Restai
Apache 2.0 self-hosted AI-as-a-Service platform: RAG, MCP agents, visual logic, app builder, and evals behind one REST API.
If your constraint is data residency or per-token spend, Restai is one of the few genuinely all-in-one self-hosted offers — RAG with scheduled auto-sync from S3, Confluence, SharePoint, and Google Drive, zero-shot ReAct agents with MCP tools, a Blockly logic IDE, DeepEval-based evals, RBAC, and per-team white-labeling arrive in one Apache 2.0 install instead of five stitched tools. Point it at Ollama or vLLM when you want the whole stack to stay on your own hardware, or at OpenAI, Anthropic, Gemini, Azure, or Bedrock when you want frontier quality on your own plumbing. The catch is you own Postgres, the vector store, and the GPU. Teams with a DevOps habit get real leverage; teams without
Verified 1d ago · liveness 63/100 · cite: rightaichoice.com/tools/restai
- Regulated teams that cannot send data to third-party AI APIs
- Agencies shipping white-label AI to client sites
- Platform engineers who want RAG, agents, evals, and RBAC in one install
- WordPress publishers wanting AI content, SEO meta, and site search
- Teams with no appetite for maintaining Postgres, a vector store, or a GPU
- Buyers who want a fully managed service with zero setup
- Non-technical users expecting a polished consumer app rather than an admin UI
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Skip Restai if nobody on your team wants to own a Postgres instance, a vector store, and upgrades — the Apache 2.0 license buys you control, not an operations team.
You pay for the infrastructure either way: a Postgres instance, a vector store (ChromaDB, PGVector, Weaviate, or Pinecone), and the VM or cluster Restai runs on are all yours to provision and pay for.
Restai publishes an Apache 2.0 community edition with full source on GitHub — all five project types, 11+ LLM providers, 5 vector stores, the eval framework, and MCP support — so the licence cost is zero at any team size. Your real comparison is inference spend. Point projects at Ollama or vLLM and the model bill stays on your own hardware; point them at OpenAI, Anthropic, Gemini, Azure, or Bedrock and those providers bill per token on top. Against managed AIaaS platforms that charge per seat
In short
Restai — Apache 2.0 self-hosted AI-as-a-Service platform: RAG, MCP agents, visual logic, app builder, and evals behind one REST API. Best for Regulated teams that cannot send data to third-party AI APIs, Agencies shipping white-label AI to client sites, Platform engineers who want RAG, agents, evals, and RBAC in one install. Free to use.
What people actually say about Restai — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
45 mentions across 3 sources (YouTube, GitHub, Lemmy) · researched Aug 3, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Free and open-source (Apache 2.0), with no vendor lock-in for self-hosters.
- +Broad integration: 11+ LLM providers and 5 vector stores.
- +Includes RAG, agents with MCP support, and a no-LLM visual logic builder.
- +Built-in analytics for token usage, cost, and latency per project.
- +Enterprise features like RBAC, OAuth/LDAP/OIDC, and TOTP 2FA.
- −Security concerns: possible directory traversal and demo lockout issues.
- −Unicode/PDF parsing bug breaks document handling for non-ASCII files.
- −Missing LiteLLM integration limits access to some less-common LLMs.
- −Sparse community buzz—hard to find detailed user reviews or tutorials.
- −Requires significant setup and troubleshooting, particularly for non-developers.
- • Infrastructure costs for running the platform (servers, storage, LLM API costs).
- • Time investment for setup, maintenance, and security hardening.
