Blog
Notes from the team building the Postgres backend for AI apps — RAG, retrieval, and agents.
Agentic AI hedge funds are reshaping quant trading. Discover how leading firms deploy autonomous AI agents to analyze markets and execute strategies.
Learn how prompt caching for AI agents cuts cost and latency in long loops — KV cache basics, provider differences, cache hit rates, and best practices.
Row-level security for AI agents raises a key question: should they run as the user or not? Explore the tradeoffs to make the right call for your system.
Which industry is most disrupted by agentic AI? We rank the sectors — with financial services leading — and explain the jobs, SaaS, and healthcare fallout.
Claude vs OpenAI enterprise, compared on seat pricing, HIPAA and SOC 2 compliance, context windows, cloud deployment, and API tooling.
Claude workspace vs custom build: learn where Claude Projects are enough, where they hit a wall, and when to build custom with Powabase.
Vibe coding backend risks like security holes and tenant data leaks make from-scratch builds fragile. See why Powabase makes core backend logic reliable.
The AI deployment services available in 2026, from hyperscaler platforms and serverless GPU inference to MLOps, BaaS, and forward-deployed engineering.
The main types of world models in AI: latent-space, JEPA, generative, and object-centric, with real use cases in driving, robotics, games, and science.
The best backend as a service compared: Firebase, Supabase, Neon, Appwrite, and Powabase on pricing, features, AI, and lock-in, plus which to pick.
Enterprise IT full stack vs external vendors: which wins? Learn when owning your stack beats outsourcing and how to make the right call for your org.
Explore how agentic AI in energy is transforming oil & gas, grids, and mining, with ADNOC and AIQ deployments, governance, digital twins, and ROI.
A deep dive into agentic AI in government: how federal agencies deploy AI agents, the OMB M-25-21 governance rules, security risks, and readiness frameworks.
Learn how to run a MoE LLM locally on your GPU and wire it to a BaaS backend. A step-by-step guide to fast, cost-efficient AI in your own stack.
Searching for the best automation tool 2026? We compare viaSocket, Zapier, Make, n8n, and Powabase so you can pick the right fit for your workflows.
Agentic AI in industrials: adoption rates, real deployments, use cases, OT integration, governance, and a roadmap from pilot to production.
Agentic AI in healthcare: how it differs from generative AI, real use cases, market growth, adoption barriers, governance, and workforce impact.
A 200-line Postgres agent orchestrator shows your database can be the framework. What that agent backend gets right, and what production still needs.
A deep dive into agentic AI in media: adoption stats, real use cases, multi-agent infrastructure, governance, IP risks, and the ROI reshaping the industry.
Master multi-tenant RAG tenant isolation with proven strategies to keep data secure, prevent leakage, and scale confidently across all your customers.
How agentic AI in gaming is reshaping NPCs, living worlds, and development pipelines, plus adoption trends, risks, and what the EU AI Act requires.
Build an AI customer support agent on Postgres: tool-calling, pgvector semantic search, escalate-on-no-match, and one database instead of Pinecone plus a CRM.
How to build long-running agents on Postgres with durable, resumable state, so workflows survive failures and pick up where they left off.
A practical guide to working with an AI deployment advisor: what they do, how engagements run, governance, pricing, ROI, and how to choose the right partner.
How AI adoption by industry compares in 2025: adoption rates, leading sectors, generative and agentic AI use, ROI, and the top barriers.
Store agent memory in Postgres without a separate vector store. Learn how one database handles everything your AI agent needs to remember.
Assessing economic benefits of AI deployment is complex. Learn the key metrics and methods to measure ROI and make smarter AI investment decisions.
Learn how to reduce LLM API costs without sacrificing quality using model routing, prompt caching, token trimming, batching, and smart cost tracking.
When to say no to an AI deployment: the red flags, readiness gaps, and governance, oversight, and ROI tests that say reject, wait, or kill it.
Pinecone's $50/month minimum killed hobby RAG. This Pinecone alternative walks you through migrating to pgvector with vec2pg, schema, code, and cost math.
An agentic RAG loop's real dependency is a governed RAG backend: pgvector, BM25, metadata, and state on one Postgres, not LangGraph glue.
Avoid the enterprise AI deployment mistakes that stall pilots before scale: unready data, runaway costs, weak governance, and agents rushed into production.
Learn how to assess AI workflow readiness with scoring criteria, the new-employee test, HITL, ROI, and a go/no-go checklist for deciding what to automate.
Learn how FlutterFlow Powabase lets you build powerful AI apps with ease. Follow our step-by-step guide and ship your first AI-powered app faster.
Streaming MoE experts on-demand lets you run massive language models on minimal RAM. Learn how expert offloading works and why it changes everything.
A step-by-step guide to building an internal AI deployment team: the right roles, hiring sequence, operating model, governance, and SLAs that make AI stick.
Not sure where to start with enterprise AI deployment? Follow a 5-step guide covering readiness, use cases, build vs. buy, governance, and ROI.
Why AI app agencies are moving from one-off builds to managed hosting and MRR: own a governed multi-tenant backend instead of reselling SaaS.
CI/CD for AI demands more than standard DevOps. Learn how to build robust pipelines that handle the unique challenges of ML and LLM workflows.
The enterprise AI decision makers behind every deal: CTO, CAIO, CFO, CISO, the buying committee, and the line-of-business leaders who shape purchases.
The most common enterprise AI workflow automation use cases across customer service, finance, HR, IT, and supply chain, plus ROI and rollout tips.
Agencies pay $200–400/hr to fix unreviewed AI code. How an agent-native backend as a service ships correct schemas, RLS, and migrations.
Vibe-coded apps outgrow their prototype fast. Here's why vibe coding platforms need a solid BaaS underneath to scale and keep data in sync.
Regulated enterprise AI buyers weigh compliance, data sovereignty, auditability, and lock-in over raw accuracy. What they evaluate and the red flags.
A practical guide to custom AI deployment for enterprises: fine-tuning vs RAG, on-premise vs cloud vs sovereign, security, compliance, and LLMOps.
A step-by-step guide to token efficiency: measure usage, tighten prompts, add caching, route models, and cut LLM costs while keeping AI systems fast.
Unified BaaS vs compose-your-own stack: which wins for your project? We compare cost, flexibility, and speed so you can choose with confidence.
Build vs buy enterprise AI? Use this framework to weigh custom AI workflows against off-the-shelf platforms on cost, lock-in, compliance, and orchestration.
93% of teams hit an AI infra incident last year. See why a backend for AI apps needs tenant isolation, RLS, and validation as governed BaaS primitives.
A production RAG audit says you don't need LangChain in 2026. The alternative: native SDK, Postgres/pgvector, and a thin router you don't build.
Using Supabase as your backend for Claude Code? Learn why it keeps breaking and which agent-native alternatives actually hold up under AI-driven workloads.
Our Postgres MCP server comparison shows why servers with 2–10 tools break coding agents, and what to look for in one that actually works.