Self-hosted AI agents in 2026: 10 you can run on your own servers, and what they send home

We checked the 34 tools named on those pages, and on one list of on-premises platforms, against five rules a tool could fail. Ten passed. Where to start:

• Your operations team wants to build agents on a canvas: n8n or Dify. Both install on your own servers, both connect to a model you run through Ollama, and both can stop an agent until a person approves. n8n's licence limits it to your own internal business use, and Dify's free edition runs one workspace.
• Your developers will write the agent: LangGraph. It's an MIT-licensed library that runs inside your own code and can pause a run until a person resumes it.
• You already run UiPath, IBM or Automation Anywhere: that vendor's on-premises edition. All three now run agents on your own infrastructure with a documented approval step. None of them publishes an on-premises price.

Meronym builds agents for clients, sometimes on n8n and UiPath, and sells none of the tools in this list. We have no referral, reseller or partner deal with the companies that make them. Our own custom build is the last entry, labelled, with its drawback. We read each tool's documentation, licence and pricing page on 27 and 29 September 2026; we didn't install them.

What "self-hosted" covers for an agent, and why it isn't the same as private

An agent has three parts, and each one can sit inside your network or outside it.

The pages ranking for this search blur the first two rows. openclaw-ai.net's self-hosting guide rates the privacy of every tool in its table "100% local", while its own setup offers "Cloud APIs (easiest): Claude (Anthropic), GPT (OpenAI), Gemini (Google), DeepSeek". Fastio's platform list says in its FAQ that "Many teams self-host the agent logic but call external APIs for inference to avoid GPU costs." Only OpenHands' list says it outright: "Self-hosting the agent does not automatically mean prompts, code context, or logs never leave the environment."

So a private AI agent needs all three parts inside your control. The software runs on your infrastructure, the model runs where your data is allowed to go, and nothing else leaves without your say-so. Self-hosting covers the first part only.

When another guide is a better fit

• Developers who want a coding agent in their repository. OpenHands' list of seven open-source coding agents is the better page; coding agents do a different job from the business agents here.
• Someone after a personal assistant on a laptop. The r/opensource thread that tops this search starts from one: Observer AI, a screen-watching agent its developer runs on Ollama. This list is for a business's servers.
• A business whose data may go to a vendor's cloud. A hosted agent is less work to run. Our lists of AI agents for business and enterprise AI agents cover them, with each vendor's data terms.

How we chose the ten

We started from every product that one of these pages gives an entry of its own, meaning its own heading or table row in a list:

• the four lists on the first page for this search, from OpenHands, Fastio, openclaw-ai.net and ClawTank;
• VDF AI's list of 15 on-premises platforms, the one list we could read from a search for on-premises agent platforms (SourceForge's category page blocked our browser, and Shakudo's page is a guide to its own product);
• the three forum threads on the page, each about its poster's own product.

That makes 34 products. Two pages name tools without giving them an entry, and those names aren't counted. haimaker's guide, also on the first page, is a setup guide rather than a list: Ollama and OpenClaw appear in its setup steps, and both are counted from the lists. VDF's fifteenth entry is a stack of open-source parts rather than a product; its text names OpenClaw, which ClawTank's list gives an entry, beside Nous Research's Hermes Agent and NVIDIA's NemoClaw, which no page does.

A tool stays on the list only if it passes all five rules:

1. It runs agents that act. Its own pages sell building or running agents that choose and call tools. Model runners, chat apps, coding agents and storage are out, and so is NVIDIA AI Enterprise: its reason is with the drops below, and NIM, the serving software that comes with it, is in the model section with the runners.
2. Someone other than its maker lists it. A page other than its maker's gives it an entry. openclaw-ai.net is OpenClaw's community site, so it counts for the other tools on its list but not for OpenClaw.
3. You can run all of it yourself, today. The maker documents installing the part that runs agents on your own servers or cloud account, the licence or plan allows internal business use, it's released for production rather than as a preview or beta, and it had a release between 29 March and 29 September 2026. The production test applies to the part that runs agents and to its approval step, so a preview elsewhere in a product doesn't fail it: n8n's newer agents section is a preview, and its AI Agent node isn't. Each entry below names the release that meets the date test.
4. The model can stay inside. The maker documents connecting a model you host, through a local runner such as Ollama or your own endpoint.
5. A person can step in. The maker documents a named step where the agent, or the workflow it runs in, stops and waits for a person. These don't count: a review before the agent ships, monitoring afterwards, and "human in the loop" that doesn't say where the agent waits.

