · comparative TCO
Multi-account Landing Zone with Control Tower
Replatform with EKS / ECS · OCI containers
SQL Server, Oracle and legacy migrations

S/02

Cloud → Cloud multi-vendor

For the business — cut single-vendor dependency and unify governance after M&A.

Exit from single-vendor lock-in or consolidation after M&A. We move workloads between AWS, Azure and GCP preserving observability, identity and compliance.

Re-architecture for equivalent managed services
Cross-cloud SSO/IAM federation
Data replication with < 4h window
Unified FinOps post-migration

S/03

Application Modernization app · 12factor

For the business — unlock delivery speed without rewriting the product from scratch.

We extract monoliths from WAS/IIS, refactor in strangler-fig and package them in Kubernetes-ready containers. No big-bang, no 18-month rewrite.

Containerization · Helm · ArgoCD GitOps
Service mesh when it makes sense (Istio/Linkerd)
API gateway, authentication and rate-limiting
Observability OpenTelemetry end-to-end

S/04

Kubernetes Platform CKA · CKS

For the business — a platform your team can operate, with cost control and compliance.

EKS, AKS, GKE or bare-metal. We build hardened platforms with pod security, KMS-backed secrets, runtime security and predictable cost via Karpenter/Cluster Autoscaler.

Cluster bootstrap in reproducible IaC
Multi-tenancy with namespaces + OPA Gatekeeper
Tested disaster recovery (Velero, etcd backups)
Developer onboarding via developer portal

S/05

SRE & DevOps as a Service on-call

For the business — senior coverage while your team ramps up, without permanent hiring.

We extend your team for 3–12 months with senior on-call engineers, living runbooks and real SLOs. We leave when your team runs on its own.

Error budget, postmortem and monthly GameDay
CI/CD pipelines with SLSA 3+
Continuous cost optimization (savings plans, RIs)
Documented knowledge transfer

S/06

Cloud Native Security CKS · zero-trust

For the business — pass audits and reduce risk without blocking the engineering roadmap.

Platform threat model, cluster hardening and supply chain pipeline review. We meet PCI-DSS, ISO 27001, GDPR and SOC 2 requirements.

Workload identity without long-lived secrets
Image signing (cosign) and SBOM per build
Runtime detection (Falco/Tetragon)
IAM auditing and continuous least-privilege

AI in operations · 04

AI changes infra, cost, code, deploy and risk. All at once.

For the business → adopt AI without blowing up the cloud bill, leaking sensitive code, or getting locked into a single vendor.

Adopting AI is not just "spinning up an LLM endpoint". It impacts capacity planning, cloud billing, code review standards, deploy policy, attack surface and scalability model. We tackle the six fronts together - because they fail together.

F/01 · Infra

Where AI touches the platform

Inference workloads change the capacity profile: VRAM as bottleneck, long cold starts, bursty traffic patterns. We design dedicated node pools, scale-to-1 when it makes sense and tenant isolation.

GPU node pools with Karpenter (H100 / A100 / L4)
vLLM, Triton, KServe and NIM on EKS / GKE
Self-hosted RAG (Qdrant, Pgvector) with cache

F/02 · Cost

FinOps when the token is the unit

Commercial APIs bill per token, dedicated GPUs bill per hour, embeddings bill twice. We map cost-per-feature, compare vendor vs. self-hosted and cut invisible spend - cache, batching and model fallback.

AI showback per team, feature and endpoint
Anomaly alerts before invoice close
Bedrock / Vertex / Anthropic with budget guardrails

F/03 · Code

Copilot, but under company policy

Code assistant integrated with GitLab / GitHub - with self-hosted model when code is sensitive. Automated review standards, test generation and suggested ADRs. Without sending the whole repo outside.

Internal LLM gateway · per-dev audit log
Test scaffolding and doc generation in the PR
Auditable usage policy (PII, IP, secrets)

F/04 · Deploy

AI inside the pipeline, not on top of it

LLM reviewing Terraform plans, summarizing diffs, classifying PR risk before the human. Changelogs and release notes generated from commit history. Runbook draft the moment the alert fires.

PR risk scoring (blast radius, cost, breaking change)
LLM-assisted postmortem (event correlation)
Runbook generation from past incidents

F/05 · Security

New surface, old controls aren't enough

Prompt injection, PII leakage, model supply chain, tool-use jailbreaks. We apply DLP on the prompt path, tool-calling gating and per-session auditing. Compliant with GDPR, ISO 27001 and SOC 2 for AI-generated content.

