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OpenAI Presence vs Lindy vs n8n vs Make: the enterprise agent decision for teams that can't wait for an FDE
Drafted July 23, 2026 by Pondero Reviews.
OpenAI shipped Presence on July 22, and by the next morning the wrong question was already circulating in ops-director inboxes: "what is Presence." The right one is blunter. Can you even buy it, and if not, what do you stand up this quarter instead?
Here is the answer before the table. Presence is not self-serve. It deploys through OpenAI Forward Deployed Engineers or a select systems integrator, and OpenAI has not published a price, per VentureBeat's launch report, whose reporter asked twice and was still waiting on a number. If your company is not a Fortune 1000 account with an existing OpenAI enterprise contract, Presence is not on your shortlist for Q3. Three platforms that are: Lindy, n8n, and Make. All three are affiliate partners, the disclosure sits in the footer, and none of them pays us to rank it above Presence.
| Platform | Best for | Self-serve? | Starting price | Model lock-in |
|---|---|---|---|---|
| OpenAI Presence | Fortune 1000 with an OpenAI contract and budget for an FDE engagement | No. FDE or integrator only | Undisclosed | Yes. OpenAI models run the core agent |
| Lindy | Assistant-first teams: inbox, calendar, phone, voice | Yes | $49.99/mo Plus | No. Pick the model on Pro and up |
| n8n | Technical teams that want to self-host and stay model-agnostic | Yes | Free self-hosted; 20 euros/mo Cloud | No. Route to any LLM |
| Make | Ops teams that want visual, debuggable agents across many apps | Yes | Free tier; $9/mo Core | No. Make's AI provider or your own key |
Prices confirmed live on July 23, 2026 from each vendor's pricing page (linked in the sections below). The rest of this piece resolves the pick per team: what Presence bundles, the profile it actually fits, then the three self-serve options and a flat recommendation by headcount.
What OpenAI Presence actually is
Strip the launch copy and Presence is a managed service for running production agents inside a large enterprise. It packages the parts that teams usually stitch together by hand: company knowledge, standard operating procedures, the set of actions an agent may take, a simulation harness to test against edge cases, graders that score whether the agent followed policy, and guardrails that pull it back when a conversation drifts out of bounds. Each deployment is scoped to one job, say resolving a billing dispute or handling an IT request, and the agent gets only the access that job needs. You decide what it can do alone, what needs sign-off, and when a human takes the call.
The improvement loop is the genuinely new part. Once an agent is live, Codex (running on GPT-5.6) watches production signals through a Presence plugin, spots where the agent is escalating too often, and proposes a fix that your team tests against the live version before rollout, per SiliconANGLE's coverage. OpenAI runs its own English phone-support line on this stack and says it now resolves 75% of inbound issues without a human, and that the Codex loop cut handoffs by 15 percentage points over ten days, per VentureBeat. Both numbers are company-reported and not independently verified. Treat them as a target OpenAI hit on its own traffic, not a promise on yours.
Two facts do the real work for a buyer. The agent's core runs on OpenAI models. You can wire third-party models and APIs in for guardrails, tools, and other workflow parts, but the reasoning core is OpenAI, confirmed by an OpenAI spokesperson to VentureBeat. And you cannot sign up. A Forward Deployed Engineer or an approved integrator builds it with you, the same high-touch model Palantir made famous. That is a procurement cycle, not a free trial.
Who should stop reading about Presence right now
Presence is the right call for a narrow, real profile. You already run OpenAI at the enterprise level. You have a dedicated AI engineering team or budget to bring one in. Your compliance posture rules out standing up agents on a self-serve SaaS plan, and you want OpenAI's own engineers accountable for the deployment. BBVA, SoftBank, and IAG are named as evaluators in VentureBeat's report, and that is the weight class: a bank exploring voice support in Mexico, a telecom testing Japanese-language conversations, an insurer bracing for storm-season call spikes.
If that is not your team, close the tab on Presence. A 100-person company waiting on an FDE calendar slot is burning a quarter it does not have to burn. The undisclosed price alone should end the evaluation for most mid-market buyers: you cannot line-item a number OpenAI will not give a reporter. Everything below ships today, on a credit card, without a sales call.
Lindy: the assistant that answers the phone
Lindy is the pick when the job is acting like a person rather than running a pipeline. You describe the role in plain language ("watch my inbox, draft replies to inbound leads, flag anything from an existing customer"), and Lindy stands up an assistant that fires on triggers: a new email, a calendar event, an inbound call. Inbox triage, meeting notes, and voice are where it wins outright. Make does not pick up the phone. Lindy does.
Setup is a 60-second flow, not a build. Sign up for the 7-day trial, connect an inbox and a phone number, and describe the job. Three use cases carry it: front-line customer support over voice and SMS, sales development that watches a shared inbox and drafts follow-ups, and recruiting coordination that schedules and preps interviews off a calendar.
Pricing, confirmed on Lindy's pricing page July 23, 2026: Plus is $49.99 a month and covers up to two inboxes with email drafting, scheduling, and meeting notes. Pro is $99.99 a month, roughly triple the usage, three inboxes, computer use, and the option to pick which model Lindy runs on. Max is $199.99 a month for up to five inboxes. HIPAA, a signed BAA, SSO, and audit logs live on Enterprise.
