Choosing the Right Infrastructure for Your AI App
Table of contents
1. Hosting that matches your deployment model
Whether you ship to Vercel, Fly.io, or container-based platforms, align the decision with your runtime, latency requirements, and compliance needs.
- • Serverless frameworks shine for event-driven AI workloads.
- • Container platforms give finer control for GPU-backed inference.
- • Hybrid architectures keep prototypes nimble while scaling critical paths.
2. Data stores designed for iteration
Most AI prototypes start with a single database. In production, we layer in persistence tailored to the workload and compliance requirements.
- • Managed Postgres remains the default for structured app data.
- • Vector databases join when retrieval quality matters.
- • Blob storage covers user uploads, embeddings, and larger artifacts.
3. Observability that accelerates learning
Visibility is the fastest way to understand how your AI app behaves in the wild. Instrument early and adjust quickly.
- • Stream logs and traces to a central dashboard with alerts.
- • Track prompt success metrics and latency across providers.
- • Create feedback loops from production data back into model fine-tuning.
Frequently asked questions
How do you choose between serverless and container platforms?
We evaluate runtime requirements, latency expectations, and compliance constraints, then pick the platform that balances speed to launch with long-term maintainability.
Do you support multi-cloud or hybrid deployments?
Yes. When teams need regional redundancy or specialized workloads, we design infrastructure that spans providers while keeping ops manageable.
What observability tools do you typically recommend?
We lean on hosted stacks like Datadog, OpenTelemetry pipelines, or Vercel Analytics depending on budget and complexity, ensuring AI-specific signals are captured.
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