Viability Score
How well maintained and how widely used is Restai? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Self-hosted AI-as-a-Service platform under Apache 2.0
- Five project types: RAG, Agents, Block, App Builder, Inference
- RAG with natural-language-to-SQL, ColBERT and LLM-based reranking
- Scheduled knowledge auto-sync from web URLs, S3, Confluence, SharePoint, Google Drive
- Zero-shot ReAct agents with MCP tool support over HTTP/SSE and stdio
- Blockly visual logic IDE that runs without an LLM in the loop
- App Builder with "Call Project" blocks for composing AI pipelines
- OpenAI-compatible REST API for chat, images, and audio transcription
- Analytics dashboard tracking tokens, cost, latency, and model fleet view
- Evaluation framework with Answer Relevancy, Faithfulness, and Correctness
- Automatic prompt versioning with history compare, restore, and eval A/B links
- Image generation via Stable Diffusion, Flux, DALL-E, RMBG2 with NVIDIA GPU auto-detection
- Multi-tenant teams with RBAC, OAuth/LDAP/OIDC SSO, TOTP 2FA
- Input/output guardrails, audit logging, per-project rate limits, and budget caps
- Embeddable chat widget with streaming responses and Shadow DOM isolation
About Restai
Restai is an Apache 2.0 AI-as-a-Service platform you install on your own infrastructure. It ships five project types behind one REST API and a React admin UI: RAG with natural-language-to-SQL and ColBERT or LLM-based reranking, zero-shot ReAct agents with Model Context Protocol tool support over HTTP/SSE and stdio, a Blockly visual logic IDE that runs without an LLM in the loop, an App Builder that composes pipelines through a "Call Project" block, and direct inference. You deploy it via pip, a prebuilt Docker image (amd64 and arm64), or from source. Provider coverage is broad by design: Ollama, vLLM, OpenAI, Anthropic, Gemini, Groq, Grok, LiteLLM, Azure, AWS Bedrock, and any OpenAI-compatible endpoint. Vector storage covers ChromaDB, PGVector, Weaviate, and Pinecone. Knowledge bases stay current through scheduled auto-sync from web URLs, Amazon S3, Confluence, SharePoint, and Google Drive, configured per project. The Web UI is where most teams will live. A centralised dashboard tracks tokens, cost, and latency per project with daily charts, and a model fleet view shows what is running. An evaluation framework runs Answer Relevancy, Faithfulness, and Correctness metrics against test datasets and charts score trends. Prompt versioning automatically saves every system prompt change with history, comparison, restore, and A/B links between eval runs and prompt versions. Enterprise controls include multi-tenant teams, RBAC, OAuth/LDAP/OIDC SSO, TOTP 2FA with admin enforcement, input and output guardrails, full audit logging for all mutations, per-project rate limits, and budget caps. White-label branding sets custom logos, colors, and app names per team. Beyond text, dynamically loaded generators handle image creation via Stable Diffusion, Flux, DALL-E, and RMBG2 with automatic NVIDIA GPU detection and hardware monitoring, while OpenAI-compatible endpoints cover chat, images, and audio transcription. An embeddable chat widget streams responses from a single script tag with Shadow DOM isolation, and a WordPress plugin maps each capability to its own Restai project.
