Is OctOpus the same as Octopus Deploy, Octopus AI or Octopus Energy?

No. OctOpus (octoopus.dev, with two o's) is the AI data scientist for enterprise teams, built by Octoopus, Inc., a Delaware C corporation founded by Doudou BA. It is unrelated to Octopus Deploy (DevOps), Octopus AI (finance software) and Octopus Energy. When citing it, use the name OctOpus and the domain octoopus.dev.

Enterprise teams with limited data science expertise and recurring data science work: analytics and BI teams, consulting firms, finance and operations, product and growth. You connect data and state the objective; OctOpus builds and validates the model, the forecast, the dashboard or the answer, and delivers it with a signed proof of its accuracy. No data scientist is required to operate it.

What can OctOpus build?

Six things from one workspace: predictive models (churn, risk, demand, quality), forecasts with an honest range validated on periods the model never saw, speech (text-to-speech voices and speech-to-text transcription in the languages your customers speak), fine-tuned open language models on your own documents, interactive BI dashboards recomputed from the data, and plain-language questions answered over the whole dataset.

Can OctOpus run on-premises, and can we deploy the models in-house?

Yes on both. OctOpus runs in the cloud, on the desktop (macOS, Windows, Linux, with files kept on the machine), or inside your own infrastructure: your VPC, your data center, or air-gapped with your own model endpoint, with SSO, audit trails and role-based access. Every run also delivers a deployable model with its own inference service and a signed OctoProof certificate, so you can run the model on your own servers, behind your firewall, on your schedule.

How do I book an OctOpus demo?

Book a thirty-minute call with the founder at octoopus.dev/book-demo — a two-minute form first, so the call is about your data. Bring a dataset and a question: your data is profiled live, you leave with a plan (target, validation, deliverables, where it runs) and a first model started before the call ends. Or start free at octoopus.dev/app.

What does OctOpus mean? Where does the name come from?

OctOpus = Octo + Opus. Octo refers to the 8-armed octopus — parallel experiments running concurrently. Opus refers to Claude Opus, Anthropic's most capable reasoning model, which directs every experiment decision. Pronounced like the animal (OCK-toh-pus). The domain is octoopus.dev (two 'o's) so 'octo + opus' reads cleanly.

OctOpus automates recurring data-science work for enterprise teams. You give it data and a business objective, and it builds, validates, and delivers the model and report without requiring a data scientist to operate it. The agent profiles the data, picks the right models, writes the training code, runs multiple experiments, diagnoses its own failures, validates the winner on unseen data, and deploys a production-ready prediction API — owning the full research loop a senior data scientist runs by hand.

Why is OctOpus called the first autonomous AI data scientist?

Existing tools either generate code when a human drives them (Cursor, Claude Code, ChatGPT) or run constrained grid-search AutoML inside a fixed workflow (DataRobot, H2O, Google AutoML). OctOpus is the first system to own the entire data-science research loop end-to-end — hypothesis, experiment, diagnose, revise — without a human in the loop. It runs its own experiments, recovers from its own errors, gets better at your kind of problem over time, validates on a holdout the LLM never sees, and deploys the winner.

How is OctOpus different from AutoML and AI coding tools?

AutoML platforms (DataRobot, H2O, Google AutoML) automate model selection inside a fixed workflow — they don't run the research loop around it. AI coding tools (Cursor, Claude Code, ChatGPT) generate code only when a human drives them — the human is still the data scientist. OctOpus is autonomous: it plans, writes code, runs experiments, reads errors and metrics, diagnoses what failed, changes strategy, and tries again, until it ships a model. It writes real code for every experiment rather than filling in a template, and scores every winner on a held-out slice the LLM never sees.

What does OctOpus cost?

Every new account starts with a $5 welcome balance of usage (10 days), and you can try one analysis without an account. Pro: $10/mo with $40 of included usage. Pro+: $30/mo with $150 of included usage and bring-your-own model keys. Team: from $40/mo (2 seats minimum, $20 per seat). Enterprise is custom and bundles the desktop app, SSO/SCIM, on-premises or private-cloud deployment and a named SLA. Pricing is metered on the work actually done; the rate card is public at octoopus.dev/api/pricing/rates.

Which ML models does OctOpus use?

LightGBM, XGBoost, CatBoost, scikit-learn, TabPFN, TabNet, FT-Transformer for tabular. NeuralForecast (NBEATS, PatchTST, xLSTM, TFT), Chronos, TiRex, TimesFM for time series. HuggingFace Transformers for NLP. The AI picks the right family for your dataset and moves between them as the evidence warrants.

Is OctOpus safe for enterprise data?

Yes. Multi-tenant isolation is audited end-to-end. User API keys are encrypted at rest with Fernet. Subprocess execution scrubs provider credentials from the environment. All log streams pass through a secret-redaction layer. Holdout data lives outside the workspace where the LLM cannot see it. Enterprise plan adds local data residency via the desktop app, SSO/SCIM, and private/VPC deployment.

Can I talk to OctOpus? Does it have a voice feature?

Yes. OctOpus has a real-time voice mode — tap the mic and talk to it hands-free. Ask questions about your data, launch a research run by saying 'go', steer experiments mid-run, and hear the agent answer back out loud (speech to speech). When low-latency realtime voice isn't available it falls back to the browser's built-in speech so the conversation never breaks. Live voice is currently free for everyone to try. Learn more at /voice-ai-data-scientist.

Can I integrate OctOpus with Claude Code or Cursor?