Ten passed and 24 didn't. Each drop below is listed with a rule it fails, not with every rule it fails.

Where the drops' reasons come from:

• AutoGPT's repository tags its latest release autogpt-platform-beta-v0.8.1.
• AutoGen's README says it "is now in maintenance mode" and points new users to Microsoft Agent Framework.
• Red Hat's OGX guide says "Red Hat does not recommend using them in production" of Technology Preview features, OGX included.
• Appian's self-managed AI page lists no AI agents among its features.
• LocalAGI's README describes no approval step.
• DataRobot's platform overview names "approval workflows and versioning before an agent ships".
• C3 AI's agentic process automation page offers "optional human review".
• For DataRobot and C3 AI we read the overview and product pages, not every page of their documentation; a named step documented deeper in would change their result.
• NVIDIA AI Enterprise is a subscription sold per GPU for serving, tuning and guarding models. NVIDIA's AI Enterprise page says its tools "provide support for each step" of training and deploying agents, and VDF's entry calls NVIDIA "the infrastructure and acceleration layer underneath many enterprise AI systems". Its agent library, the NeMo Agent Toolkit, works "side-by-side with agentic frameworks" such as LangChain and CrewAI, and none of these pages gives it an entry, so it isn't among the 34.

We read Automation Anywhere's documentation through the Internet Archive's copies from April, May and June 2026, because its docs site doesn't render in our browser. Flowise's pricing page redirects to a sign-in page, so no Flowise price appears here.

What to check before you pick a self-hosted AI agent

These are the questions to put to any tool you run yourself, including ones not on this list.

• What it sends home. n8n's docs say it plainly: "By default, a self-hosted n8n instance sends data to n8n's servers." Ask for the full list and the setting that stops each item; the table further down has them for the seven tools here whose code is public, and marks the three vendors that don't say.
• What the licence lets you do. n8n's licence covers "your own internal business purposes", and Dify's forbids running a multi-tenant service without its written permission. If you'll offer the agent to your own customers, check this before anything else.
• Where the model runs. Every tool here can call a cloud API. The setup is only private once it points at a model you run, or at a provider's service in your own cloud account whose terms you've read.
• Where a person approves, and what happens when no response arrives. Dify's approval step times out after "3 days" by default and then takes a timeout branch, or ends the run if there isn't one. Ask what the agent does while it waits.
• Whether users are kept apart. Langflow doesn't isolate users within one process, and its code editor runs arbitrary Python with full access to the host. OpenClaw's docs say "One trust boundary per gateway." Plan one install per team that shouldn't see the other's data.
• Who keeps it maintained. Look for a release in the last six months. AutoGen's README says it "is now in maintenance mode".
• What the paid tier adds. CrewAI's approvals for production are "Webhook-based (Enterprise)", and Automation Anywhere's approval task needs its Enterprise Platform licence.
• What the hardware must hold. OpenAI's gpt-oss-20b runs "within 16GB of memory"; gpt-oss-120b needs "a single 80GB GPU".
• Which licence checks need the internet. LangGraph's standalone server needs "Egress to https://beacon.langchain.com from your network" unless it runs in air-gapped mode, and n8n's paid licence keys have to reach n8n's licence server every day, carrying "data like the number of production executions".

The ten self-hosted AI agents, by kind of tool

The ten fall into four kinds, and the kind decides more than the product: who builds the agent, what you install, and where the approval sits. Within each kind the tools aren't ranked. Every entry has the same fields in the same order, and our own build comes last.

Visual builders you run on your own servers

For an operations team that wants to draw an agent's steps rather than code them. All four connect to a model you run through Ollama.

n8n: best for an operations team that already automates workflows and wants an agent step inside them

What it is and what you run. A workflow automation tool with an AI Agent node. n8n's hosting docs say "You can self-host n8n on your own infrastructure, on-premises, or in a private cloud, using Docker Compose, one-line setup, or other deployment methods." Its latest release, 2.41.3, came out on 25 September 2026. Its newer "Build and manage agents" section is still marked Preview, and n8n's agents docs say those agents "aren't ready for self-hosted Enterprise yet"; the approval step below belongs to the AI Agent node inside a workflow.

