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  "meta": {
    "productId": "klypup-deploy-001",
    "productName": "Klypup Deploy",
    "version": "1.0.0",
    "locale": "en-US"
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  "hero": {
    "tagline": "Ship AI agents to AWS in minutes, not weeks",
    "subline": "Purpose-built deployment platform for platform engineering teams shipping AI workloads at scale"
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  "stats": [
    {
      "value": "87%",
      "label": "Faster deployment vs. manual setup"
    },
    {
      "value": "<5 min",
      "label": "Time to first agent in production"
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    {
      "value": "$340K",
      "label": "Annual infrastructure cost savings"
    },
    {
      "value": "12x",
      "label": "Increase in deployment frequency"
    }
  ],
  "problem": {
    "headline": "Platform teams waste weeks on AI agent infrastructure instead of shipping features",
    "body": "B2B SaaS startups lack a unified deployment path for AI agents. Teams manually configure VPCs, container orchestration, monitoring, and scaling policies\u2014duplicating work across projects. This delays time-to-market, fragments operational knowledge, and forces platform engineers to become DevOps specialists. The result: AI initiatives stall while competitors ship faster."
  },
  "capabilities": [
    {
      "icon": "\u26a1",
      "title": "One-Click Agent Deployment",
      "body": "Deploy any AI agent to AWS with a single command. Klypup handles VPC provisioning, container setup, auto-scaling, and networking. No Terraform, no CloudFormation templates to maintain."
    },
    {
      "icon": "\ud83d\udcca",
      "title": "Built-in Observability",
      "body": "Real-time dashboards for agent latency, token usage, error rates, and cost per inference. Integrated tracing and alerting eliminate blind spots in production AI workloads."
    },
    {
      "icon": "\ud83d\udd04",
      "title": "GitOps-Native Workflows",
      "body": "Define agent configs in YAML, commit to Git, and auto-deploy on merge. Rollbacks, canary deployments, and version pinning work out of the box."
    },
    {
      "icon": "\ud83d\udcb0",
      "title": "Cost Optimization Engine",
      "body": "Automatic right-sizing of compute, spot instance selection, and batch inference scheduling reduce AWS bills by 40-60% without sacrificing performance."
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    {
      "icon": "\ud83d\udee1\ufe0f",
      "title": "Security & Compliance",
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        "with": "One-click rollbacks, canary deployments, and automated health checks. Zero-downtime updates."
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        "with": "Single config, multi-region deployment. Klypup handles replication, failover, and cross-region monitoring."
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      "response": "EKS requires you to manage node groups, networking, storage, and observability\u2014all before your first agent runs. Klypup abstracts that complexity. You get Kubernetes benefits without the operational tax. Teams using Klypup ship 12x faster than those managing EKS manually."
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    {
      "objection": "How does Klypup handle agent state and persistence?",
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    },
    {
      "objection": "What's the learning curve for our team?",
      "response": "If your team knows Git and basic YAML, they can deploy agents on day one. We provide templates, CLI tools, and a web dashboard. Most teams are productive within 2 hours."
    },
    {
      "objection": "How do you handle cost overruns from runaway agents?",
      "response": "Klypup enforces per-agent cost budgets and auto-scales down when thresholds are hit. Real-time alerts notify your team before overspend occurs. You maintain full control."
    },
    {
      "objection": "Can we use Klypup for non-AI workloads?",
      "response": "Yes. Klypup works with any containerized workload. We optimize for AI inference patterns, but the deployment and observability layer is general-purpose."
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    "phases": [
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        "title": "Onboarding & First Agent",
        "deliverables": [
          "Klypup CLI installed and authenticated to your AWS account",
          "First AI agent deployed to production with auto-scaling enabled",
          "Cost dashboard and alerting configured",
          "Team trained on GitOps workflow and rollback procedures"
        ]
      },
      {
        "week": 2,
        "title": "Optimization & Scale",
        "deliverables": [
          "Multi-agent deployment pipeline established",
          "Cost optimization recommendations applied (40%+ savings identified)",
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Klypup Deploy

Ship AI agents to AWS in minutes, not weeks

Purpose-built deployment platform for platform engineering teams shipping AI workloads at scale

87%

Faster deployment vs. manual setup

<5 min

Time to first agent in production

$340K

Annual infrastructure cost savings

12x

Increase in deployment frequency

The problem

Platform teams waste weeks on AI agent infrastructure instead of shipping features

B2B SaaS startups lack a unified deployment path for AI agents. Teams manually configure VPCs, container orchestration, monitoring, and scaling policies—duplicating work across projects. This delays time-to-market, fragments operational knowledge, and forces platform engineers to become DevOps specialists. The result: AI initiatives stall while competitors ship faster.