Behind the Verdict
Restai's pitch is breadth without glue code. Most self-hosted AI stacks are assembled from a RAG framework, an agent library, an eval harness, a prompt tracker, and whatever auth you bolt on top. Restai puts all five behind one REST API and one React admin UI, which is the real differentiator — not any single component. Strengths worth naming. The RAG project type is unusually complete: ChromaDB, PGVector, Weaviate, or Pinecone for storage, ColBERT and LLM-based reranking for retrieval quality, natural-language-to-SQL, smart-chunking analytics with recommendations, and scheduled auto-sync from web URLs, Amazon S3, Confluence, SharePoint, and Google Drive configured per project. That sync story is the piece most open-source RAG tools leave to you. The agent project type is zero-shot ReAct with MCP support over both HTTP/SSE and stdio, so any MCP-compatible server becomes a tool source without custom code. The Blockly IDE is a genuinely different idea: a visual pipeline builder that runs without an LLM in the loop, with a "Call Project" block letting you chain AI projects into deterministic logic. Prompt versioning that auto-saves every system prompt change and links eval runs to prompt versions for A/B comparison is the sort of feature you usually only get in a paid observability product. Enterprise controls are more serious than the average open-source AI project: multi-tenant teams, RBAC, OAuth/LDAP/OIDC SSO, TOTP 2FA with admin enforcement, input and output guardrails, audit logging for all mutations, per-project rate limits, and budget caps. White-label branding per team makes the agency use case real. The embeddable widget with Shadow DOM isolation and project-scoped read-only API keys, plus a WordPress plugin that auto-provisions starter projects across Gutenberg content, Yoast/Rank Math SEO meta, Polylang/WPML translation, WooCommerce descriptions, and AI site search, are concrete distribution wins rather than checkbox integrations. Where it bites. Everything is yours to operate: Postgres, the vector store, upgrades, and backups. Local image generation via Stable Diffusion, Flux, or RMBG2 depends on an NVIDIA GPU the platform auto-detects — without one you fall back to remote generators. The evaluation framework covers three DeepEval metrics — Answer Relevancy, Faithfulness, Correctness — which catches regressions but is not a dedicated ML observability platform. Wiring MCP servers and configuring per-project auto-sync takes real setup time, and five project types means a learning curve before you are productive. The breadth that makes Restai a bargain for a platform engineer is also the reason a single-purpose tool will feel lighter for someone who only wants a chatbot. Where it fits: regulated teams that cannot send documents to third-party APIs, agencies shipping white-labeled AI widgets, WordPress publishers who want AI content and site search behind their own instance, and platform engineers who want RAG, agents, evals, and RBAC
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Real-world workflow fit
Concrete scenarios for the personas Restai actually fits — and what changes day-one when you adopt it.
You install Restai via Docker, point it at PGVector and an internal vLLM endpoint, and create a RAG project synced on a schedule to a Confluence space and an S3 bucket.
Outcome: Documents stay on your infrastructure, the knowledge base refreshes without manual uploads, and the dashboard shows token, cost, and latency per project from day one.
You spin up a team per client, set custom logos, colors, and app names, then drop the embeddable chat widget script tag on each client site using a project-scoped read-only API key.
Outcome: Each client sees their own branded assistant, and Shadow DOM isolation means the widget does not conflict with host page styles.
You install the Restai WordPress plugin, which auto-provisions starter projects on first connect, then generate Gutenberg content, Yoast or Rank Math SEO meta, and translated drafts for Polylang or WPML.
Outcome: Each capability maps to its own Restai project, so prompts and budgets stay tunable per task without touching the site's theme code.
Use Cases
- Deploy a self-hosted RAG system with scheduled syncing from S3, Confluence, SharePoint, or Google Drive.
- Build agentic workflows with zero-shot ReAct agents wired to any MCP server over HTTP/SSE or stdio.
- Compose visual logic pipelines in the Blockly IDE with no LLM in the loop.
- Track token spend, cost, and latency per project on the analytics dashboard before regressions bite.
- Serve white-labeled AI chat widgets on client sites with per-team logos, colors, and app names.
- A/B compare prompt versions against Answer Relevancy, Faithfulness, and Correctness eval runs.
- Generate WordPress content, excerpts, and Yoast or Rank Math SEO meta from Gutenberg.
- Run Stable Diffusion, Flux, DALL-E, or RMBG2 image generation on a local NVIDIA GPU.
Models Under the Hood
as of 2026-10-18
Limitations
- Restai is self-hosted, so you run your own Postgres instance, choose and operate a vector store (ChromaDB, PGVector, Weaviate, or Pinecone), and manage your own updates and security.
- Image generation via Stable Diffusion, Flux, DALL-E, and RMBG2 depends on NVIDIA GPUs the platform auto-detects — without one you are limited to remote generators.
- The built-in evaluation framework covers three DeepEval metrics (Answer Relevancy, Faithfulness, Correctness), which catches regressions but is not a dedicated ML observability platform.
