CloudGoCustomer: Telos
Industry: AI and Developer Productivity
Stage: Early-stage SaaS
Cloud Platform: AWS
Use Case: Migration planning, scaling strategy, and cost optimization
Metric Highlights
- Minimal downtime required for the planned production cutover
- Independent scaling enabled for API services and background AI workers
- Approximately 50 to 60 percent lower infrastructure cost at scale compared to Render
- Clear AWS break-even point identified at roughly 500 concurrent customers
- AWS-ready architecture and migration plan delivered in 1 day
Overview
Telos is an AI-powered developer productivity platform that helps teams turn high-level requirements and ideas into actionable development tickets and visual mockups. By analyzing a company’s tech context and inputs, Telos automates parts of the product planning and design process that are typically manual, bridging the gap between product management and engineering teams. This approach accelerates team alignment and reduces the time from concept to implementation.
The team launched on Render to move quickly, but as usage increased, it became clear that long-term growth would require a more scalable and cost-efficient cloud foundation. When they received free AWS credits, they decided they should create a plan to migrate over when they needed to scale.
CloudGo.ai partnered with Telos to design a future-ready AWS architecture, model infrastructure costs based on real usage patterns, and define a phased migration strategy that minimizes risk while preserving flexibility.
The Challenge
As Telos grew, limitations in their Render-based setup became more pronounced. Scaling required cloning entire service stacks, which increased costs and reduced efficiency. API traffic and background AI workloads were tightly coupled, preventing independent scaling. In addition, the team lacked access to advanced cloud primitives needed for long-term security, isolation, and standardization. Most critically, there was no data-backed answer to when migrating to AWS would make financial sense.
Why CloudGo.ai
Telos chose CloudGo.ai to avoid a reactive migration under pressure. Instead of moving workloads prematurely, the team wanted a clear understanding of the right architecture, the right timing, and the true cost implications.
CloudGo.ai was selected because it could translate an existing PaaS deployment into a right-sized AWS architecture, identify real scaling boundaries between services, and build a cost model tied directly to peak concurrency. Just as important, CloudGo.ai provided a migration strategy that prioritized safety, rollback, and minimal operational overhead.
The Solution
First, CloudGo.ai designed a low-operations AWS architecture that closely matched Telos’ existing service model. The frontend was mapped to static hosting with CDN support, while the backend API and AI worker services were separated so they could scale independently. Managed Redis and PostgreSQL replaced self-managed state, and centralized logging, metrics, secrets, and alerts were introduced to support production reliability.
Second, CloudGo.ai defined a phased migration strategy designed to eliminate downtime risk. This approach relied on shadow deployments and progressive traffic shifting using weighted DNS, while keeping Render live as a rollback option during the transition window. Explicit validation checkpoints ensured authentication flows, webhooks, and background job processing behaved correctly before full cutover.
Finally, CloudGo.ai built a realistic cost and scaling model based on how Telos actually grows. This analysis showed that AWS and Render costs are similar at low usage, but that AWS becomes meaningfully cheaper as concurrency increases. At higher scale, AWS enables cost savings by allowing only the services under load to scale, rather than duplicating entire stacks.
One suggested architecture for Telos:
Results
With CloudGo.ai, Telos now has a production-ready AWS architecture aligned with its product and growth goals. The team has a clear, low-risk migration plan with rollback built in, along with a data-backed understanding of when AWS becomes the financially optimal choice. Instead of guessing or reacting under pressure, Telos can migrate with confidence when demand warrants it.
For growing SaaS teams, migrating too early can waste money, while migrating too late can introduce reliability risks. CloudGo.ai helps teams plan cloud migrations ahead of scale, understand real cost tradeoffs, and move to AWS with confidence rather than trial and error.
Ready to Plan Your Migration?
CloudGo.ai works with startups and scaling SaaS companies to design AWS architectures tailored to their existing stack, model costs using real usage assumptions, and execute phased, rollback-safe migrations.
Plan your migration before scale forces your hand.