The short answer
Teams searching for Celonis alternatives usually fall into one of three situations, and each points to a different category of tool.
If the concern is cost, implementation effort, or platform fit, the alternatives are other log-based process mining vendors: SAP Signavio, UiPath Process Mining, IBM Process Mining, ARIS, ServiceNow, Pega, iGrafx.
If the concern is that a large share of the work happens outside the ERP, the alternative is not another miner. It is a category that captures work systems do not log: task mining, or conversational discovery across employees.
If the concern is that the tool produces excellent analysis but the organization still cannot decide what to fix first, the gap is prioritization and follow-through rather than discovery method.
Naming which of the three you are in changes the shortlist entirely. Most evaluations go wrong because the buyer compares vendors before naming the constraint.
Key takeaways
- Celonis is a log-based process mining platform. Its alternatives split into three categories, only one of which is a direct substitute.
- Log-based mining inherits one structural limit regardless of vendor: it sees the work that connected systems record.
- Task mining captures desktop activity but not cross-functional context or intent.
- Conversational discovery captures why the process behaves the way it does, at the cost of needing synthesis and validation.
- The right question is where your process evidence lives, not which platform ranks highest.
- Many enterprises end up combining categories rather than replacing one with another.
What Celonis does, precisely
Celonis is a process intelligence platform built on process mining. (cite index="34-1">It analyzes transactional data from ERP and other enterprise systems using case-centric and object-centric process mining, where case-centric mining follows a process around a defined case such as an order or invoice, and object-centric mining connects related business objects and events across processes involving multiple systems.</cite>
The company is headquartered in Munich with offices worldwide, and (cite index="28-1">it was the first vendor to fully embrace object-centric process mining, introducing Process Sphere as a capability of its platform.</cite>
That approach is genuinely strong for what it does. For a standardized, high-volume workflow that executes inside a system of record, log-based mining reconstructs the real paths and variants with a precision no interview process can match.
Understanding that scope is what makes the alternatives legible. Two limits come up consistently in buyer evaluations.
Evidence scope. (cite index="34-1">System-log process mining can only analyze work captured in the systems connected to it, so spreadsheet reconciliations, email exchanges and manual workarounds disappear from the process model because those steps never produce the event data the analysis depends on.</cite>
Implementation and cost. (cite index="34-1">Celonis case studies show implementation ranges from under 12 weeks to four months from contract signing to go-live, varying by scope.</cite> Reviewers on G2 note that (cite index="33-1">the platform relies heavily on high-quality, complete and consistent event log data, and that organizations with tighter budgets should plan for a higher investment and a clear ROI story up front.</cite>
Neither is a flaw. Both are consequences of the method. Every log-based miner shares them.
The three categories of alternative
| Category | How it gets evidence | Sees | Does not see | Vendors |
|---|---|---|---|---|
| Log-based process mining | Extracts event logs from systems of record | Paths, variants, cycle time, deviations inside connected systems | Work outside those systems; cause and intent | Celonis, SAP Signavio, UiPath, IBM, ARIS, ServiceNow, Pega, iGrafx |
| Task and activity mining | Records or observes desktop and application activity | Repetitive manual work, swivel-chair tasks, application switching | Cross-functional context, judgment, why a workaround exists | UiPath Task Mining, Skan AI, Mimica, KYP.ai |
| Conversational discovery | AI-led interviews across the employee population | Exceptions, handoffs, informal approvals, root causes, improvement ideas | Precise transaction volumes and timings | Horizon |
Only the first row contains direct substitutes for Celonis. The other two are different instruments, and buyers who treat them as competing options usually end up disappointed by whichever one they choose.
Choosing by where your evidence lives
The useful selection question is not which platform is best. It is what proportion of the process you care about produces event data.
| If the process... | Start with | Because |
|---|---|---|
| Runs almost entirely in SAP, Oracle, Salesforce, or ServiceNow | Log-based mining | The evidence is already structured and complete |
| Runs in systems but with heavy manual steps between transactions | Log-based mining plus task mining | Logs show the transactions, task mining shows the gaps |
| Spans many teams, tools, and approval paths | Conversational discovery, then targeted mining | The shape of the process is not knowable from any single system |
| Depends on judgment, exceptions, and regional variation | Conversational discovery | Variation is a policy and context problem, not a log problem |
| Is well understood and needs monitoring | Log-based mining | Continuous conformance checking is what it does best |
| Is understood by nobody, including its owners | Conversational discovery | You cannot instrument a process you cannot describe |
(cite index="29-1">If your processes execute inside major enterprise systems, a system-log miner will reconstruct them well; if a large share of the work happens in email, spreadsheets and SaaS tools outside your ERP, event logs will miss it.</cite>
Log-based alternatives to Celonis
For teams that need process mining and are evaluating vendors, the differences are mostly about ecosystem fit and cost structure rather than method.
| Vendor | Strongest fit | Consideration |
|---|---|---|
| SAP Signavio | SAP-centric landscapes, where the data model is already aligned | Value drops outside the SAP estate |
| UiPath Process Mining | Teams whose end goal is RPA, with a native handoff from mining to automation | Automation-first framing can bias which findings get attention |
| IBM Process Mining | Organizations already inside the IBM stack, tying mining to automation programs | Enterprise procurement cycle |
| ARIS | Teams that need modeling and governance alongside mining | Heavier modeling discipline required |
| ServiceNow / Pega | Processes that already run on those platforms | Limited value for work outside them |
If the evaluation is between these, the deciding factors are usually connector coverage for your specific systems, the internal data engineering capacity required to maintain the pipelines, and total cost over three years rather than year one.