- Wiring MCP servers over HTTP/SSE or stdio and configuring per-project auto-sync takes real setup time, and the breadth of five project types means a learning curve before you are productive.
as of 2026-10-17
Verification history
We have re-verified Restai 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Restai tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Community
$0/mo
Ideal for
Teams that can host their own Postgres, vector store, and (optionally) GPU and want the full platform without a licence fee.
What this tier adds
Starting tier: Apache 2.0 with full source, all five project types, 11+ LLM providers, 5 vector stores, the eval framework, and MCP support.
Where the pricing makes sense
The company stage and team size where Restai's pricing actually pencils out — and where peers do it cheaper.
Restai publishes an Apache 2.0 community edition with full source on GitHub — all five project types, 11+ LLM providers, 5 vector stores, the eval framework, and MCP support — so the licence cost is zero at any team size. Your real comparison is inference spend. Point projects at Ollama or vLLM and the model bill stays on your own hardware; point them at OpenAI, Anthropic, Gemini, Azure, or Bedrock and those providers bill per token on top. Against managed AIaaS platforms that charge per seat
Setup time & first value
How long it actually takes to get something useful out of Restai — broken out by persona, not the marketing-page minute.
Docker or pip install gets the stack running in an afternoon if Postgres and a vector store are already available; plan a week before you are productive across all five project types, and budget extra time for MCP server wiring and per-project auto-sync credentials. Without an NVIDIA GPU, skip local image generation entirely.
Switching to or from Restai
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a hand-rolled LangChain RAG script: recreate the retrieval config as a RAG project with your existing vector store (ChromaDB, PGVector, Weaviate, or Pinecone) and re-point auto-sync at the same sources.
- →From a hosted chatbot widget: replace the embed with Restai's script tag and a project-scoped read-only API key to keep conversation context and streaming.
- →From a standalone eval notebook: move test datasets into the evaluation framework and let prompt versioning link runs to system prompt history.
- ↗To a managed AI platform: export your system prompts via prompt versioning history and rebuild RAG retrieval and MCP tool configs on the new service.
- ↗To single-purpose tools: split RAG, agents, and evals into dedicated products, accepting the glue code Restai was replacing.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Restai”, and we withheld 6: 6 could not be judged, because “Restai” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Restai.
Official links
Tools that pair well with Restai
Common stack mates teams adopt alongside Restai, with the specific reason each pairing earns its keep.
Langflow
Visual low-code canvas for building AI agents, RAG pipelines, and MCP servers, deployable as an API or self-hosted.
Dify
Dify is an open-source visual builder for agentic workflows and RAG pipelines, deployable on cloud, VPC, or self-hosted Docker.
Trag AI
Trag AI builds no-code RAG crypto trading agents that cite RSI, MACD, and news data behind every signal.
Featured Head-to-Head Comparisons
Restai vs Spider Cloud
Spider Cloud is ideal if your priority is high-performance web data extraction (crawling, scraping, search) for AI agents and RAG pipelines, with a scalable pay-as-you-go cloud API. Restai is better if you need a full self-hosted AI platform with RAG, agents, visual builder, and enterprise features like RBAC and white-labeling, but you must manage your own infrastructure. Choose based on whether you need data retrieval or a comprehensive AI back-end.
Restai vs Temporal Ai
For teams that need bulletproof durability for long-running AI agent workflows, Temporal AI is the clear choice. If you want a self-contained, multi-project AI platform with RAG, agents, and a visual builder—all self-hosted and free—Restai is the better pick. Your decision hinges on whether you prioritize fault-tolerant orchestration (Temporal) or a full AI service stack (Restai).
Restai vs Presto Voice
For QSR drive-thru automation, Presto Voice is purpose-built with proven results (e.g., Dairy Queen partnership, up to 95% non-intervention). For flexible, self-hosted AI services (RAG, agents, apps), Restai is a powerful open-source alternative at zero licensing cost. Choose based on your domain: drive-thru or general AI.
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