Your generative features deserve production infrastructure

We build the AWS-native infrastructure layer for generative workloads - Bedrock, SageMaker, Step Functions, Lambda - wired for production, cost-controlled, and handed off to your team in 5-6 weeks.

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No ongoing dependency. Your engineers own everything after handoff

/01
Why It Matters

Most teams self-assemble generative infrastructure - self-hosted models, vector databases, Python-script orchestration. It works on staging. Production brings cold starts, runaway costs, and zero visibility into quality degradation.

AWS has managed services for every layer. Wiring them into a production system that scales, stays within budget, and meets compliance requirements takes patterns AWS doesn't document.

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What It Involves

We replace self-managed GPU clusters and open-source patchwork with managed services. Bedrock for model hosting. SageMaker for fine-tuning. Step Functions for orchestration. Lambda for event-driven inference. EKS integration where your team runs Kubernetes.

Every component wired for production: auto-scaling, cost controls per endpoint, IAM policies, CloudWatch monitoring with custom metrics, infrastructure-as-code (CDK/Terraform), and compliance configurations scoped to your requirements.

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What You Get

Production Generative Infrastructure. Bedrock endpoints, SageMaker pipelines, Step Functions workflows, Lambda functions - running, monitored, scaling with your traffic.

Cost & Performance Dashboard. CloudWatch-native dashboards: real-time inference costs per model, per endpoint, per feature. Latency percentiles, throughput metrics, cost anomaly alerts. Your team operates it with standard AWS skills.

Why Streamlogic for GenAI Infra

Build and Leave

Most AWS partners upsell to managed service contracts. We don't: not our core business. Fixed-fee engagement, and then your team runs it independently. We join only when requested.

Generative Focused

We don't do cloud migrations or networking overhauls. Narrow focus means pre-built patterns and faster delivery than a generalist partner staffing up.

Cost Engineering

We architect for right-sized compute, Bedrock pricing models, endpoint auto-scaling, and caching before your first production request. Teams typically see 30-50% lower inference costs compared to naive deployments.

5-6 Weeks, Not 5-6 Months

Large AWS partners scope these engagements at 12-20 weeks with team ramp-up. We run focused engagements with senior engineers who've deployed these patterns before. Firm price before any work starts.

How We Deliver

1
Days 1-3: Infrastructure Audit

Map your current generative stack and AWS footprint. Identify what managed services replace, what integrates with existing K8s/ECS, what stays. Firm price and architecture plan.

2
Weeks 1-3: Core Deployment

Deploy Bedrock endpoints, configure SageMaker, wire Step Functions, build Lambda layer. IAM, compliance, IaC repository, EKS integration where applicable.

3
Weeks 3-5: Integration & Optimisation

Connect to your product. Tune auto-scaling, cost controls, caching. Load test under realistic traffic. Cost baseline established.

4
Weeks 5-6: Dashboards, Runbook

CloudWatch dashboards deployed. Runbook delivered. Team walkthrough completed. Full ownership transferred.

FAQ

Why move from self-managed GenAI to AWS managed services?

Self-managed inference infrastructure works at prototype scale but becomes expensive and fragile in production. Managed services eliminate GPU provisioning, model serving maintenance, and most scaling complexity - your engineers focus on features, not operations.

Which AWS services do you deploy?

Primary: Bedrock, SageMaker, Step Functions, Lambda, EventBridge, CloudWatch. Supporting: S3, DynamoDB, API Gateway, IAM, KMS, VPC, EKS. Infrastructure-as-code via CDK or Terraform, matched to your existing tooling.

We run Kubernetes. How does this integrate?

We integrate Bedrock and SageMaker endpoints into your existing EKS service mesh and networking. The generative layer works alongside your K8s stack.

What about HIPAA and GDPR?

HIPAA-eligible architectures: BAA-covered services, encryption, audit logging, access controls. GDPR: data processing configurations and residency controls. Scoped to your specific requirements. For teams with dedicated compliance, we work within your framework - not around it.

How is it priced?

Fixed-fee. Typical: 5-6 weeks, senior engineers only. Firm price and architecture plan after the 3-day audit - before any commitment. Optimisation-only engagements (existing AWS generative infra) run 2-3 weeks.

What Our Clients Say

Before Streamlogic stepped in, our media pipeline was already efficient. Now it's exceptional. Their team embedded a system that adapts, learns, and scales with our production flow. What used to take hours now takes minutes. What used to slip through cracks now comes out polished. We've seen a measurable lift in both output volume and content quality.

Andrew Krupski, Client Testimonial
Andrew Krupski
Product Director, NT Technology (Lithuania)

As a design-led studio, our work lives in the details - textures, lighting, growth patterns. Before Streamlogic, visualizing complex botanical installations meant hours of manual prep and rendering. They built us an automation layer that feels almost magical: it pulls data from our planning tools and generates near-final visuals in a fraction of the time. We gained headspace. Now my team spends more time designing, less time chasing files. And for the level of quality they delivered, the investment was fair and smart.

Serge Prahodsky, client testimonial
Serge Prahodsky
CEO, April Studio (Poland)

In the legal field, precision, security, and responsiveness are the baseline. What impressed us most about the team at Streamlogic was their discipline, structure, and proactive style of work. 

Dmitri Dubograev Client
Dmitri Dubograev
CEO, Int'l Legal Counsels PC (USA)

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Denis Avramenko, CTO at Streamlogic
Denis Avramenko
CTO, Co-Founder Streamlogic