Licence and price. n8n's licence says "You may use or modify the software only for your own internal business purposes or for non-commercial or personal use", and "we do not call ourselves open source". The self-hosted Community Edition is free, according to n8n's hosting documentation. The self-hosted Business plan was "$800 /mo, billed annually" for 40,000 executions on n8n's pricing page on 27 September 2026, in US dollars; other reads of the page have shown this plan's price in euros. Enterprise is priced on request.

Where the model runs. Its Ollama Chat Model node connects the agent to a model you run yourself.

Where a person approves. "You can require human approval before an AI Agent executes a specific tool," say n8n's docs. The reviewer sees "which tool the AI wants to use and with what parameters", in Slack, Telegram or n8n's own chat. A denial stops the action and tells the agent it was refused.

What it sends home. Update notices, workflow templates and diagnostics until three settings turn them off, and on paid plans a daily licence check that carries usage counts (the table below has both).

For the parts n8n's own nodes can't reach, see our n8n automation agency page.

Dify: best for a team building several AI apps on one platform, with approvals that go out by email

What it is and what you run. "Dify is an open-source LLM app development platform," says its README. It runs with Docker Compose, and its install guide asks for "CPU >= 2 Core" and "RAM >= 4 GiB". Its latest release, 1.17.1, came out on 10 September 2026.

Licence and price. Dify's licence is a modified Apache 2.0, which allows commercial use with two conditions:

• no multi-tenant service without Dify's written permission, where a tenant is a workspace: "you may not use the Dify source code to operate a multi-tenant environment";
• the logo stays: "you may not remove or modify the LOGO or copyright information in the Dify console or applications".

Its pricing page aims the free Community edition at "open-source enthusiasts, individual developers, and non-commercial projects", with a "Single Workspace". Enterprise, with a commercial licence and multiple workspaces, is "Custom". Fastio's list calls Dify's self-hosting "free without restrictions"; the licence says otherwise.

Where the model runs. Dify's official Ollama plugin: "Ollama runs open models locally on macOS, Windows, and Linux".

Where a person approves. Dify's Human Input node pauses a workflow and sends a request form by web app or email, where "Anyone with the link can respond, no Dify account required". The request closes at the first answer. If no response arrives before the timeout ("The default is 3 days"), the workflow follows the timeout branch, and "If no timeout branch is connected, the workflow ends."

What it sends home. Its default Docker settings check updates.dify.ai, use Dify's plugin marketplace, and leave the telemetry of the bundled vector database on (the table below has the settings).

Flowise: best for building and testing an agent visually, with approval on its tools

What it is and what you run. A visual builder whose README sums it up as "Build AI Agents, Visually", installed with npm install -g flowise or Docker Compose. Its latest release, 3.1.4, came out on 29 July 2026. Workday announced on 14 August 2025 that it "has acquired Flowise", and its chief technology officer spoke of "investing in its open-source foundation".

Licence and price. Apache 2.0, except the enterprise folder and files with their own copyright notice, which fall under a commercial licence. Flowise's pricing page redirected us to a sign-in page, in our browser and in the Internet Archive's copy of 2 September 2026, so no price is public.

Where the model runs. Its ChatOllama node connects to a model you run.

Where a person approves. "The Human Input node allows execution to be paused and only resumed after a human has provided feedback to either approve or reject the action," say Flowise's docs. You can also switch on "Require Human Input" for an agent's tools.

What it sends home. Anonymous telemetry, on until you set DISABLE_FLOWISE_TELEMETRY to true; the line sits commented out in its Docker settings file. Flowise's Spanish telemetry page says it never collects node inputs, outputs, messages or credentials. The English telemetry page no longer opens, and the English list of settings leaves the switch out.

Langflow: best for developers who want a visual canvas with Python underneath

What it is and what you run. A visual builder for flows and agents with a Python code editor built in. It installs with uv pip install langflow -U or as a Docker container, per its README. Its latest release, 1.12.4, came out on 29 September 2026.

Licence and price. MIT, and free.

Where the model runs. "Basic Ollama support is available in all Langflow installations through the core Language Model component," say Langflow's docs.

Where a person approves. "Human-in-the-Loop (HITL) pauses a flow, creates a checkpoint, and waits for a human decision," per the docs. It works through the Human Input component, or by setting the Agent component to require approval for its tools. The feature arrived in version 1.11.

What it sends home. Anonymous telemetry on feature usage and performance; DO_NOT_TRACK=True stops it (docs).

Langflow's security page is the source for both drawbacks: the editor has "full access to the host Langflow backend process, filesystem, and network", and "Langflow neither enforces isolation between users within a single Langflow process, nor restricts access to the local disk or network resources."

