Haystack Enterprise Platform vs PrivateGPT

Haystack Enterprise Platform is an operational layer for building, evaluating, deploying and governing production AI agents and applications. Teams can assemble and debug agents and retrieval-augmented generation (RAG) pipelines using a visual builder, templates, custom components and retrieval and memory settings. The Playground and Prompt Explorer support comparisons of prompts, retrieval strategies and agent workflows, as well as prototype sharing and feedback. Deployment options include deepset-managed cloud or self-hosting, with serverless autoscaling and a REST API. Governance tools include role-based access, run history, traces, performance monitoring, usage reports and guardrails. The platform lists SOC 2 Type II and ISO 27001 certifications, GDPR and HIPAA compliance, SSO, audit logs, CSA Star Level 1 and pipeline isolation. Listed model integrations include Anthropic, OpenAI, Mistral, Cohere, Gemini and custom models; data-store integrations include PostgreSQL, Snowflake, Qdrant, Weaviate and Pinecone. Pipelines can be exported as Python or YAML, and models and providers can be changed without rewriting code. Studio is free with stated limits; Enterprise pricing is custom and requires contacting deepset.

PrivateGPT is a free, open-source API layer for building private AI applications on your own infrastructure. It does not run models itself: it connects to a separate OpenAI-compatible inference server, which can be local or remote. The Messages API supports streaming, asynchronous processing, and token counting. Document workflows include file and artifact ingestion, retrieval with citations, and tools for querying databases and analyzing tabular data. The documented capabilities also include web search and extraction, code execution, custom tools, and MCP connectors. A browser workbench at `/ui` can be used for demos and inspecting API requests, but it stores chats and settings in browser localStorage and has no access control; the documentation describes it as a local demonstrator. PrivateGPT can be installed as a Python package, from source, or with Docker, and runs as a local server. Package installation requires Python 3.11; Python 3.10 and 3.12 or later are unsupported. Platforms include Linux, macOS, Windows, web, API, and self-hosted environments.