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Inside: Consolidating your multi-database stack into one, confessions from our CEO, and the architecture that survived losing an entire cloud provider.

Latest & Greatest

Learn why multi-database architecture is a problem for AI.

One database to rule them all

When builders ship AI-powered applications, the data layer quietly becomes the hardest part of the stack. What starts as a single PostgreSQL instance grows into a composite architecture: PostgreSQL for transactions, Redis for caching and session state, and a dedicated vector database like Pinecone or Weaviate for embeddings and similarity search. Each system adds complexity with its own connection pools, backup strategies, security perimeters, failover runbooks, and on-call expertise.

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Learn why traditional IAM doesn’t work for agents.

Who is your AI agent, really?

Your security team spent years building access controls designed for a different actor. Roles, permission scopes, MFA, and audit logs built to track what humans do and what they touched. None of it was designed for a software process that might make 40 API calls in 30 seconds, access 500 customer records to complete a single task, and delegate work to other agents without a human approving each step—a gap at many enterprises shipping agentic systems.

Learn why traditional IAM doesn’t work for agents →

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Spill the tea with our CEO & co-founder!

Spencer Kimball built a database named after one of the world’s most resilient (and least appreciated) creatures. Now he’s spilling the tea in this 20 minute no-slides, no-filter session with hot takes, hard questions, and opinions that usually stay off the keynote stage.

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Inference engineering…the next big role in AI?

In this episode of the Big Ideas in App Architecture podcast, David talks with Philip Kiely, engineer at Baseten and author of Inference Engineering, a free guide to building and operating AI inference systems. He walks through how agentic workloads are reshaping inference demands, which open-weight models are worth watching, and key optimization techniques including KV cache reuse, quantization, and speculative decoding.

Listen here →