Lamatic.ai
Visual, low-code platform to build, deploy, and optimize agentic AI apps on serverless edge infrastructure.
Lamatic is a pragmatic pick for teams that want to ship agentic AI features quickly without deep ML expertise. The visual builder, serverless edge, and generous free tier are real differentiators. But be ready to commit to the enterprise plan for on-premise or heavy customization, and watch out for the 3,000-request limit on the Team plan—it's oddly lower than expected for that price. For code-first teams, n8n or LangChain might offer more flexibility; for a more managed experience, Dify is a closer competitor.
Verified 3d ago · liveness 78/100 · cite: rightaichoice.com/tools/lamatic-ai
- Startup founders wanting to ship AI features fast without deep ML expertise
- Agencies building AI automations for clients with no-code ease
- Enterprise teams needing a secure, scalable AI middleware layer
- Non-technical builders who prefer visual, low-code workflows
- Teams needing on-premise deployment without an enterprise plan
- Projects requiring custom model training or fine-tuning
- Users seeking a purely code-first, infrastructure-agnostic framework
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Skip Lamatic if you need on-premise deployment without an enterprise contract, require custom model training, or prefer a fully code-first framework with no vendor lock-in.
Additional integrations cost $20 per integration beyond your plan's limit.
Lamatic's freemium pricing (free tier with 3,000 requests/month) is generous for experimenting, but Pro at $99/mo gives 100k requests—pricier than n8n's self-hosted option but cheaper than some managed AI platforms. Team at $149/mo with only 3k requests is a poor value; skip to Pro. Enterprise is custom-priced, similar to competitors like Dify.
In short
Lamatic.ai — Visual, low-code platform to build, deploy, and optimize agentic AI apps on serverless edge infrastructure. Best for Startup founders wanting to ship AI features fast without deep ML expertise, Agencies building AI automations for clients with no-code ease, Enterprise teams needing a secure, scalable AI middleware layer. Free to start; paid plans from $99/mo.
What's new in Lamatic.ai
Checked 3 days agoAcross the latest 3 updates: 3 feature updates.
Introducing Lamatic 3
Lamatic 3 reimagines the developer experience with a new Builder interface and enhanced Agent nodes, making it easier to build and deploy reliable AI agents.
AgentKit Templates and GitHub VCS Integration
Added AgentKit templates for quick start and native GitHub integration for version control, allowing teams to manage flows as code.
AI Agent Evaluation Framework
Introduced a framework for reproducible testing and evaluation of AI agents, helping teams validate performance before deployment.
What people actually say about Lamatic.ai — 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.
18 mentions across 3 sources (Hacker News, Product Hunt, Bluesky) · researched Jul 4, 2026.
- +Visual flow builder makes AI agent creation accessible to non-coders.
- +Edge deployment reduces latency for real-time applications.
- +Built-in vector database simplifies RAG implementation without separate setup.
- +Pre-built workflows and templates accelerate prototyping significantly.
- +Free tier offers 3,000 requests/month for low-cost experimentation.
- −Almost no independent community feedback to validate claims.
- −Code-first teams may find visual builder too restrictive.
- −Pricing jumps from free to $99/mo, which may surprise early users.
- −No on-premise deployment option; cloud-only could be a dealbreaker.
- −Lack of detailed technical documentation or benchmarks online.
- • Overages beyond free tier may be expensive if usage spikes
- • Custom domain add-on likely costs extra
- • Enterprise pricing is opaque and requires sales call
Viability Score
How well maintained and how widely used is Lamatic.ai? 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: September 2026
How we score →Key Features
- Visual flow builder with drag-and-drop nodes
- Serverless edge deployment with automatic scaling
- Built-in vector database for RAG
- Agent nodes: Text, JSON, Multi-Modal, Supervisor
- 100+ text, image, and embedding models
- Memory and context stores (external, stateful)
- GitHub VCS integration for version control
- AgentKit templates for quick start
- AI agent evaluation framework
- Real-time tracing, logs, alerts, dashboards
- Jobs and scheduling for recurring runs
- GraphQL API, Webhooks, Widgets, SDKs (JS, Python, Go, CLI)
- MCP (Model Context Protocol) node and server
- Test case assistant and prompt IDE
- Request caching and retries/failovers
About Lamatic.ai
Lamatic is a fully managed, low-code middleware platform that lets teams build, deploy, and optimize agentic AI apps without deep ML expertise. It combines a visual node-based flow builder, a built-in vector database, and integrations with 100+ models, apps, and data sources. Designed for cross-functional teams—from startup founders to enterprise agencies—it helps you ship AI features quickly and iterate reliably. The platform supports both no-code builders and code-first developers through SDKs, APIs, and a CLI, fitting into existing engineering workflows. With Lamatic, you chain AI generation, data retrieval, logic, and integration nodes into workflows called Flows. Agent nodes (Text, JSON, Multi-Modal, Supervisor) reason and orchestrate multi-step tasks. The latest Lamatic 3 release (announced January 2026) reimagined the Builder interface and enhanced Agent nodes for more reliable deployment. Deployments are serverless and run on the edge, automatically scaling and cutting latency in half. Real-time tracing, logs, alerts, and dashboards give full visibility into every request. Key capabilities include a prompt IDE, test case assistant, request caching, retries/failovers, and support for GraphQL API, Webhooks, and Widgets. The platform includes a built-in vector database for RAG, memory and context stores, and pre-built templates like RAG chatbot, Google Drive indexation, and Slack Bot. Recent additions include AgentKit templates and GitHub VCS integration for managing flows as code, plus an AI agent evaluation framework for reproducible testing before deployment. Lamatic offers a free Starter tier with 3,000 requests per month, paid Pro and Team plans, and a custom Enterprise plan. Compared to alternatives like n8n, Dify, or LangChain, Lamatic is more visual and opinionated, making it a fit for teams that want to move fast without building AI infrastructure from scratch.