What we do

One-Click Agent Deployment

Deploy any AI agent to AWS with a single command. Klypup handles VPC provisioning, container setup, auto-scaling, and networking. No Terraform, no CloudFormation templates to maintain.

Built-in Observability

Real-time dashboards for agent latency, token usage, error rates, and cost per inference. Integrated tracing and alerting eliminate blind spots in production AI workloads.

GitOps-Native Workflows

Define agent configs in YAML, commit to Git, and auto-deploy on merge. Rollbacks, canary deployments, and version pinning work out of the box.

Cost Optimization Engine

Automatic right-sizing of compute, spot instance selection, and batch inference scheduling reduce AWS bills by 40-60% without sacrificing performance.

Security & Compliance

Encryption in transit and at rest, IAM role isolation per agent, audit logging, and SOC 2 Type II compliance baked in. No additional security overhead.

With us vs. without us

	Without	With Klypup Deploy
Deployment Speed	Manual AWS setup: VPC, subnets, security groups, ECS/EKS config, load balancers. 3-4 weeks per agent.	Klypup: Git push → agent live in AWS. 5 minutes. Reusable templates for all future agents.
Operational Overhead	Platform team owns infrastructure, scaling, monitoring, and cost optimization for each AI workload independently.	Centralized control plane. One team manages all agents. Auto-scaling and cost optimization handled by Klypup.
Cost Visibility	Scattered AWS bills. No per-agent cost tracking. Difficult to optimize or justify AI spend to finance.	Per-agent cost dashboards, inference-level billing, and automated cost optimization recommendations.
Rollback & Safety	Manual version management. Rollbacks require SSH access, manual container updates, and downtime risk.	One-click rollbacks, canary deployments, and automated health checks. Zero-downtime updates.
Multi-Region Scaling	Replicate infrastructure across regions manually. Duplicate configs, separate monitoring, operational complexity.	Single config, multi-region deployment. Klypup handles replication, failover, and cross-region monitoring.

Common objections

“We already use Kubernetes. Why not just use EKS directly?”

EKS requires you to manage node groups, networking, storage, and observability—all before your first agent runs. Klypup abstracts that complexity. You get Kubernetes benefits without the operational tax. Teams using Klypup ship 12x faster than those managing EKS manually.

“Does Klypup lock us into AWS?”

Klypup is AWS-native today, optimized for cost and performance on that platform. We're building GCP and Azure support in Q2 2025. Your agent configs remain portable—switching clouds requires a single flag change.

“How does Klypup handle agent state and persistence?”

Klypup integrates with RDS, DynamoDB, and S3 for stateful workloads. We manage connection pooling, failover, and backup automatically. No additional configuration needed.

“What's the learning curve for our team?”

If your team knows Git and basic YAML, they can deploy agents on day one. We provide templates, CLI tools, and a web dashboard. Most teams are productive within 2 hours.

“How do you handle cost overruns from runaway agents?”

Klypup enforces per-agent cost budgets and auto-scales down when thresholds are hit. Real-time alerts notify your team before overspend occurs. You maintain full control.

“Can we use Klypup for non-AI workloads?”

Yes. Klypup works with any containerized workload. We optimize for AI inference patterns, but the deployment and observability layer is general-purpose.

First 2 weeks

Week 1

Onboarding & First Agent

Klypup CLI installed and authenticated to your AWS account
First AI agent deployed to production with auto-scaling enabled
Cost dashboard and alerting configured
Team trained on GitOps workflow and rollback procedures

Week 2

Optimization & Scale

Multi-agent deployment pipeline established
Cost optimization recommendations applied (40%+ savings identified)
Canary deployment strategy tested and validated
Handoff to your team with ongoing support access

Ready to move?

Klypup Deploy · 2-week onboarding

View Documentation Start Free Trial →