Guardrails: PII, jailbreak, output schema, rate-limit
Model signing and SBOM for production models
Prompt / response audit log · configurable retention

F/06 · Scale

Traffic pattern different from what your API knows

Inference has context-dependent latency, unbalanced request size and high marginal cost. We reorganize throttling, queueing and cache so the traditional backend doesn't pay the price of the hype.

Queue-based autoscaling (KEDA, queue_depth)
Semantic cache · routing by cost and quality
Small / medium / large model per request type

.github/workflows/iac-review.yaml · LLM-assisted

01# Terraform plan review with self-hosted model

02name: iac-review · cost + security + blast-radius

03on: pull_request

04jobs:

05 ai-review:

06 steps:

07 - uses: actions/checkout@v4

08 - run: terraform plan -out=plan.json

09 - uses: shiftcore/llm-review@v3

10 with:

11 model: "self-hosted/llama-3.1-70b"

12 plan: plan.json

13 checks: [iam-blast, cost-delta, ha-loss, cve-drift]

14 budget_usd: 0.04 # max spend per PR

16✓ 14 PRs/day · 0 external API calls · avg cost $0.012/PR

workflow · iac-review.yaml ● self-hosted · PII redacted · audit on

−54%

avg IaC review time after LLM in the pipeline

67%

of PRs approved on first review (was 41%)

$0,18

cost per 1M tokens on self-hosted 70B model

prompt / PII leaks in 12 audited months

We don't sell models. We design the operation around them - infra, cost, code, deploy, security and scale - so AI delivers accountable productivity, not accountable risk.

Expertise · 05

Engineers, not resellers.

100% senior team with at least 8 years of cloud. All certifications below are active and maintained - none expired, none "in training". We show the badges under NDA if you want to verify.

/01 AWS Solutions Architect - ProfessionalSAP-C02 · rolling validity AWS 11/14

/02 AWS DevOps Engineer - ProfessionalDOP-C02 AWS 9/14

/03 Certified Kubernetes AdministratorCKA · Linux Foundation CNCF 12/14

/04 Certified Kubernetes Security SpecialistCKS · CKA prerequisite CNCF 7/14

/05 AWS Security - SpecialtySCS-C02 AWS 5/14

/06 Azure Solutions Architect ExpertAZ-305 / AZ-104 Microsoft 4/14

/07 Google Cloud Professional Architect+ Pro DevOps Engineer GCP 3/14

/08 HashiCorp Terraform Associate+ Vault Associate HashiCorp 14/14

Results · 06

Numbers we back up with runbook in hand.

Averages from the last 24 months, 18 closed contracts and 11 active. Every metric is measured before the project starts and audited at close - not estimated in slides.

−42%

Cloud cost reductionAverage across modernization projects completed in 2025

3,4×

Deploys / weekFrom 1× every 2 weeks to several per day

−71%

Incident MTTRAfter onboarding to SLO + OTel observability

Data leaksIn 24 months, with 6 clients on PCI-DSS / GDPR

Monthly cloud cost · before vs. after (USD · average client)

Pre-project Post-shiftcore

sample · n = 11 contracts completed 2024–2025 source · AWS CUR + Azure Cost Mgmt + GCP Billing

Methodology · 07

Five phases. No PowerPoint theater.

Every engagement follows the same cadence. You know the phase you're in, what needs to be delivered next, and who's responsible on each side. Meetings are short; the work lives in pull requests.

PHASE 01 · 1 WEEK

Discovery

Initial conversation, mapping workloads, dependencies, costs and risks. Output: technical report + 6R plan.

Wiz / Steampipe scan
TCO comparativo
Risk map

PHASE 02 · 2 WKS

Landing Zone

Multi-account, IaC, federated identity and guardrails - reproducible foundation in Terraform.

Control Tower / OUs
SSO + IAM Identity Center
Network hub-and-spoke

PHASE 03 · 3–8 WKS

Migration / Build

Wave-by-wave. For each workload: lift, refactor or rewrite - technical decision, not emotional.

Pipelines GitOps
Cutover plan + rollback
Smoke + load

PHASE 04 · 2 WKS

Operation

Observability, SLOs, runbooks e on-call. Time do cliente conduz, nós cobrimos a retaguarda.

OpenTelemetry stack
Error budget policy
GameDay #1

PHASE 05 · CONTINUOUS

Handover

Living documentation, formal transfer and evolution plan. You don't stay hostage to us.

Runbooks as ADR
Hands-on training
6-12 month roadmap

Cases · 08

What we deliver, off the slide.