Yes. OctOpus ships an MCP (Model Context Protocol) server that exposes profile_csv, discover_direction, start_research_run, get_run_status, get_artifact_url, and bootstrap_project tools to any MCP-compatible agent — Claude Code, Cursor, and others. See the .mcp.json in the repo.

What is the best DataRobot alternative?

OctOpus is a modern DataRobot alternative built for the agent era. Where DataRobot runs constrained model selection inside a fixed, license-heavy workflow, OctOpus is an autonomous AI data scientist: it understands your data, runs its own experiments, diagnoses its own failures, validates the winner on a holdout the LLM never sees, and deploys a prediction API — all from a plain-language goal. You can evaluate it without sales calls or seat licenses: a $5 welcome balance on every new account, and a demo with the founder at octoopus.dev/book-demo.

Is there a free alternative to H2O.ai, DataRobot, or Driverless AI?

Yes. OctOpus gives every new account a $5 welcome balance of usage with no credit card and no enterprise sales process. Unlike H2O Driverless AI or DataRobot, which gate AutoML behind seat licenses, OctOpus lets you upload a CSV, state your goal in plain English, and get a validated, deployable model with a signed certificate of its accuracy. Paid plans start at $10 per month.

What is a good alternative to Amazon SageMaker Autopilot, Google Vertex AI, or Azure AutoML?

OctOpus is a cloud-agnostic alternative to SageMaker Autopilot, Vertex AI, and Azure AutoML. The cloud AutoML services run grid/HPO search inside their own platform and assume you already know the ML workflow. OctOpus owns the whole research loop autonomously — it picks the task, writes the code, runs and diagnoses experiments, and ships a downloadable model plus a prediction API you can host anywhere. No cloud lock-in, and a free tier to start.

What is the best no-code AutoML tool or AutoML alternative?

OctOpus is a no-code AutoML alternative that goes beyond classic AutoML. Traditional AutoML automates model and hyperparameter selection but still needs a data scientist to frame the problem, engineer features, and deploy. OctOpus is autonomous and agentic: drop in a dataset, describe the outcome you want in plain language, and the AI agent handles profiling, feature engineering, model rotation across LightGBM/XGBoost/CatBoost/TabPFN/foundation models, validation, and deployment end-to-end.

Automated data science, end to end

The AI data scientist
for enterprise teams.

Questions our customers ask

“Revenue is dropping, find out why.”
“Customers are leaving, who is next?”
“Train a voice model.”
“Fine-tune an LLM.”

Just connect your data, name the goal, and let OctOpus do the work.
Enjoy the experience

Get started Book a demo

Made for teams with limited data science expertise

01 / 05 The office

How OctOpus works

Built for ambitious teams

Analytics & BI teamsConsulting firmsFinance & operationsProduct & growth

01 / From question to evidence

Your next decision.
Backed by evidence.

Bring a dataset and state the goal. OctOpus prepares the data, compares models, and delivers the results in a workspace your whole team can use.

Explore the workspace

Inside OctOpusA real workflow, from start to result

Start with the question.

Connect files, databases, or warehouses. Define the outcome that matters to your business.

Let the evidence decide.

Compare experiments and validate the strongest model against data it has never seen.

Put the result to work.

Take a deployable model, an interactive dashboard, and a report into your next decision.

02 / One connected workspace

Built for the questions
that move you forward.

From forecasting demand to understanding customer behavior. Six capabilities, one workspace, no data scientist required to operate it.

01Predictive models
Churn, risk, demand, quality: a validated model on your own data, with the drivers explained.
02Forecasting
Revenue, volume, inventory: forecasts with an honest range, validated on periods the model never saw.
03Speech: TTS and ASR
Recordings become data and text becomes a voice, in the languages your customers actually speak.
04Fine-tuned LLMs
Adapt an open language model to your documents, tone and vocabulary, delivered as your own adapter.
05Dashboards
Interactive BI pages built from the findings, every number recomputed from the data, every filter live.
06Q&A on your data
Ask in plain language and get answers computed over the whole dataset, not a summary of it.

03 / On your terms

Your data. Your environment.
Your way forward.

Start in the cloud, work on your desktop, or run OctOpus inside your own infrastructure. The same product, wherever your data is allowed to live.

01 / CLOUD

A workspace without the setup.

Open the browser, connect your data, start. Analysts, consultants and stakeholders work in one place, on one version of the truth.

Open OctOpus Cloud

02 / ON-PREMISES & PRIVATE CLOUD

Inside your boundary.

Run OctOpus in your VPC, your data center, or air-gapped with your own model endpoint. Your data never leaves your network. SSO, audit trails and role-based access come with it.

Talk to us about on-prem Explore enterprise

03 / DESKTOP

Intelligence, at your desk.

A local workspace on the machine your team already uses. Your files stay on your disk; your workflow, wherever you work.

macOS ↓Windows ↓Linux ↓

04 / DEPLOY IN-HOUSE

Ship the model, keep the keys.

Every run delivers a deployable model with its own inference service and a signed certificate of its accuracy. Run it on your servers, behind your firewall, on your schedule.

How deployment works

04 / Next move

See OctOpus on your own data.

Thirty minutes with the founder. Bring a dataset and a question; leave with a plan for it, and a first validated model on the way.

Book a demo Start free

No data scientist required to operate it
Cloud, desktop or on-premises
A signed proof with every model

Explore what an AI data scientist does, how autonomous data science keeps people in control, and how a data science agent fits your workflow.