Anaplan vs Datarails (2026)

Data reaches Anaplan through Anaplan Data Orchestrator, which connects, transforms and loads data from source systems into planning models, and through CloudWorks for cloud storage, a prebuilt Informatica connector, a command line utility for on premises jobs and transactional and bulk APIs. The agents work on that data inside Anaplan.

Analyst writes updates back to the live planning model within the user's permissions, CoModeler edits import data sources and creates list import actions, and the role based agents start Anaplan workflows. None of them posts a change into an ERP, CRM or HR system on its own.

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FinanceOS connects to more than 600 systems and data sources, from ERPs, CRMs and HR platforms to banks, billing systems and spreadsheets, and keeps them synchronized and consolidated. The AI works on what arrives. Spend codes, matches and reconciles invoices and card transactions against ERP entries, Desk pulls invoice, payment, vendor and customer records into each request, and the assistant drafts a journal entry in the conversation for a person to check and post. Corrections and payments stay inside Datarails, and no agent posts a change back into the ERP or CRM by itself.

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Anaplan Workflow breaks a planning process into tasks, approvals and handoffs with owners and deadlines, and ties them to data updates run by Data Orchestrator. Workflow Agent builds those flows from a typed or spoken description, a document or a whiteboard image, adjusts them as the process changes and flags bottlenecks with fixes such as adding a reviewer. The role based agents for finance, sales, supply chain and workforce share one calculation and data foundation, so each one works with the plans of the other functions in view.

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The strategy, planning and reporting agents have merged into one general assistant, which asks clarifying questions, offers analysis options, confirms the key decisions and then builds the output, so one request can run from a data pull to a finished Excel model and deck.

Task agents sit inside the screens where the work happens, such as flux analysis and journal entries in the close. Workflows route budgets, forecasts and reports through the right reviewers, Month-End Close assigns tasks with deadlines across the close, and Desk routes each finance request by team, type and priority with SLA timers.

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Workflow launches tasks by itself when a threshold is crossed, a condition is met or an upstream change lands in the model, and any task can run on a schedule. Data updates triggered by Data Orchestrator feed the same process. The role based agents watch performance continuously and send alerts when they spot a deviation or a risk, so the finance agent can surface a variance before anyone asks. People also reach the agents by typing or speaking a question inside their Anaplan applications, on desktop or mobile.

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Insights sends AI written summaries on the schedule a team sets, weekly, monthly or quarterly, to the people it chooses, and automated agents rerun recurring analyses without being asked. Desk takes finance requests from email, Slack, Teams and other channels into one queue, where the AI gathers the records and drafts an answer. People also reach the agents and the assistant in the web app, and outside assistants reach the data through the AI Connector.

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Agents answer from the customer's live planning models, modules and lists, and Anaplan's data ontology ties them to the business context of finance, supply chain, sales and workforce planning. Analyst queries across every model behind an application, works from a synced copy of the data that honors selective access, and reads extra context admins add in the notes of any line item.

The calculation engine computes the numbers, so each figure in an answer comes from the model, and Analyst shows the source data behind it. CoPlanner works from key data points in the application's models, refreshed on a regular cadence, so its answers can lag the latest change to a plan.

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Agents work on FinanceOS, a governed data model that consolidates, maps and harmonizes data across entities, currencies and systems, with mappings, allocations and version control kept by the finance team. A Semantic Layer defines business metrics such as revenue, gross margin, headcount, ARR and DSO once for use across Datarails, and the assistant says whether a number came from a governed metric or an aggregation of the tables. Every answer can drill from a summary to departments, accounts and transactions.

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CoModeler knows the current state of the model and remembers the last few prompts of the conversation, which survives a page refresh, an accidental close or signing in again, but conversations are not saved from one session to the next. Analyst carries follow up questions within a session and keeps no chat history once the session ends or the user moves to another page. What lasts beyond that is the planning model itself, the workflow history and the audit trail, all of which belong to the application.

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Every conversation with the assistant is saved automatically for the user who had it, across all areas of the product, and nobody else sees it. Each conversation runs as a session on Anthropic's infrastructure, which holds its history, files and outputs, so it falls outside Anthropic's zero data retention option. A team can also keep company profile documents, such as board presentations or financial models, that agents and outside assistants read for context, and the team reviews and updates them itself. An agent does not write down what it learned from a conversation for next time.

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CoModeler lays out a step by step build plan with annotations and dependencies and executes no change without the builder's explicit confirmation, though a builder who opens a prompt with Approve skips the plan and lets it build at once, and Anaplan steers CoModeler toward development environments.

Analyst writes back to a live plan only within the permissions the user already holds, and Anaplan Workflow routes submissions to assigned approvers with deadlines and notifications. Agent Studio keeps every agent inside the platform's existing roles, so an agent cannot reach data or actions its user could not.

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Before creating or changing anything, the assistant states exactly what will change and waits for the user's OK, so nothing is created or changed without approval. A journal entry arrives in the conversation as a draft for the user to check, and a person posts it, never the agent. In Spend, agents work under defined controls and approval requirements, so a person approves before any payment, and invoices with discrepancies wait for review. Desk drafts each reply for a team member to review and send.

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Anaplan holds SOC 1 Type 2 and SOC 2 Type 2 reports and ISO/IEC 27001, 27017, 27018, 27701 and, since September 2026, 42001 certificates, and its trust center adds CSA STAR Level 1, Cyber Essentials, ENS, TX-RAMP and TRUSTe.

Sign in runs through SAML 2.0 single sign on, with OAuth for APIs and integrations, role based access control, central identity management and IP allow lists, and only users with the AI Admin role can open Agent Studio. Data is encrypted at rest and in transit, a bring your own key add on puts the key in the customer's hands, and customer data, model structures and metadata never train a foundation model. Every agent and every AI Gateway request runs under the user's existing Anaplan permissions.