Frameworks your developers code against

For teams that will write the agent in Python and want it inside their own application. Both are MIT-licensed libraries, and both work with a model served by Ollama.

LangGraph: best for developers who want to control every step of the agent in code

What it is and what you run. A library from LangChain for building agents as graphs of steps, installed with pip install -U langgraph and run inside your own code. The library's latest release, 1.2.12, came out on 21 September 2026. LangChain also offers a standalone Agent Server that runs "using Docker, Docker Compose, or Kubernetes without the LangSmith control plane", with PostgreSQL and Redis.

Licence and price. The library is MIT and free. The standalone server needs a licence key from LangChain, whose price isn't on the pages we read.

Where the model runs. Any model LangChain connects to, including Ollama, which LangChain's docs say "allows you to run open-weight large language models (LLMs), such as gpt-oss, locally."

Where a person approves. "Interrupts allow you to pause graph execution at specific points and wait for external input before continuing," say LangGraph's docs. The graph's state is saved, and it "waits indefinitely until you resume execution."

What it sends home. Nothing is stated for the library on the pages we read. The standalone server needs "Egress to https://beacon.langchain.com from your network" for licence checks and usage reporting, unless it runs in air-gapped mode.

CrewAI: best for developers building several agents that share one job

What it is and what you run. "CrewAI is an open-source Python framework with high-level abstractions and low-level APIs for building production-ready multi-agent workflows," says its README. It installs with uv tool install crewai and runs in your own code; the latest release, 1.15.23, came out on 28 September 2026.

Licence and price. The framework is MIT and free. The paid Enterprise plan is "Custom" priced on CrewAI's pricing page, which says you can "Deploy on CrewAI cloud, your own VPC, or your own infrastructure".

Where the model runs. CrewAI's docs have a section on "Using Local Models with Ollama".

Where a person approves. Two ways, per its docs. A @human_feedback decorator in flows is for "Local development, console-based review, synchronous workflows". For "Production deployments", approval is "Webhook-based (Enterprise)".

What it sends home. "CrewAI uses anonymous telemetry to collect usage data," says the README's telemetry section, and it's on by default. Setting CREWAI_DISABLE_TELEMETRY to true turns it off, and per the telemetry docs a value it doesn't recognise leaves telemetry on.

An assistant that acts on its own

OpenClaw: best for a team assistant that works through your chat apps and the tools on its host

What it is and what you run. An open-source assistant you run on your own hardware, which acts through the chat platforms you connect and runs commands on its host. Its README says "State, memory, and credentials live on your hardware." It installs with a script for macOS, Linux and Windows, and released v2026.9.6 on 23 September 2026.

Licence and price. MIT, from the OpenClaw Foundation. It "has no paid tier, hosted service, or token".

Where the model runs. OpenClaw's local models docs say it "can install and manage a local model or connect to a server you already run", with LM Studio and Ollama as options, and warn that "Local models do not provide hosted providers' safety filters." The README's other line still applies: "Your prompts go to the model provider and chat platforms you configure".

Where a person approves. On its host, "Commands run only when policy + allowlist + (optional) user approval all agree," per OpenClaw's approval docs. Its advanced approval docs put approval buttons in Discord, Slack and Telegram; on Microsoft Teams, a person approves by typing /approve in the same chat.

What it sends home. A daily update check, and nothing else by default. The README says "by default OpenClaw itself phones home for nothing but a daily version check, anonymous feature statistics are opt-in, and update.checkOnStart: false disables both". Its telemetry docs list what the check carries, and the table below has it.

Both drawbacks are OpenClaw's own words. The README says "Tools run on the host for the main session unless you configure sandboxing." The security docs add that "OpenClaw is not a hostile multi-tenant security boundary for mutually adversarial users sharing one agent or gateway."

Automation platforms that install on premises

For companies that already run one of these for robots or workflows and now want agents inside the same walls. None of the three publishes an on-premises price, and none says on the pages we read what it sends home.

UiPath Automation Suite: best for companies already running UiPath robots on their own Kubernetes

What it is and what you run. UiPath's announcement of 20 May 2026 says that with "the April 2026 release of UiPath Automation Suite", "The full UiPath agentic automation stack ... now available on-premises, on the Kubernetes infrastructure customers already operate on: AKS, EKS, and OpenShift."

