Abacum vs Cube (2026)

Data comes into Abacum through more than 700 native integrations across ERP, HRIS, CRM, billing and data warehouse systems, plus SFTP and an Excel connector, and the AI works on the model those feeds keep current. Teams that keep hybrid models in spreadsheets stay in step through a two way Google Sheets integration or Excel uploads.

The MCP connection runs the other way, carrying Abacum's governed answers out to Claude, Gemini and ChatGPT. None of the agents creates, updates or sends anything in the source systems themselves, so their actions stay inside Abacum's own models, spaces and mappings.

Sourceabacum.ai/product/dataread 2026-10-13

Cube connects to hundreds of source systems, with guides for NetSuite, Salesforce, Workday, Sage Intacct, Snowflake, Databricks, ADP Workforce Now, QuickBooks, HubSpot, Rillet, Dynamics 365 Business Central and Campfire, and syncs them on a schedule or on demand. Agents can start a fresh import from an existing connection before they answer, and they put their work into Cube scenarios, Boards, Tables and decks. None of them books an entry, updates a deal or changes an employee record back in the ERP, CRM or HR system.

Sourcecubesoftware.com/integrationsread 2026-10-13

AI Modeler works from a plan. Asked for a model, it scans the connected datasets, picks a structure from its blueprints and proposes every variable and formula, and once approved it builds the model with its dimensions and totals in one pass, the same way it adds a variable and a new breakdown to a model that already exists. Workflow automation carries planning across people. Department spaces are duplicated by dimension, open and lock by status, and route budget and headcount requests to finance for a decision.

Sourceabacum.ai/blog/ai-modelerread 2026-10-13

Charlie, the super agent, breaks a question into tasks, routes each piece to one of four agent teams (Data Managers, Analysts, Planners and Business Partners), lets them pull, reconcile, model and draft, then checks the combined answer against the source data before returning it. One request can run from a data refresh through variance analysis to a drafted board deck. Cube Workflows add task lists with owners, assignees, statuses and due dates for processes such as the month end close.

Sourcecubesoftware.com/ai-agentsread 2026-10-13

Incoming data starts work without anyone asking. As actuals land from connected systems, the AI classifies new accounts, maps and cleans the records and flags anomalies as they appear, and it pulls in business signals such as deal sentiment and product usage. Modeler and the chat agent start from a person's request inside Abacum, by typing or by voice, and the MCP connection lets the same question start in Claude, Gemini or ChatGPT.

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People reach Charlie in the Cube Workspace, in the Excel and Google Sheets add ins, in Slack and Teams, and from any MCP assistant such as Claude or ChatGPT. Anomaly Detection watches the numbers and flags lines that look off before anyone asks. Source systems sync on a schedule, so fresh actuals land in the model and the plans update around them without someone starting the work.

Sourcecubesoftware.com/ai-agents/analystsread 2026-10-13

Answers come from the governed model Abacum keeps current from connected systems, so a question about pipeline conversion or spend by vendor rests on the same actuals, plans and dimensions finance reports from. AI Modeler also draws on a curated library of model blueprints that Abacum keeps extending. Board packs, contracts, benchmarks or meeting notes attached to a chat add outside context to that conversation alone.

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Charlie answers from the customer's own Cube. It decides which slices of actuals, budgets, forecasts and scenarios it needs and fetches them through the governed layer, with the company's formulas, account and entity hierarchies and currency conversions already applied, so revenue means what the company means by revenue. It can drill from a summary figure to transaction lines in the connected source systems, and every number opens back to the records behind it.

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Within a conversation the agent keeps what it has been given, so a question asked again after a board pack is attached comes back with the pack's drivers folded into the answer. Approved models, published spaces and budget changes persist as the planning data itself. There is no sign of a memory that follows a user from one chat to the next or holds a team's conventions for later questions.

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Each conversation with Charlie and each Planner session is saved for the user who started it, and a History view reopens any of them to carry on where it left off. Context stays inside one conversation, so starting a new one for an unrelated question keeps the earlier context from carrying over. Admins can also write standing instructions for Charlie, such as always showing budget against actuals or how the fiscal calendar runs, which apply in every conversation for every user. Nothing an agent learns in one conversation reaches the next.

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AI Modeler shows its whole proposal, variable by variable with the logic behind each, and saves nothing until a user approves it, after refining it in plain language if needed, or rejects it. A delete needs explicit confirmation, with its downstream impact shown first. The agent can read or change only what the user could already read or change by hand, and budget requests from department heads wait as pending until finance approves or rejects them.

Sourceabacum.ai/blog/ai-modelerread 2026-10-13

Agent writes land only in scenarios opened for AI write back or created by the AI, so the live budget stays untouched unless someone opens it, and each write carries the user's name and an AI flag. Writing through MCP needs its own permission, which only Finance or Admin users can hold, new workflows and tasks arrive as drafts with no assignee, and a publish can be undone. Data Manager agents score each proposed mapping or bulk change by confidence, and the team approves it before anything lands.

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Single sign on works with any identity provider that uses SAML or OpenID Connect, and roles are assigned by group from one screen, down to sensitive payroll and compensation data, with the AI working inside that same permission structure. Abacum holds a SOC 2 Type II report and an ISO/IEC 27001:2022 certificate issued by Armanino LLP and declares GDPR compliance. Data is encrypted at rest with AES 256 GCM and in transit over TLS 1.2 or later, and its trust center shares reports and policies on request.

Sourceabacum.ai/product/securityread 2026-10-13

Cube holds SOC 2 Type II and SOC 1 reports, the SOC 2 examined by Dansa D'Arata Soucia, and handles data to HIPAA requirements. Sign in runs through SAML or OIDC single sign on with Okta, Duo, Google or Microsoft Entra, which an admin can enforce, plus app or text based multifactor authentication. Role based access and each user's Data Access Scope reach down to the cell, the agents inherit the asker's permissions, and separate permissions govern Charlie, the Planner, MCP read, MCP write and AI administration, so AI can be switched on or off for each use through the same roles.