Candid con: Lindy is priced and shaped around a person's workload, not a firehose. The usage caps on Plus are fine for one operator's inbox and calendar. Point it at a 500-ticket-a-day support queue and you are into Pro or Max fast, and at that volume a workflow tool metered by operation can come out cheaper. Model choice is also gated: you are on Lindy's default until you reach Pro.
n8n: the model-agnostic option you can self-host
n8n is the answer for a technical team that wants to own the stack. It is the only platform here you can run entirely on your own infrastructure, and the only one where model choice is a first-class design decision rather than an upsell. Agents built in n8n route to whatever LLM you point them at, OpenAI, Anthropic, a local open-weights model, per node. For a compliance team that needs data residency, or a developer allergic to vendor lock-in, that is the whole argument.
Self-hosting is a single container. The Community Edition is free under n8n's fair-code license, and this gets you a working instance:
docker run -it --rm \
--name n8n \
-p 5678:5678 \
-v n8n_data:/home/node/.n8n \
docker.n8n.io/n8nio/n8nOpen http://localhost:5678, drop in an AI Agent node, and attach a model credential plus the tools the agent can call. Swap the model by changing one node, not by upgrading a plan.
If you would rather not run servers, n8n Cloud starts at 20 euros a month billed annually for 2,500 workflow executions on the Starter plan, with Pro at 50 euros a month for 10,000 executions, per n8n's pricing page on July 23, 2026. The billing model is the quiet advantage: n8n charges per full workflow execution, not per step or per user, so a chatty multi-step agent does not multiply your bill the way a per-operation meter can. Every plan includes unlimited users and every integration.
Candid con: self-hosting is free the way a puppy is free. Someone owns updates, backups, scaling, and the pager. If your team does not have a DevOps person who wants that job, n8n Cloud or a managed rival will cost less once you price in the hours. The builder is also more technical than Make's or Lindy's; expect a steeper first week.
Make: the visual agent an ops manager can actually debug
Make is the recommendation for a workflow-first team that has to explain its automations to someone who did not build them. An agent in Make lives on the same canvas you already use for scenarios. You give it a goal, hand it a set of Make modules as tools, and it decides which to call and in what order across Make's 3,000-plus app catalog. One agent can read a Gmail thread, check Airtable, and update HubSpot without you drawing every branch in advance.
The Reasoning panel is why Make earns its slot. It shows each decision the agent makes, step by step, on the board, per Make's AI Agents page. When an agent does the wrong thing, you see the tool call it chose and can fix the goal or the tool set. Most agent platforms hide that. For an ops lead who has to defend a bad automation to a director, the visibility is the product.
Make AI Agents ships in beta on every plan, including the free tier, and you can run them on Make's built-in AI provider or your own LLM key, per Make's pricing page. Pricing there, confirmed July 23, 2026: Free is $0 for 1,000 credits a month, Core is $9, Pro is $16, and Teams is $29, with the paid tiers quoted at 10,000 credits. Each module action counts as one credit, so an agent is metered per operation.
Setup is a click-path, not code. Open a new scenario, add the AI Agent module, write the goal in plain language, then attach modules as tools (a Gmail "Search Emails" module, a Slack "Create a Message" module). The agent chooses among them at run time.
Candid con: per-operation metering makes a badly scoped agent expensive in a way you cannot fully predict before you build it. An agent that loops or over-calls tools burns credits fast. Watch the first runs in the Reasoning panel, count the module calls per completed task, then decide whether the shape holds at volume before you turn it loose.
The pick by team size
| Your team | The pick | The reasoning |
|---|---|---|
| Small ops team (5-20 people) | Make, Free or $9 Core | Visual builder anyone can debug, agents on the free tier, and per-operation pricing that stays cheap at low volume. Add Lindy Plus if you need voice or inbox triage. |
| Mid-market (50-500 people) | Make or n8n for workflow-first; Lindy for assistant-first | Workflow-first shops want Make's debuggability or n8n's execution-based pricing and model choice. Support and sales teams whose core need is inbox and phone go to Lindy. This is the band Presence markets to and cannot yet sell to on a timeline. |
| Enterprise (500+ people) | n8n self-hosted if data residency rules; Presence only with an OpenAI contract and FDE budget | Self-host n8n Enterprise when compliance demands the stack live on your infrastructure with any model. Presence fits only if OpenAI already runs your AI and you can fund an FDE engagement at an undisclosed price. |
The verdict
Presence is a real product for a narrow buyer: a Fortune 1000 already committed to OpenAI, with an engineering budget and a compliance case that a managed, FDE-built deployment answers better than a SaaS plan. For that team, the Codex-driven improvement loop and OpenAI's own accountability are worth the procurement cycle. Everyone else is reading launch coverage for a product they cannot purchase.
For the overwhelming majority of this article's readers, the ones who need an agent running before an FDE returns a calendar invite, the split is clean. Pick Make when the job is connecting apps and you need to audit every decision the agent makes; the free tier and the Reasoning panel make it the low-risk place to start. Pick n8n when you want to self-host, stay model-agnostic, and pay by execution instead of by step; it is the developer's choice and the compliance team's. Pick Lindy when the job is a person's work, the inbox, the calendar, the phone, and voice is on the requirement list. The flip condition that trips most teams: if your "agent" is really a support rep, buy Lindy; if it is really a pipeline, buy Make or n8n. Presence can wait until you are the size that OpenAI sends an engineer to.