Licence and price. Standard and Enterprise are "Contact Sales for pricing" on UiPath's pricing page, which lists Standard as "Hosted in one of our available regions or on your own premise".

Where the model runs. "In self-hosted mode, the models themselves stay inside as well." UiPath recommends "self-hosted open-source models (GPT-OSS, GLM, Qwen), or bring your own".

Where a person approves. A Maestro user task: "The process execution pauses at this step until an assignee has completed the task." The user task docs exist for Automation Suite as well as the cloud.

What it sends home. Not stated on the pages we read. Ask UiPath for the list of outbound calls an on-premises install makes.

For the agent side of a UiPath estate, see our UiPath consultant page.

IBM watsonx Orchestrate: best for companies on Red Hat OpenShift that want IBM's agent tooling inside

What it is and what you run. IBM's agent platform, installed on IBM Software Hub or Cloud Pak for Data. IBM's install page describes Software Hub as running "on Red Hat OpenShift". The latest on-premises release is 5.4.0 Patch 5, from 20 August 2026, per the release notes.

Licence and price. No on-premises price is published. The cloud plans on IBM's pricing page start at $567 a month (Essentials).

Where the model runs. Any model registered as a "virtual model", including through Ollama or Red Hat OpenShift AI. And "If you self-host an LLM or use a model proxy or gateway as a pass-through to another provider, you can use OpenAI chat completion compatibility," per IBM's model docs. A fresh on-premises install has no default model, and creating an agent before you set one fails "with no additional diagnostic information".

Where a person approves. Agentic workflows are the tool when "Human-in-the-loop interaction is needed for approvals or decision-making," say IBM's developer docs, and they can run for "minutes, hours, or even days". A user activity step collects a form from the people you name, such as approvers.

What it sends home. Not stated on the pages we read.

IBM's feature parity page, updated 29 September 2026, marks most on-premises features "Partial support", agentic workflows included, and gives AgentOps and "Governing assets with AI Gateway" "No support".

Automation Anywhere AI Agent Studio: best for companies already running Automation 360 on premises

What it is and what you run. The AI agent and AI skills part of Automation 360. Its release notes for v.34 mark features for "Cloud and On-Premises", and those for v.39, updated 6 April 2026 and the newest we could read, say a new capability "is supported in Task Bots for both Cloud and On-Premises environments". We read these through the Internet Archive.

Licence and price. "Access to AI Agent Studio is included with the Enterprise Platform that enables AI Agents," with usage drawn from "Automation AI credits", per the AI Agent Studio FAQ. No price is published.

Where the model runs. The FAQ asks whether it connects to "models hosted on private, customer infrastructure" and answers: "Yes, you can define and connect to your own custom models."

Where a person approves. "You can configure an approval task to share requests with decision makers before the process automation is completed," per the approval task docs. You set the number of approvals required, when the task expires, and the labels on its approve and decline buttons. The task sits at the end of a process automation, and its user configurations page shows it waiting, as "Waiting Approval (1/2)" for example, until it's "Approved" or "Declined". What a decline does next is for whoever builds the process: the page advises that "if the approver rejects, it could loop back to the requestor to adjust the data and resubmit". The approval task needs the Enterprise Platform licence, on premises too.

What it sends home. Not stated on the pages we read.

The first drawback comes from the custom model API page, updated 10 April 2026: the call that redefines a model "updates only custom models that have no associated Model connections", and a second call changes the name and description.

Our custom build

Meronym's custom agent: best for a business that wants the agent built into its own systems, with the approvals and what leaves agreed before the build

This entry is ours.

What it is and what you run. We build the agent for you, "in your cloud tenancy by default, in the region you choose, and on-premises where the data cannot leave your network", as our enterprise AI development services page puts it. A first agent runs in its narrow version four to six weeks after the two-week scope.

Licence and price. A build, not a download. Our scope takes two weeks, costs $4k–$12k credited against the build, and ends in a fixed quote, as our AI agent development services page sets out. One agent in your cloud tenancy is $8k–$35k as a pilot and $16k–$82k in production; on-premises work is quoted apart.

Where the model runs. The enterprise AI page adds that models run "through the provider's service in your account or as open-weight models you host", and that the provider agreement's retention and training terms are checked before any of your data is sent. For the open-weight ones, the AI agent development services page names Hugging Face, vLLM and Ollama.

Where a person approves. Written rules for "what the agent may do alone, what needs a person, and what it must refuse outright, agreed with your risk owner during scope", with approval gates, a queue, a kill switch and a rollback path, as the AI agent development services page sets out.

