What is Itzam?

Itzam is an AI platform and open-source backend for building AI apps with SDKs and APIs to access 30+ models.

Itzam supports model and prompt hot swap, streaming responses (streamText), and retrieval-augmented generation (RAG) via context slugs and thread IDs.

Workflows and a playground let developers test prompts, run automated flows, and deploy customer support agents.

Usage and cost management provide provider- and model-level breakdowns, request/token tracking, and observability into latency and spend.

Integrations include Anthropic, OpenAI, and Google models, with four-line SDK examples for quick integration.

Target users include developers building chatbots and AI features, support teams deploying CS agents, and engineering teams managing multi-model deployments and observability.

Itzam pricing

Hobby $8/mo

Start building basic $8/mo

Start building pro $16/mo

Verify on the official pricing page.

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Itzam's key features

Streaming text API (itzam.streamText) for real-time responses
Model and prompt hot-swapping (change model and prompt instantly)
Multi-provider model access (30+ models from OpenAI, Anthropic, Google) via SDK/API
Cost and usage tracking with provider/model breakdown (requests, tokens, cost)
SDK and workflow engine (generateText with workflowSlug; run workflows and AI agents)

Itzam use cases

Deploy scalable, real-time support agents using Itzam's open-source AI backend and SDKs/APIs to combine streaming responses, model hot-swapping and RAG for accurate, context-aware replies while monitoring usage and cost observability to optimize SLAs and agent handoffs
Build an enterprise knowledge search and answer system by ingesting documents into Itzam's RAG pipelines and using the workflows/playground to iterate retrieval and prompt strategies across 30+ models, enabling fast model swaps for quality/cost tradeoffs and unified observability
Run multi-model production experiments and automated routing with Itzam's model hot-swap API and workflows to A/B test generation quality, latency and cost, stream outputs to clients in real time, and use usage/cost tracking to shift traffic to the most cost-effective models