Comparison
Akumi vs Aimable
Both of us are European, both put a layer in front of the model, and both expose an OpenAI-compatible API. The difference is what actually answers the request. Aimable redacts your data and forwards it to the frontier model you pick, most of which are American. Akumi serves the request on EU-resident models by default and blocks anything leaving the EU at a fail-closed guard.
Redacted and sent, or answered where it started.
Both platforms sit between your sensitive data and a large language model, and both are European companies. The decision comes down to one question: do you want your data redacted before it is sent to an American frontier model, or do you want it answered inside the EU in the first place? If your compliance objection is that the data reaches a US provider at all, rather than that it reaches one unredacted, that single difference decides the evaluation.
The two things being compared
What each one actually is.
Aimable is a suite people log into. Akumi is a platform you build on. That difference runs through everything below.
Akumi
An EU-resident AI platform you build on. One OpenAI-compatible endpoint fronts EU-resident models, with the services already wired in: a firewall, a knowledge graph for retrieval and memory, a response cache, and per-request observability. Self-serve signup, published pricing, and first-party SDKs for PHP, TypeScript and Python.
Aimable
An AI productivity suite for organisations with sensitive data, built around Spaces, Collections and Policies that end users work in directly. It detects and tokenizes personal data, then routes to the model you choose across GPT, Claude, Llama, Gemini and Mistral. It also exposes an OpenAI-compatible API and MCP. Available as an EU-hosted single-tenant cloud or deployed in your own environment.
Side by side
Redact and forward, or keep it in the EU.
| Dimension | Akumi | Aimable |
|---|---|---|
| What answers the request | EU-resident models by default. Routing to an external or non-EU model is blocked at a fail-closed guard unless you explicitly allow it. | The frontier model you select, including GPT, Claude and Gemini, with personal data tokenized before it is sent. |
| Getting started | Self-serve signup, published pricing, and a first request in minutes without talking to anyone. | Demo-gated. Every call to action books a demo or contacts sales, and pricing is quoted per deployment. |
| Time to production | Minutes. A key and one base_url, with nothing to schedule. | States 2 to 6 weeks to production, on either the cloud or the self-hosted path. |
| Retrieval | Conversation memory and your documents share one knowledge graph. Each collection is its own partition, and every response carries the sources it used. | Curated Collections of documents, team-managed and kept current, searched per Space. |
| Personal data | firewall on by default: pseudonymized before egress, restored in your response. | Comparable: personal data is tokenized before the model and restored on the way back. |
| Developer surface | OpenAI-compatible API, first-party PHP, TypeScript and Python SDKs, an MCP server, and a published OpenAPI spec. | OpenAI-compatible API, MCP, space-scoped keys, and public developer docs. |
- What answers the requestAkumiEU-resident models by default. Routing to an external or non-EU model is blocked at a fail-closed guard unless you explicitly allow it.AimableThe frontier model you select, including GPT, Claude and Gemini, with personal data tokenized before it is sent.
- Getting startedAkumiSelf-serve signup, published pricing, and a first request in minutes without talking to anyone.AimableDemo-gated. Every call to action books a demo or contacts sales, and pricing is quoted per deployment.
- Time to productionAkumiMinutes. A key and one base_url, with nothing to schedule.AimableStates 2 to 6 weeks to production, on either the cloud or the self-hosted path.
- RetrievalAkumiConversation memory and your documents share one knowledge graph. Each collection is its own partition, and every response carries the sources it used.AimableCurated Collections of documents, team-managed and kept current, searched per Space.
- Personal dataAkumifirewall on by default: pseudonymized before egress, restored in your response.AimableComparable: personal data is tokenized before the model and restored on the way back.
- Developer surfaceAkumiOpenAI-compatible API, first-party PHP, TypeScript and Python SDKs, an MCP server, and a published OpenAPI spec.AimableOpenAI-compatible API, MCP, space-scoped keys, and public developer docs.
Claims about Aimable are taken from aimable.ai and were last checked on 29 July 2026. Products move: if something here is out of date or unfair, tell us at contact@akumi.eu and we will correct it.
The residency question
Redaction reduces exposure. It does not move the processing.
Tokenizing personal data before a prompt leaves your building is a genuinely good control, and Akumi does it too: the firewall is on by default and restores the original values in your response. What it does not do is change where the inference happens. Under GDPR, pseudonymized data is still personal data, so sending it to a US provider is still a transfer that needs a lawful basis, a DPA and standard contractual clauses.
That is why Akumi treats the model itself as the control point rather than only the payload. EU-resident models answer by default, and reaching anything outside that set is blocked unless you have explicitly enabled it. The guard fails closed, so a misconfiguration keeps data in rather than letting it out.
| Dimension | Akumi | Aimable |
|---|---|---|
| Default destination | An EU-resident model, with no configuration required to get there. | The frontier model you select for the Space, including US-hosted options. |
| Leaving the EU | Blocked at a guard that fails closed, and only ever opened by an explicit, recorded decision. | A routing choice, with personal data tokenized before the request is sent. |
| If the config is wrong | The request is refused. The failure mode keeps data inside the EU. | The request proceeds to the configured model. |
- Default destinationAkumiAn EU-resident model, with no configuration required to get there.AimableThe frontier model you select for the Space, including US-hosted options.
- Leaving the EUAkumiBlocked at a guard that fails closed, and only ever opened by an explicit, recorded decision.AimableA routing choice, with personal data tokenized before the request is sent.