Behind the Verdict
Lamatic positions itself as an AI middleware that bridges the gap between no-code and pro-code. Its visual flow builder is genuinely approachable, allowing non-technical team members to design complex workflows with drag-and-drop nodes. The emphasis on serverless edge deployment with automatic scaling is a strong selling point, especially for startups expecting variable traffic. The built-in vector database and memory stores make RAG and stateful agents straightforward to build without third-party dependencies. We appreciate the breadth of integrations—100+ models, apps, and data sources—and the flexibility of multiple deployment methods: GraphQL API, Webhooks, Widgets, and SDKs for JS, Python, Go, and CLI. The GraphQL IDE and test case assistant lower the barrier for non-developers, while the code-first crowd gets raw SDK access. Weaknesses: The pricing structure has some oddities. The Team plan at $149/month offers only 3,000 requests/month, same as the free tier, which seems like a copy error and could frustrate customers who upgrade expecting more. Also, on-premise deployment is locked behind Enterprise, and the free tier's 3-day log retention is short for debugging. The platform is opinionated—if you need a fully custom, infrastructure-agnostic framework, you might feel constrained. Lamatic fits best for teams that prioritize speed over fine-grained control, like agencies building client automations or startups validating AI features. It's less ideal for teams needing deep customization or strict data residency without an enterprise contract.
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Real-world workflow fit
Concrete scenarios for the personas Lamatic.ai actually fits — and what changes day-one when you adopt it.
You need to launch an AI chatbot that answers questions from your docs quickly.
Outcome: In a day, you create a flow using the RAG chatbot template, connect your Google Drive, and deploy it via a widget on your site—no backend code needed.
You're building an invoice automation tool for a client.
Outcome: You use the visual builder to chain a file extractor, a text generation node, and a Google Sheets integration, then schedule it to run nightly—all without manual server setup.
You need to integrate AI into an existing SaaS product.
Outcome: You use the Python SDK to call a flow from your backend, pass user context, and get responses in under a second—with full tracing and analytics in the dashboard.
Use Cases
- Build a customer support chatbot that scrapes your knowledge base and answers via RAG.
- Automate invoice processing by extracting data from emails and Google Drive.
- Deploy a personalized content recommendation engine using user memory and vector search.
- Create a multi-agent system that routes customer queries to the right AI specialist.
- Schedule daily reports that summarize database changes and send them via Slack webhook.
- Integrate Lamatic flows into existing apps via GraphQL API to add AI features without rewriting.
Models Under the Hood
as of 2026-08-28
Limitations
- The free Starter plan caps requests at 3,000/month, logs at 3 days, and limits integrations to 5.
- The Team plan at $149/month appears to have similar limits to the free plan, possibly a copy error on the pricing page.
- Pro and above are needed for 30-day log retention, scheduling, and guest users.
- On-premise and enterprise features are locked behind the Enterprise tier.
- Overage charges apply for extra requests, integrations, records, and custom domains.
as of 2026-08-31
Verification history
We have re-verified Lamatic.ai 6 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-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-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
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 Lamatic.ai tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Starter
$0/mo
Ideal for
Individuals or small teams prototyping an AI feature—up to 3,000 requests/month and 5 flows, enough for a demo or internal tool.
What this tier adds
Free entry point with 3,000 requests, 3 team members, 5 flows, and basic features like visual builder and GraphQL API.
