We’re building AI Angels, a personalized conversational AI platform with contextual memory and multimodal generation.

This week we hit an all-time high in daily active users, which pushed our infrastructure harder than expected and surfaced several scaling challenges.

Some of the areas we’ve been working through:

Managing inference spikes during peak hours

Memory persistence without excessive token growth

Conversation summarization vs full-context replay

Session concurrency limits

Moderation pipelines at scale

Subscription + payment load handling

One of the more interesting problems has been balancing persistent conversational memory with latency and cost efficiency. We’re currently experimenting with hybrid approaches (short-term context window + structured long-term memory storage).

For those running AI-first SaaS products:

How are you handling long-term conversational memory?

Are you using vector DBs for user history or structured state storage?

How are you compressing conversation history efficiently?

Any best practices for inference cost optimization at higher concurrency?

Happy to share more technical details if useful.