Two established platforms with different architectures and heritage. Both serve enterprise finance - here's how they differ.
The short version
Planful (formerly Host Analytics) was built for accountants. Excel-like interface, strong consolidation, statutory reporting baked in. Cloud-based with a focus on financial close and traditional FP&A. The platform accountants find familiar.
IBM Planning Analytics is powered by the TM1 in-memory OLAP engine - exceptional calculation speed and flexibility. Deep Excel integration via PAx, deployment flexibility (cloud, on-premise, hybrid), and the ability to handle modelling complexity that other platforms struggle with.
Planful is simpler to deploy and maintain. IBM Planning Analytics is more powerful and flexible. The choice depends on your complexity requirements and how much platform capability you actually need.
Feature comparison
| Planful | IBM Planning Analytics | |
|---|---|---|
| User interface | Excel-like, familiar | PAW or Excel (PAx) |
| Deployment | Cloud only | Cloud, on-premise, hybrid |
| Primary strength | Consolidation & close | Complex modelling |
| Implementation time | 8-16 weeks typical | 3-9 months typical |
| Statutory reporting | Out-of-the-box | Custom configuration |
| Complex allocations | Standard capabilities | Exceptional depth |
| Skills required | Finance users can manage | Specialist TM1 skills |
| AI capabilities | Planful Predict | IBM watsonx integration |
AI capabilities
Both platforms include AI, though the focus and integration differ.
Machine learning focused on financial close and reporting. Signals flag anomalies and unusual patterns. Automated insights during consolidation. Designed for finance-specific workflows rather than general planning.
AI-powered forecasting integrated into the TM1 engine. Predictive scenarios and what-if modelling. Leverages IBM's watsonx portfolio. Natural language querying in Planning Analytics Workspace.
Planful's AI is tightly focused on accounting workflows. IBM's is broader but requires more setup. Neither should drive the platform decision - core planning capabilities matter more than AI features at this stage of market development.
When to choose
Multi-entity consolidation with intercompany eliminations, currency translation, minority interest. Planful was built for this. If close and consolidation are 80% of your workload, it's purpose-built.
No infrastructure to manage, no upgrade projects, automatic maintenance. Planful is cloud-only and managed. Finance owns it without IT involvement.
GAAP, IFRS, local statutory requirements. Planful's reporting framework is designed around audit and compliance. This isn't an afterthought - it's core functionality.
Planful implementations typically complete in 8-16 weeks. If you're under time pressure for budget cycles or board requirements, faster deployment matters.
When to choose
Multi-step allocations, transfer pricing, sophisticated profitability analysis. TM1's rules engine handles scenarios that simpler platforms can't. If your planning logic is genuinely complex, this matters.
Data sovereignty, regulatory requirements, or IT policy mandates. IBM Planning Analytics offers on-premise, cloud, and hybrid options. Planful is cloud-only - no flexibility here.
Planning Analytics for Excel (PAx) provides native Excel integration - not an add-in workaround. Users work in familiar spreadsheets with live TM1 data. For Excel-centric teams, this is the point.
Upgrading existing TM1 to Planning Analytics Workspace preserves your models and logic. Moving to Planful means rebuilding everything. Sometimes evolution beats revolution.
The honest truth
Excellent at consolidation and traditional FP&A, but if you later need sophisticated operational planning, supply chain, or sales territory modelling, you'll outgrow it. Consider your 3-5 year requirements.
Deep TM1 expertise takes years to develop. The talent pool is smaller than it once was. Plan for how you'll resource ongoing maintenance - internal team or managed service.
Planful projects are typically shorter and require less technical expertise to maintain. IBM implementations take longer but deliver more flexibility. Match the investment to your actual needs.
Planful finance users can often make changes independently. IBM Planning Analytics typically requires TM1 specialists for model changes. Consider your ongoing change requirements.
On pricing
Planful is generally positioned as more accessible than enterprise IBM deployments. Implementation costs are typically lower due to shorter project timelines and less specialised skills required.
IBM's pricing can be more flexible for organisations with existing enterprise agreements. Total cost of ownership depends heavily on ongoing support requirements - TM1 managed services vs. internal Planful administration.
Compare based on your specific requirements and long-term support model, not just initial licensing.
Alternative paths
Sometimes neither Planful nor IBM Planning Analytics fits.
You want modern UX and fast deployment. Pigment offers a more contemporary interface and typically faster implementation. Worth considering if user adoption or time-to-value are primary concerns.
You need connected enterprise planning. Anaplan excels at linking finance, sales, supply chain, and workforce in a single platform. Neither Planful nor IBM PA position themselves primarily for this use case.
Your requirements are straightforward. Well-structured Excel or simpler tools might be sufficient for smaller teams without complex consolidation or modelling needs.
How to decide
Standard budgeting and consolidation? Planful handles it well. Complex allocations, transfer pricing, sophisticated profitability? IBM's flexibility may be needed.
If on-premise is required, Planful isn't an option. If cloud-only is acceptable, both are in play. This single question often narrows the decision.
Can finance users make ongoing changes? Do you need or have TM1 specialists? The ongoing support model affects total cost and platform suitability.
Bring your consolidation requirements, your hardest allocation rules, your edge cases. Generic demos hide real differences. Proof of concept with real data reveals truth.
Questions
We work with both platforms and can give you objective guidance based on your specific requirements. Let's discuss what actually makes sense for your organisation.