Sourcecubesoftware.com/securityread 2026-10-13

Any result the AI produces can be inspected from its cell, showing the data sources, formulas and assumptions behind it. Abacum keeps a history of every edit to models, spaces and dimension mappings, and asking the agent what changed in a forecast returns the changelog with previous and new values, the dimensions touched and the person behind each change, inside Abacum or through an assistant connected over MCP. Each department head can pull the same trail for their own changes.

Sourceabacum.ai/product/intelligenceread 2026-10-13

Every agent query, answer and publish is recorded with the user, the permission scope applied and the sources used, and denied actions are recorded too. The audit trail shows each data change with who made it, the event type, the number of values changed and the before and after figures, and change history marks which changes came from AI. Because answers open to the transactions behind each figure, a reviewer can see exactly what an agent relied on.

Sourcecubesoftware.com/securityread 2026-10-13

Every customer runs on Abacum's AWS environment in the European Economic Area, in Ireland and Germany, with no other region on offer. AI processing on AWS stays inside the EEA, Microsoft's AI services run in the EEA and Anthropic in the United States. Abacum's data processing terms place the primary processing in the European Union and allow transfers to the United States where the service needs them. A buyer that must keep data in its own country or cloud has no option to choose.

Sourcetrust.abacum.ai/subprocessorsread 2026-10-13

Cube runs as a hosted service on AWS, with all data and infrastructure in US based regions, and offers a data processing agreement for GDPR needs. A company that needs its financial data held in the EU or another country cannot get that, and there is no customer hosted edition.

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AI Modeler can start from a curated library of model blueprints, so a requested P&L or headcount plan follows a proven structure from the first build. AI Spaces comes with a library of finance templates, ready prompts that build the reports finance runs each month on the customer's own model in a click. The AI Classifier, anomaly detection, AI Forecasting with backsolving and Modeler each do a separate job and work without the others.

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Four agent teams come with every plan. Data Managers handle mapping, bulk edits, connection setup and hierarchy upkeep, and Analysts bring slices, Smart Variance and Anomaly Detection. Planners carry the Planner Agent, Smart Forecaster and Formula Modeler, and Business Partners carry Visualization, Board Builder and Deck Builder. The Agent Exchange adds ready plays, such as a variance bridge to budget, a rolling forecast refresh, an investor update or a new entity mapping, which Charlie runs once the user fills in the bracketed inputs.

Sourcecubesoftware.com/ai-agentsread 2026-10-13

Abacum picks the models. Its AI features run through AWS inside the EEA, Microsoft's Azure AI services in the EEA and Anthropic in the United States, and no setting lets a customer choose or swap the model behind an agent. Calculations never go to a model, since Abacum's own engine performs every one. A team that prefers its own assistant can reach Abacum data from Claude, Gemini or ChatGPT, while the agents inside Abacum stay on Abacum's choice.

Sourcetrust.abacum.ai/subprocessorsread 2026-10-13

Charlie and the agent teams run on language models from providers available through Amazon Bedrock, chosen by Cube, with Anthropic named among Cube's subprocessors since March 2026, and Cube trains no models of its own for these features. AI settings hold a company's standing instructions and nothing else, so a company cannot pick the model behind its agents. Outside assistants such as Claude or ChatGPT can work on Cube data through the MCP server, but those are the customer's own tools reading Cube.

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An MCP connection lets Claude, Gemini and ChatGPT query Abacum data under the same governance and pull the forecast change report. Its address, sign in method and tool list are not public, and no public API or developer reference for the platform is offered, so outside software works with Abacum through that connection, the Excel connector, SFTP and exports.

Sourceabacum.ai/product/intelligenceread 2026-10-13

The Cube MCP Server, generally available on every plan with usage limits, signs users in with OAuth and offers a full tool set, from get_financial_slice_of_data and get_drilldown_data to publish_data, duplicate_scenario, create_dashboard, create_planning_table, create_workflow and initiate_import, so a team can point any agent framework at the governed model and write plans, Boards, Tables, scenarios and workflows back under the user's permissions. The Cube REST API has a public OpenAPI reference covering agent chat and Planner sessions, AI settings and credits, canvases, workflow runs and connection syncs, authenticates through OAuth with a PKCE flow, and is sold as an add on.

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Nothing lets a customer test an agent before it reaches users, such as scored test cases or a staging step for AI changes. AI Modeler's Review mode reads a customer's model and suggests where its logic can be simplified, which checks the model, not the agent. Abacum runs its own observability and evaluation tooling for the language models behind its features, out of the customer's hands.

Sourceabacum.ai/blog/ai-modelerread 2026-10-13

Before Charlie returns an answer it checks each figure against the source data, and every query, answer and AI write sits in the log for review afterward. The Planner, still in open beta, asks clarifying questions before it builds a scenario. Cube tested its own accuracy on its own books and does not warrant AI output, reminding users on every AI surface to check results. A finance team cannot run an agent against a set of known questions to see how it does before giving it to the wider team.

Sourcecubesoftware.com/ai-agentsread 2026-10-13

Abacum's agents work through the platform's own model, its native integrations and its MCP connection, and none of them drives a browser or a desktop application. Data arrives through connectors and SFTP, and spreadsheets link through the Excel connector, with no screen automation involved.

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The agents work through Cube's governed data layer, its source connections and the spreadsheet, Slack, Teams and MCP surfaces. None of them opens another application and clicks through it for a user.

Sourcecubesoftware.com/ai-agentsread 2026-10-13