What Does Process Mining Cost?

For smaller businesses, the answer is probably not what you've assumed.

In 2017, I sat in a Deloitte demo of Celonis. Process mining was still the new kid on the block, and what I saw genuinely changed how I thought about process analytics. Every approach I'd used before felt suddenly limited by comparison. I wanted to run with it immediately.

The quoted price for a proof of concept: £200,000.

My finance director said no.

So I phoned Celonis directly. Their own price for running a proof of concept: £10,000. By no means free, but it was a different conversation entirely.

The Biggest Cost Is One You Can Sidestep

That gap, £200k with a partner and £10k direct (for a proof of concept only) wasn't arbitrary. Enterprise process mining tools are genuinely complex to implement, and vendors have historically built their go-to-market around a partner ecosystem. The vendor focuses on the software. Implementation partners, ranging from Big Four consultancies to specialist boutiques, handle the technical integration, navigate the organisational complexity, and bring industry-specific process knowledge. At enterprise scale, all three pillars of support matter. The partner earns their fee.

In 2017, going direct worked for me because I had spent 10+ years implementing process analytics as a consultant. I knew how to structure the data and interpret the outputs. Today, AI assisted tools can handle much of that technical integration work for you, which means the gap in process mining specific expertise is narrower than it used to be.

For a smaller business, the systems, the data, and the decision making are usually much closer together so you may not need someone to navigate a fragmented enterprise landscape on your behalf.

Where external help may still add value is on the strategic side. Someone who understands your industry and knows what good looks like for your kind of operation. But these are often people you're engaging with already and they can use the outputs from process mining as part of their engagement, rather than being brought in to manage process mining as its own engagement.

The Costs That Exist Regardless

After a successful proof of concept, I got internal sign-off for a license deal. We assembled an in-house team of data engineers, data scientists and analysts, and managed the implementation ourselves. Nine months later, we had our first purchase-to-pay model up and running.

Even without a partner in the room, the costs were real. There are four worth understanding before you commit to anything.

Licensing. The major enterprise vendors like Celonis and SAP Signavio don't publish list prices. Pricing is negotiated individually, typically scaled to the size and revenue of the organisation, and bundled with professional services. It's a model designed for large organisations where the contract value justifies the conversation. Enterprise deals can run to six figures annually per process and scale up from there.

Transparent pricing is still rare even among tools positioning themselves at the smaller end of the market. Many will offer a free tier followed by a sales process that is in itself a cost. When evaluating tools, look for published pricing or ask the vendor about costs upfront before you invest time getting set up on their free tier. Their paid option may be well beyond what a smaller business can justify.

Data extraction. Someone always has to do this work. Before a process mining tool can do anything useful, it needs a structured record of what happened when, and to what business objects. This needs to be extracted from whatever systems your business runs on — usually more than one. If you want real-time analysis rather than periodic snapshots, a live connection needs to be established and maintained. API integrations make this considerably more straightforward: either by connecting directly to your existing system's API, or by sending data to the process mining tool via its own public API. Look for tools that offer both options.

Data transformation. Once you have your data, it rarely arrives in exactly the format a process mining tool expects. Traditionally, this meant engineering work to reshape and prepare data extracts before analysis could begin. The better modern tools handle this for you by using AI-assisted data loading agents that map and interpret your data automatically, inferring the structure and relationships without you having to specify them manually. If a tool requires significant data preparation work before you can see anything useful, that's a cost worth factoring in.

Analyst time. Even after setup, someone needs to interpret the output. In our case that was a team. The question for a smaller business is whether the tool does enough of the interpretive work for you. Automated insights generation and natural language copilots mean that an operations manager can now ask questions of their data directly and get useful answers without needing a trained process mining analyst in the room. That's a meaningful shift, and the shift has happened fast. Large language models have only become capable enough to do reliable interpretive work in the last year or two. Before that, you still needed a trained analyst to make sense of the output.

Organisational Cost

The nine months it took us to get our first process model connected wasn't purely technical. In a large organisation, a process mining initiative touches system owners, data governance teams, IT security, and the people whose processes you're analysing. When those stakeholders haven't bought in or actively resist, progress slows to a crawl. Our initiative didn't have joined-up sponsorship. The people who owned the underlying systems hadn't been brought along. That added months, and it nearly derailed the whole thing.

This pattern is common in large organisations. Process mining gets championed by one function and experienced as a threat by another. The initiative gets done to people rather than with them.

In a smaller business, you may not be navigating that kind of organisational fragmentation. But it's still worth thinking about before you start. Who owns the data you need? Who will be affected by what the analysis reveals? Getting those people on board early, rather than presenting findings after the fact, is the difference between an initiative that lands and one that stalls.

Flow Myna's Pricing And Why We Publish It

We have a strong view on pricing in this market. Process mining has been positioned as an enterprise tool for so long that many smaller businesses assume it's not for them. Opaque pricing reinforces that assumption and that's why we publish our pricing openly.

Our free tier supports small datasets and is worth using before you commit to anything. If you can't get something useful out of your data at the free tier, that's important information. Paid plans are in the low hundreds of dollars per month, scaling up for larger data volumes and more users. You can see the full breakdown on our pricing page.

Those prices put process mining within reach. Something you can justify against a single process improvement and not a capital decision that needs a separate business case. The scenarios where you genuinely need enterprise-level spend are specific: custom integrations into complex legacy systems, very large data volumes, or contractual SLAs. For most smaller operations, those aren't day-one requirements.

We're currently in early access, working with a small number of design partners for free in exchange for real operational data and collaborative feedback. If you want to understand what your processes actually look like without any commercial commitment, this is the best place to start.

Final Thought

When I reflect on that 2017 experience, the obstacles were real on multiple fronts. At the time, the vendor landscape was thin so if you needed to push serious data volumes through a process mining platform, your options were limited, and the most capable tools were also the most expensive. Per-process licensing meant that every new question you wanted to ask came with a commercial conversation attached.

Things have changed. There are now capable tools that can handle meaningful data volumes without enterprise price tags and licensing models that no longer penalise you for being curious. AI has compressed the expertise gap that made going direct so difficult without a background in process mining. And the organisational overhead that consumed so much of our time and goodwill was also largely a function of enterprise scale.

If you're running a smaller operation and previously concluded process mining wasn't for you, it's worth asking whether what you were looking at was a specific moment in the technology's history, aimed at a specific kind of organisation.

The underlying questions process mining answers like how do our processes run in practice, and where are we losing time and money? are just as relevant at 100 employees as they are at 10,000. What it costs and takes to find out is genuinely different now.