What it sends home. The architecture records every external model, telemetry and logging destination before the build. This guide does not claim one universal configuration for every client environment.

What it has done. This guide does not cite a published client result for our entry.

The ten side by side

What each self-hosted AI agent sends home, and how to stop it

A self-hosted agent that calls home isn't private until the calls are switched off. Hand this table to whoever installs the tool: it gives each one's default, from its own docs or config file, and the setting that stops it.

Where a vendor's docs say nothing, ask for the list of hosts an on-premises install calls, block everything else at your firewall, and read the firewall's log in the first week. Six of the seven tools here whose code is public send something home by default, so assume the three vendors' editions do too until they tell you otherwise.

Where the model runs, and what a private one costs

Every tool above can call a cloud API, a provider's service in your own cloud account, or a model on your own hardware. Only the last keeps prompts from leaving, and it's the one you pay for by the hour whether or not it's busy.

Running it yourself takes two things.

• A runtime to serve the model. Ollama and LocalAI are MIT-licensed and vLLM is Apache 2.0, all free. NVIDIA's own serving software, NIM, comes with NVIDIA AI Enterprise, which NVIDIA's price list puts at "$4,500 / GPU" for a year's subscription, or "$1 / hour / GPU + CSP Instance Cost(s)" on the cloud marketplaces.
• An open-weight model and a GPU that holds it. OpenAI's gpt-oss models are Apache 2.0, and their model cards say quantisation makes "gpt-oss-120b run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the gpt-oss-20b model run within 16GB of memory" (120b, 20b).

One NVIDIA H100 with 80 GB, rented on demand from Lambda, costs $3.29 an hour before tax: $2,401.70 a month if it runs all the time. The table sets that against the API bill for the same work: 10,000 support tickets a month, each the worked ticket from our AI agent cost guide, with nothing cached.

That ticket takes four model calls: three that each call a tool, and one that writes the reply. Every call re-sends 1,500 tokens of instructions, 1,000 tokens describing four tools and the customer's 500-token message, plus everything so far. Each tool call is 200 output tokens and brings back a 500-token result, and the reply is 400 tokens. That comes to 16,200 input tokens and 1,000 output tokens a ticket. Anthropic adds its own tool-use prompt to every call, 354 tokens on Claude Sonnet 5 and 496 on Claude Haiku 4.5, so their tickets are larger; OpenAI and Google publish no equivalent figure, so their rows leave it out.

Three things the table doesn't settle:

• The break-even is price only. At Sonnet 5's break-even, the GPU would serve about 376 tokens a second around the clock, counting input and output together; about 20 of those a second are output it has to generate. At GPT-6 Luna's, it would be about 7,415. Even the table's 10,000 tickets a month assume one GPU keeps up with about 70 tokens a second on average, and tickets don't arrive evenly. This guide does not claim a measured throughput for gpt-oss-120b on one H100; benchmark it at your expected concurrency before relying on the comparison. Past the server's tested ceiling you need more capacity, and the break-even moves.
• They're different models. gpt-oss-120b isn't Claude Sonnet 5 or GPT-6 Sol, so test it on your own cases before you count the saving.
• Operations work is not counted. A team member still patches, monitors and restarts the GPU server, and that time is outside the table.

The case for running the model yourself anyway is the data, not the price: when the data can't leave the building, the API isn't an option at any price. Some on this results page say fully local is practical now. Simon Høiberg's video, "Can we actually self-host AI agents now?", has chapters on open models such as Qwen3-Coder and GLM-5.1, and a reader in the r/opensource thread wants "an application I can run locally with all the data staying on my device". The middle route keeps the model in your own cloud account through the provider's service, at API-style prices under the provider's data terms. Our LLM development services page works that break-even out at scope.

Which self-hosted AI agent to start with

Pick the kind first:

• if your operations team will build it, start with n8n or Dify;
• if your developers will, start with LangGraph or CrewAI;
• if you already run UiPath, IBM or Automation Anywhere, ask that vendor for the on-premises edition and the list of outbound calls it makes.

Before the first run, switch off what the tool sends home, using the table above.

Run the model yourself when the data can't leave, or when your volume passes the break-even for the model you'd otherwise call. Below that, keep the model in your own cloud account, or call the API under terms you've read.

If none of these fits, or your team would prefer help owning the build, send us the job instead: use the Scope an agent button at the end of this page.