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Datarails holds a SOC 2 Type II report and a SOC 1 report, both available on request from its trust center. Sign in supports SAML 2.0 single sign on with Okta, Microsoft Azure, Duo, Google Workspace and JumpCloud, with multifactor authentication. Access follows roles (Super Admin, Admin, Contributor and Viewer) and permission groups down to the entity, department or report, the AI tools read only what the user is already allowed to see, and authorization is checked again on every request.

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Workflow keeps timestamped audit trails and flow chart reports of tasks, approvals and progress, and the platform sends audit events to a customer's SIEM for alerting. Every change CoModeler makes lands in model history under the user who directed it, marked AI assisted, and Analyst and CoPlanner show the source data, model and timestamp behind each answer. AI Gateway records every tool call an outside assistant makes, with a timestamp and usage details, and throttles heavy use, and Agent Studio shows AI credits, interactions and usage per user.

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FinanceOS keeps audit trails and full data lineage on the numbers, and the close records approvals and every change. Super Admins get an audit log of outside assistant sessions on the AI Connector, listing each session's member, AI platform, time, duration and status with a timeline of its events, while a durable record of each tool call is still being built. Conversations with the in app assistant stay visible only to the user who had them, so an admin has no record of an agent session to open later.

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Anaplan runs as its own cloud service, with in country data centers that keep customer data local for residency rules, among them AWS hosted locations in Singapore and the UAE and data centers in Indonesia, India and Australia. The Singapore center keeps data inside Singapore for public sector and financial services clients. The platform is also sold through AWS Marketplace. There is no customer hosted or on premises edition.

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Datarails runs on Microsoft Azure in the United States, Canada and the UK, and each customer's environment sits in one of those regions, with the application database following the same Azure location. When a user works with the assistant, some data leaves Datarails for Anthropic's infrastructure, in a region left unnamed. The AI Connector is processed in the United States, with routing by region under evaluation and no delivery date. The platform is a hosted service with no customer hosted edition.

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Prebuilt role based agents cover financial planning and consolidation, revenue performance management, supply chain planning and workforce planning, each embedded in the matching Anaplan application.

Agent Studio switches on ready Analyst configurations, Finance Analyst, Supply Chain Analyst, Sales Analyst and Workforce Analyst, for an application, and CoModeler and Workflow Agent arrive as ready agents for building models and processes.

Packaged applications such as Integrated Financial Planning, Demand Planning, Territory and Quota Planning and Operational Workforce Planning give each agent a ready model to work in.

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Agents sit ready in the screens where finance work happens, each switched on for an account separately: flux analysis and journal entry drafting in the close, a month end close assistant, cash transaction matching and an intercompany reconciliation assistant. Spend adds agents for invoice extraction and coding, transaction classification, matching and exception checks, and Desk adds an assistant that drafts answers to finance requests. Teams working in Claude get a Datarails plugin with ready made finance skills that produce Excel workbooks and PowerPoint decks.

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Anaplan pairs a language model's reasoning with its own deterministic calculation engine, so the arithmetic in an answer is computed by Anaplan, not by the model, which also keeps token use down. A customer has no say in which model runs the agents, and the provider is not named. The Claude, Gemini and Copilot connections through AI Gateway are outside assistants reading Anaplan data, not a choice of model for Anaplan's own agents.

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The assistant and the agents built into Datarails screens run on Anthropic's Claude models through Anthropic's Claude Managed Agents platform, and other AI features use OpenAI models, directly and through Azure OpenAI. A customer cannot switch its agents to another provider or model. Teams that prefer Claude or ChatGPT can also query Datarails data through the AI Connector, but that is their own assistant reading Datarails.

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Transactional and bulk APIs extend the platform for integration work, with OAuth that a customer sets to its own authentication and access policies. AI Gateway adds a supported MCP interface with permissions, rate limits and audit, through which outside assistants and agents query planning data. Access through the gateway starts read only, so an outside agent can look up a forecast but cannot change a model, a formula or a plan through it.

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The FinanceOS AI Connector is a hosted MCP server with OAuth 2.0 and PKCE sign in per user and a public tool list for discovering tables, reading metrics, running filtered queries and aggregations. Every connector tool is read only for now, so an outside agent can analyze Datarails data but not change it. Any Datarails customer can also pull its tables over an OData feed at no extra cost, and the Data Gateway Service API accepts CSV and Excel uploads into Fileboxes from other systems.

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CoModeler finds error prone formulas, redundant structures and slow spots in a customer's planning model, then documents each formula in plain language, and Workflow Agent studies how long tasks take to suggest a faster flow. Both of those check the customer's own model and process. A team has no way to test how an agent answers or acts before rolling it out to planners.

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Custom AI workflows built under the AI Transformation package are tested against real data by a Datarails forward deployed engineer before they go live, which is a service, not something a team runs itself. Datarails' AI terms leave it to the customer to review outputs for accuracy before relying on them, and within the product a finance team cannot score an agent's answers or try it on known questions before rolling it out.

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Everything the agents do happens inside Anaplan, through its models, workflows and data connections. Data from other systems arrives through Data Orchestrator, CloudWorks and the APIs, and none of the agents clicks through another application on a user's behalf.

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Agents work on FinanceOS data and produce Excel files, decks, PDFs and widgets inside Datarails. Data from other systems arrives through connectors and uploads, and when Claude works in Excel the Datarails add in carries out refreshes and drill downs rather than an agent reading the screen. None of Datarails' agents operates another application's screen for a user.

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