Three representative engagements from the last 18 months. Names changed under NDA; architectures, metrics and schedule are real - references available on request.

CASE / 01 · FINTECH in operation

Banking core migration from on-prem to multi-region AWS

Result — 54% lower infra cost/year and cutover done in 11 weeks with no dark window.

~ 8 million accounts · PCI-DSS Lv1

−54%infra cost/year

11wkstotal cutover

Exit from own datacenter to multi-AZ EKS with synchronous replication across two regions. Oracle database migrated to Aurora PostgreSQL via DMS with a 38-minute window.

AWSEKSAuroraKarpenterArgoCD

CASE / 02 · RETAIL in operation

Monolithic e-commerce modernization in strangler-fig

Result — Black Friday with zero incidents and 9× higher deploy cadence.

14M visits/month · Black Friday peak

0incidents on BF

9×deploys / week

Decomposition of a .NET monolith into containerized microservices; checkout, catalog and payments extracted progressively. Linkerd service mesh with automatic mTLS.

AWSEKSLinkerdCosignOTel

CASE / 03 · HEALTH-TECH in operation

Exit from single-region Azure to multi-cloud GCP + AWS

Result — 99.99% SLA and 31% lower aggregate cost after leaving single-region.

2.1M users · GDPR + HIPAA-ready

99,99%post-migration SLA

−31%aggregate cost

Inference workloads migrated to GKE with on-demand TPUs; sensitive data isolated in AWS São Paulo. Workload Identity federation across clouds.

GCPAWSGKEEKSVault

CASE / 04 · LOGTECH in operation

Kubernetes Platform self-service para 180 devs

Result — 71% lower MTTR and 6 minutes from commit to production for 180 devs.

42 microservices · continuous delivery

−71%Incident MTTR

6minfrom commit to prod

Internal developer portal on Backstage, golden-path templates and end-to-end GitOps. Platform team cut ticket queue by 84% in the first quarter.

AWSEKSBackstageArgoCDCrossplane

CASE / 05 · GENAI in operation

Self-hosted LLM platform for support and product copilot

Result — 68% savings vs. commercial API and 410ms p95 latency on our own LLM.

8× H100 · 22k tokens/s · GDPR-ready

−68%cost vs. commercial API

410msp95 latency

Llama-3.1 70B on vLLM with tensor-parallel, RAG over Qdrant and PII guardrails. Spot fallback at 87% average utilization - inference dropped from $0.52 to $0.18 per 1M tokens.

AWSEKSvLLMQdrantKarpenter

05 cases · drag →

Clients · 09

What people who put us in production say.

Testimonials collected after the close of each engagement. Names are publicly omitted for confidentiality - we forward direct references in commercial conversations.

“

ShiftCore was the first vendor that refused a scope of ours because "you don't need to pay a consultancy to do this". We ended up with −47% cost, deploys in hours instead of weeks, and - for the first time - a runbook the on-call team actually understands.

Head of Platform Engineering FINTECH · 1,200 employees

“

I've done three AWS migrations in my career. This was the only one where cutover happened on schedule, with no rollback, and the in-house team learned enough to evolve on their own afterwards.

CTO B2B SaaS · series C

“

We hired them for the CKS. We stayed for how they deliver: pull request, ADR, documentation, GameDay. Grown-up engineering.

Technology Director RETAIL · publicly traded

“

In four months we stopped treating Kubernetes as a black box. Senior, didactic team that doesn't disappear after the final acceptance.

VP of Engineering HEALTHTECH · 250 eng.

“

It was the first conversation where the consultancy asked more than they presented. We left the first call with 27 findings to review, 9 obvious quick-wins and the technical reason for each. In two weeks they'd drawn up a plan three previous vendors couldn't.

Head of SRE LOGTECH · unicorn

Stack

Technologies that run our day-to-day.

AWSKubernetesTerraformEKSArgoCDKarpenter vLLMTritonKServeBedrockSageMakerOllama IstioLinkerdVaultPrometheusOpenTelemetryGrafana AzureGCPGKEAKSCosignFalco QdrantPgvectorKubeflowArgo WorkflowsLangChainNVIDIA NIM DatadogGitLabGitHub ActionsHelmPulumiCrossplane AWSKubernetesTerraformEKSArgoCDKarpenter vLLMTritonKServeBedrockSageMakerOllama IstioLinkerdVaultPrometheusOpenTelemetryGrafana AzureGCPGKEAKSCosignFalco QdrantPgvectorKubeflowArgo WorkflowsLangChainNVIDIA NIM DatadogGitLabGitHub ActionsHelmPulumiCrossplane