- If the config is wrongAkumiThe request is refused. The failure mode keeps data inside the EU.AimableThe request proceeds to the configured model.
Retrieval
One knowledge graph, not a folder of documents.
Most retrieval is a document search: chunk the files, embed them, return the closest passages. That answers questions whose answer sits inside one document, and struggles with the ones that do not. Akumi keeps conversation memory and ingested documents in the same knowledge graph, so a question can follow the link from an end-user to their organization to the document that actually answers them.
Isolation is the part worth reading closely. Each collection is its own partition, and the partition key is built server-side from the authenticated caller before the query is assembled. The graph engine never receives a tenant identifier it could be talked into changing. A request cannot address another workspace, because the code never gives it the chance to ask.
| Dimension | Akumi | Aimable |
|---|---|---|
| Shape of the knowledge | Memory and documents in one graph, so answers can traverse the relationships between them. | Curated Collections of documents, team-managed and kept current. |
| Isolation boundary | Computed in the reviewed application layer from the authenticated caller, before the query exists. | Organized around a curator who owns each Collection, scoped per Space. |
| Showing your work | Every response carries a sources array naming the documents and facts it used. | Cites sources in its answers. |
- Shape of the knowledgeAkumiMemory and documents in one graph, so answers can traverse the relationships between them.AimableCurated Collections of documents, team-managed and kept current.
- Isolation boundaryAkumiComputed in the reviewed application layer from the authenticated caller, before the query exists.AimableOrganized around a curator who owns each Collection, scoped per Space.
- Showing your workAkumiEvery response carries a sources array naming the documents and facts it used.AimableCites sources in its answers.
Trying it
You can evaluate this one without booking anything.
Aimable is sold the way enterprise software usually is: every call to action books a demo, pricing is quoted against your deployment, and their site states 2 to 6 weeks to production. For a suite being rolled out to a whole department, that is a reasonable shape.
It is a poor shape for an engineer with an afternoon and a question. Akumi publishes its prices, opens signup to anyone with a payment method, and answers the first real request in minutes against the same OpenAI-compatible endpoint you already write against.
| Dimension | Akumi | Aimable |
|---|---|---|
| Trying it | Self-serve signup, no call, no scheduled onboarding. | Demo-gated. Every call to action books a demo or contacts sales. |
| Knowing the price | Published per plan and per meter, before you talk to anyone. | Quoted per deployment. |
| Reaching production | Minutes: a key and one base_url. | States 2 to 6 weeks. |
- Trying itAkumiSelf-serve signup, no call, no scheduled onboarding.AimableDemo-gated. Every call to action books a demo or contacts sales.
- Knowing the priceAkumiPublished per plan and per meter, before you talk to anyone.AimableQuoted per deployment.
- Reaching productionAkumiMinutes: a key and one base_url.AimableStates 2 to 6 weeks.
The honest answer
Who should choose which.
Choose Akumi if
- Your objection is that the data reaches a US provider at all, not merely that it reaches one unredacted.
- You want EU-resident models serving the request by default, with non-EU routing off unless you turn it on.
- You want to evaluate today, self-serve, against published prices and without a sales call.
- You need per-request residency your own auditors can read off a log.
- You are building a product rather than rolling out a tool for staff to log into.
Choose Aimable if
- You are rolling out a suite for staff to work in directly, rather than building on an API.
- You need the frontier models specifically, and your data protection team has accepted tokenized data reaching a US provider.
- Your requirement is that the platform runs inside your own cloud or on-premise environment.
- A scheduled onboarding suits how your organization adopts software.
FAQ
Questions people ask.
- What is the main difference between Akumi and Aimable?
- Where the request is answered. Aimable tokenizes personal data and forwards the request to the frontier model you choose, which is often US-hosted. Akumi serves it on EU-resident models by default and blocks non-EU routing at a guard that fails closed. Both redact personal data. Only one keeps the inference in the EU as the default.
- Is Aimable EU-hosted?
- Yes. Aimable states an EU-hosted, single-tenant cloud, and also offers deployment into your own environment. The distinction Akumi draws is not about where the platform runs but about which model answers the request: an EU-hosted control layer can still forward the prompt to a US frontier model.
- Does Akumi have an OpenAI-compatible API?
- Yes, so an existing integration moves by changing one base_url and the key. Aimable also documents an OpenAI-compatible API and MCP, so neither platform requires you to rewrite your client.
- How does Akumi keep one team's documents out of another team's answers?
- Each collection is a separate partition of the knowledge graph. The partition key is derived server-side from the authenticated caller before the query is built, so a request can only ever address partitions inside its own organization and workspace.
- Can I prove where a specific request was processed?
- Yes. Every request records the model, the provider and the region that served it in a metadata-only audit trail. Prompts and completions are never stored in that trail.
- Do I need to talk to sales to try Akumi?
- No. Signup is self-serve and pricing is published. You need a payment method before running inference, and then a key and one base_url gets you a first response in minutes.
- Is Akumi GDPR compliant?
- The platform is built around GDPR controls: EU residency, pseudonymization before egress, one-call erasure of an end-user's data, and metadata-only records. A DPA governs Akumi's role as your processor. Compliance is a property of your whole processing activity, not only of a vendor, which is why the audit trail is designed to be evidence you can hand over.
Weighing other options?
Keep it in the EU, and start today.
Change one base_url and send a real request against your own documents, served on an EU-resident model. No demo, no deployment, no procurement cycle.