Pro
$99/mo
Ideal for
Growing startups moving an AI MVP to production with 100k requests/month, needing 30-day logs and scheduling.
What this tier adds
Adds 100,000 requests, unlimited flows, 10 integrations, 30-day logs, custom branding, guest users, reports, and chat support.
Team
$149/mo
Ideal for
Teams with multiple members that need unlimited team seats and integrations—but note the low 3,000 request cap (likely a copy error), so assess actual usage.
What this tier adds
Adds unlimited team members and unlimited integrations compared to Pro, but keeps only 3,000 requests/month.
Enterprise
Custom
Ideal for
Large organizations requiring on-premise deployment, compliance, SSO, and dedicated support.
What this tier adds
Adds unlimited requests, on-prem, SLAs, multi-tenant API, and dedicated support, all custom-priced.
Where the pricing makes sense
The company stage and team size where Lamatic.ai's pricing actually pencils out — and where peers do it cheaper.
Lamatic's freemium pricing (free tier with 3,000 requests/month) is generous for experimenting, but Pro at $99/mo gives 100k requests—pricier than n8n's self-hosted option but cheaper than some managed AI platforms. Team at $149/mo with only 3k requests is a poor value; skip to Pro. Enterprise is custom-priced, similar to competitors like Dify.
Setup time & first value
How long it actually takes to get something useful out of Lamatic.ai — broken out by persona, not the marketing-page minute.
For a non-technical founder: 30 minutes to sign up, pick a template, and test a basic flow. For a developer using the SDK: less than 15 minutes to integrate an existing flow into a React or Python app. Advanced custom flows with multiple nodes and custom data sources may take a few hours.
Switching to or from Lamatic.ai
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From n8n: Recreate your automation as a Lamatic flow using visual nodes; use pre-built integrations for common triggers. Expect a learning curve but faster edge deployment.
- →From Dify: Export your workflow definitions and manually recreate them in Lamatic's builder; Lamatic offers more flexible memory and MCP support.
- ↗To n8n: Export your flow logic and rebuild using n8n's node system; be prepared for more manual infrastructure management.
- ↗To a custom solution: Use Lamatic's SDKs and API to retrieve flow definitions, then reimplement in your own stack.
Integrations
Resources & Guides
- Documentationlamatic.ai
Docs · Lamatic.ai
Full product docs from lamatic.ai
- Documentationlamatic.ai
Flows · Lamatic.ai
Full product docs from lamatic.ai
- Documentationlamatic.ai
Nodes · Lamatic.ai
Full product docs from lamatic.ai
- Documentationlamatic.ai
Agents · Lamatic.ai
Full product docs from lamatic.ai
- Documentationlamatic.ai
Deployments · Lamatic.ai
Full product docs from lamatic.ai
- Documentationlamatic.ai
Integrations · Lamatic.ai
Full product docs from lamatic.ai
- API Referencelamatic.ai
Sdk · Lamatic.ai
Methods, params, types from lamatic.ai
- API Referencelamatic.ai
Api · Lamatic.ai
Methods, params, types from lamatic.ai
- Documentationlamatic.ai
Mcp · Lamatic.ai
Full product docs from lamatic.ai
Tutorials & Learning
Official links
Tools that pair well with Lamatic.ai
Common stack mates teams adopt alongside Lamatic.ai, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Lamatic Ai vs Spider Cloud
If you need to rapidly build and deploy AI agents with a visual builder, serverless infrastructure, and integrated vector database, choose Lamatic. But if your primary need is high-performance web crawling and scraping for feeding data into AI models or RAG pipelines, Spider Cloud's Rust engine, low cost per page, and open-source core make it the clear winner.
Lamatic Ai vs Temporal Ai
Choose Lamatic if you need a visual, low-code platform to quickly prototype and deploy AI apps with built-in vector DB and model integrations—ideal for non-technical builders. Choose Temporal if you require bulletproof durable execution, automatic retries, and SDK-based workflow orchestration for mission-critical AI agents and microservices. Temporal's open-source nature and enterprise adoption (OpenAI, Replit) make it superior for reliability at scale, while Lamatic excels in speed and simplicity for AI application development.
Lamatic Ai vs Presto Voice
Lamatic.ai and Presto Voice are not direct competitors. Lamatic is a general-purpose AI PaaS for building custom agentic workflows, ideal for startups and agencies that need flexible, low-code tooling. Presto Voice is a specialized drive-thru voice AI solution for QSR chains, focused on order accuracy and upselling. Choose Lamatic if you need to build diverse AI applications; choose Presto Voice if you run a drive-thru chain and want proven, high-automation voice ordering.
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