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Features
Visual and callable workflows: Drag and connect triggers, conditions, agents, HTTP calls, and code blocks, then deploy the graph as an HTTP endpoint that frontends or other systems can call.
Flexible deployment options: Run on Powabase Cloud, self host through Docker or Kubernetes with Helm or Compose, and keep LLM spend under local control with bring your own keys for major providers.
Pros
👍 End to end AI backend in one place: Database, retrieval pipeline, agents, and workflows sit in a single product, which shortens MVP timelines for AI apps and automations.👍 Built for cost awareness: Claims of up to 70 percent token savings and 2 to 4 times lower build cost reflect how retrieval, agents, and workflows are tuned to avoid wasteful calls.👍 High quality retrieval stack: Multiple indexing modes, hybrid and vector search, and rerankers tuned on public benchmarks give serious accuracy for knowledge heavy assistants.👍 Strong observability for agents: Streaming SSE responses with detailed logs of retrieval and tool use make debugging prompts and flows more practical than with opaque hosted chat widgets.👍 Per project isolation: Each project runs on dedicated compute with its own Postgres, vector index, storage, and backups, which suits teams that care about noisy neighbors and data separation.
Cons
👎 Early access status: The platform is still in early access, so pricing, limits, and some edges of the developer experience may evolve quickly.👎 Best suited for AI centric apps: Simple CRUD products that do not lean on RAG, agents, or workflows might find the platform heavier than necessary.👎 Self hosting complexity: Running the full stack in a private cloud or on premises introduces Kubernetes, networking, and security work that smaller teams may not want to own.