When another miner is the wrong answer
Three signals suggest the constraint is not the mining platform.
Your best analysts keep saying the data does not reflect reality. That is a coverage signal. If the process runs partly outside the logged systems, a better miner produces a more precise picture of the same fraction.
The findings are accurate but nobody acts on them. A process mining tool can show that a purchase order moves through 47 variants. It generally cannot explain why managers bypass a specific approval, why one region adds an offline check, or why teams do not trust the data enough to stop maintaining a parallel spreadsheet. Without cause, findings do not convert into initiatives.
Implementation is stalling on data quality. If connector development and log cleanup are consuming the timeline, the organization may need a faster way to establish where the problems are before it invests in instrumenting everything.
Discovery by evidence type
Three instruments, three questions
| Instrument | Answers | Typical time to first insight |
|---|---|---|
| Log-based mining | What path did work take through our systems? | Weeks to months, after connector and data work |
| Task mining | What are people doing on their screens between transactions? | Weeks, after deployment and consent process |
| Conversational discovery | Why does the process behave this way, and what would change it? | Days |
The categories answer different questions. Choosing between them as if they were substitutes is the most common evaluation error.
The hybrid pattern most enterprises land on
In practice, large organizations rarely replace one instrument with another. They sequence them.
Conversational discovery first, to establish where the problems are, which functions are affected, and which processes justify deeper instrumentation. That takes days and produces a ranked list rather than a complete model.
Log-based mining second, focused on the two or three processes that the first pass identified as high-value and system-heavy. That produces the precise volumes, timings, and variant analysis needed to build a business case.
Task mining third, and only where the specific question is desktop-level repetitive work suitable for automation.
The sequence matters because the expensive instrument is the one that requires data engineering. Pointing it at the wrong process is how process mining programs lose their sponsor.
Where Horizon fits
Horizon is not a process mining tool and should not be evaluated as a direct Celonis replacement for system-heavy, log-rich workflows. It is an AI-powered continuous discovery platform that operates on the evidence layer system logs cannot reach.
Horizon runs AI-led discovery conversations across an organization, asking role-specific follow-up questions that capture how work actually happens: the handoffs, the exceptions, the approvals that live outside any system, and the improvements the people doing the work would make. It structures that evidence against processes and systems, ranks opportunities by impact, and converts them into initiatives with owners and business cases.
The property that matters in this comparison is coverage and speed. In the published Mercado Libre case, Horizon ran discovery across 2,000 employees in Finance and adjacent functions in five countries in four days. The manual baseline had been 11 to 20 weeks. Despegar produced 45 prioritized initiatives for a CRM migration in four weeks, against a manual version that had taken 12 months.
Those are results from specific engagements with specific conditions, not a projection for any given company.
The practical fit is complementary rather than competitive. Use system mining where the process lives in systems and the question is what happened. Use conversational discovery where the process lives across people and the question is why, and what should change.
Evaluation checklist
Before shortlisting any vendor in this space, answer these.
- What proportion of the process you care about produces event data in a connected system?
- Do you know which processes are worth instrumenting, or is that the question?
- What internal data engineering capacity can you commit to connector development and maintenance?
- Can the tool show you why it reached a conclusion, back to source evidence?
- Does the output stop at findings, or does it produce prioritized initiatives with owners?
- What happens to the picture six months after implementation?
- Who inside the business will act on the findings, and are they involved in the evaluation?
- What is the total cost over three years, including internal effort?
If question 2 is unresolved, resolve it before running a mining evaluation. Instrumenting the wrong process is more expensive than choosing the wrong vendor.
FAQ
What are the best alternatives to Celonis?
It depends on the constraint. For log-based process mining, the main alternatives are SAP Signavio, UiPath Process Mining, IBM Process Mining, ARIS, ServiceNow and Pega. For work that happens outside connected systems, the alternatives are task mining tools such as UiPath Task Mining, Skan AI and Mimica, or conversational discovery platforms such as Horizon. These are different categories, not interchangeable options.
Is there a cheaper alternative to Celonis?
Several log-based miners have lower entry pricing, but total cost is usually driven by connector development, data preparation, and ongoing pipeline maintenance rather than license fees. Before optimizing for price, check whether the process you want to analyze produces usable event data at all, since that determines whether any log-based tool will deliver value.
Can process mining replace employee interviews?
No. Process mining shows what systems recorded. It generally cannot explain why a team created a workaround, why an approval rule differs by region, or which exception paths carry the most risk. Enterprises that need cause as well as sequence typically combine system evidence with human evidence.
What is the difference between process mining and process discovery?
Process mining is one method of process discovery, specifically the reconstruction of workflows from system event logs. Process discovery is the broader effort to establish how a process actually runs across people, systems, documents, decisions, and exceptions. Mining is a subset.
How long does a Celonis implementation take?
Published Celonis case studies show ranges from under 12 weeks to around four months from contract signing to go-live, depending on scope, connector requirements, and data quality. Organizations with fragmented or inconsistent event data should plan for the upper end.
Should we run discovery before choosing a process mining tool?
Usually yes. Knowing which processes carry the most value at stake, and which of them are system-heavy enough to mine well, makes the mining evaluation far more focused and reduces the risk of instrumenting a process that turns out not to matter.
Choose the instrument, then choose the vendor
Most Celonis evaluations start as vendor comparisons and end as category questions. The organizations that get this right invert the order: they establish where their process evidence lives, decide which instrument reads that evidence, and only then build a shortlist.
Every week spent comparing platforms without knowing which process is worth instrumenting is a week of investment decisions made on an incomplete picture.
See it. Fix it. Stay ahead.