Zylon asks you to install and run a complete AI platform inside your own perimeter. Akumi asks you for a base_url. We run the EU-sovereign platform, keep it patched and scaled, and give you an OpenAI-compatible API you can be building against this afternoon, with residency you can prove per request rather than per deployment.
Zylon and Akumi solve the same anxiety from opposite ends. Zylon hands you a complete AI platform to install inside your own perimeter, on-premise or in your own cloud. Akumi runs the EU-sovereign platform and hands you a base_url.
That single choice cascades into everything else. Deploying inside your perimeter means you own the capacity, the upgrades and the uptime, and it means a start date, a subscription negotiated against your deployment, and infrastructure that costs the same in a quiet month. A managed platform means none of those, published prices you can evaluate before contacting anyone, and a first real request this afternoon. If your organization wants to operate its AI platform, they built for that. If it wants to use one, we did.
A managed EU-sovereign AI platform behind one OpenAI-compatible endpoint. EU-resident models answer by default, with a PII firewall, a guard, a governed knowledge graph for retrieval and memory, a response cache and per-request observability on the same call. Self-serve signup, published usage-based pricing, and nothing for you to deploy, patch or scale.
A private AI platform installed in your environment: on-premise, in a private cloud VPC, or fully air-gapped with, in their words, no internet connection required. Built by the team behind the open-source PrivateGPT project. They describe a complete stack rather than a wrapper, with built-in authentication, logging, rate limiting and observability, OpenAI-compatible endpoints, and integrations for n8n and LangChain.
| Dimension | Akumi | Zylon |
|---|---|---|
| Who operates it | We do. Serving, scaling, upgrades, patching and capacity are ours, and there is nothing for you to run. | You do, inside your own infrastructure, with their software and support. |
| Getting started | Self-serve signup and a first request in minutes, with no call and no installation. | States production-ready in under one week, which is fast for a deployed platform but is still a project with a start date. |
| Pricing transparency | Usage-based credits at published rates. Nothing runs, nothing costs, and you can price it before you talk to us. | States a fixed-cost subscription with no per-token pricing. Prices are not published. |
| Cost when idle | Nothing. You pay for requests, not for capacity sitting there overnight and at weekends. | A fixed subscription plus the infrastructure it runs on, whatever the usage. |
| Build surface | OpenAI-compatible API, plus first-party PHP, TypeScript and Python SDKs and an MCP server. | States OpenAI-compatible endpoints, with n8n and LangChain integrations. |
| Governed retrieval | Conversation memory and your documents share one knowledge graph, so an answer follows the link from a user to their organization to the document that answers them. Each collection is its own graph partition, namespaced to your organization and workspace before the query is built, and every response carries the sources it used. | Document retrieval, deployed and operated inside your environment. |
Claims about Zylon are taken from zylon.ai and were last checked on 28 July 2026. Products move: if something here is out of date or unfair, tell us at [email protected] and we will correct it.
A platform deployed inside your perimeter is yours from that moment on. Somebody sizes the infrastructure, somebody applies the upgrades, somebody is paged when it stops at eleven at night, and somebody re-tests it after each release. That is not a criticism of the software; it is what deploying any platform means. It is a standing commitment of engineering attention that has to come from somewhere, and in most teams it comes out of product work.
Akumi is the other trade. Serving, scaling, patching and capacity are ours, and the interface you depend on is an HTTP endpoint rather than a running system. There is no version to be behind, no upgrade window to schedule, and no rota. What you integrate against on day one keeps working without anyone on your side owning it.
| Dimension | Akumi | Zylon |
|---|---|---|
| Who applies upgrades | We do, behind the endpoint, with no window to schedule. | You do, in your environment, on your release cadence. |
| Who carries the pager | Nobody on your side. | Somebody on your side. |
| Capacity planning | Ours. Spikes are absorbed without a purchase. | Yours, sized in advance for the busiest hour. |
Zylon states a fixed-cost subscription with no per-token pricing and no usage limits, which is genuinely attractive at high, steady volume: heavy users stop watching the meter. The same shape is unforgiving at the other end. A pilot, a seasonal workload or a feature still building adoption pays the full amount, and so does the infrastructure underneath it, in the months where usage is a fraction of what was provisioned.
Akumi meters per token and per request at published rates. A quiet month costs almost nothing, a launch month scales without a conversation, and each governance module bills on its own line so you can see what the firewall, retrieval, guard and cache actually cost. Their prices are not published, so a like-for-like comparison needs a call; ours can be worked out from the pricing page before you speak to anyone.
| Dimension | Akumi | Zylon |
|---|---|---|
| Pricing model | Usage-based credits at published rates, with pay as you go and no monthly minimum. | States a fixed-cost subscription with no per-token pricing and no usage limits. |
| Cost of a pilot | Roughly the tokens you spend evaluating it. | The subscription, plus the infrastructure it runs on. |
| Finding out the price | Published. Read it now. | Not published. Contact them. |
Zylon states production-ready in under one week, which is fast for a platform that has to be installed, and considerably faster than most enterprise software manages. It is still a project with a kickoff, an environment, people from both sides, and a decision made before any of it starts.
The gap that matters is not one week against another. It is that Akumi lets you find out whether the thing fits before committing anyone. Signup is self-serve, the pricing is published, and a key plus one base_url gets a real answer from a real model against your own workload within the hour. The evaluation happens before the decision rather than after it.
| Dimension | Akumi | Zylon |
|---|---|---|
| Trying it | Self-serve, no call, no environment to prepare. | An engagement, scoped and scheduled. |
| Time to a first real answer | Minutes. | States under one week to production. |
| If it turns out not to fit | You spent an afternoon. | You spent a deployment. |
Where the platform runs. Zylon is installed inside your own perimeter, on-premise or in your cloud, and operated by you. Akumi is a managed EU-sovereign platform reached over an OpenAI-compatible API, operated by us. Everything else, pricing shape, time to first request, who handles upgrades, follows from that.
No. Akumi is a managed EU platform. The data is EU-resident and residency is recorded per request in the audit trail, but the platform itself is not something you install. If your requirement is specifically that the software runs on hardware you own, that is a different category of product.
Yes. The application, the data and the default models are EU-resident, and routing to an external or non-EU model is blocked at a guard that fails closed unless you explicitly allow it. Every request records the model, provider and region that served it.
It depends on volume and on how much idle capacity you would be paying for. Zylon states a fixed-cost subscription with no per-token pricing, which suits high steady usage. Akumi is usage-based at published rates, which suits variable or growing workloads and makes a pilot cost roughly the tokens it consumes. Their prices are not published, so a precise comparison needs a conversation with them.
Yes, plus first-party SDKs for PHP, TypeScript and Python and an MCP server. Zylon also states OpenAI-compatible endpoints, so neither choice forces you to rewrite your client.
On Akumi, minutes: signup, a payment method, a key and one base_url. Zylon states production-ready in under one week, which is quick for a deployed platform but begins with a scheduled engagement rather than a signup form.
They are ingested into a governed knowledge graph, partitioned per workspace and per collection. The partition key is derived server-side from the authenticated caller, so a request can only reach partitions inside its own organization. Every answer carries the sources it used, and the audit trail stores metadata only, never your content.
Change one base_url and send a real request today. No deployment, no hardware, no week-long onboarding before you learn whether it fits.
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