From $80 to $3,000: enterprise SaaS pricing lessons with Alex Halkin @Competera

saas.unbound is a podcast for and about founders who are working on scaling inspiring products that people love, brought to you by https://saas.group/, a serial acquirer of B2B SaaS companies.

In episode #46 of season 5, Anna Nadeina talks with Alex, CEO and Founder of Competera, a pricing platform helping retailers increase their revenue.

I build pricing products for retailers and have spent the last decade-plus turning machine learning experiments into a product that enterprise retailers use to increase revenue. Along the way I learned painful and useful lessons about AI, enterprise sales, pricing strategy, fundraising, and company culture. Below I share what actually worked, what failed, and concrete rules you can apply if you are building a B2B SaaS product—especially if you are thinking about pricing, AI, and selling to enterprises.

From medical engineering to pricing AI

My background started in medical computer science. For my diploma in 2005 we trained a computer to “smell” using sensor arrays—an early machine learning project. That seed of curiosity about learning algorithms never left me.

While at university I ran an outsourcing company building websites and early mobile apps. When a large consultancy acquired a controlling stake in my company they brought me in to optimize teams and workflows. One day I walked into a client meeting and saw pricing analysts doing the same repetitive work in Excel over and over. It was manual elasticity calculations, a simple UI on top, and a big price tag charged to clients. I knew machine learning could automate most of that monkey work.

I pitched the idea internally in 2014. The problem was not only technical feasibility but economics. Back then compute costs for ML prototypes were prohibitively high—our first estimates showed massive daily bills on cloud GPUs. It took years of iteration and industry improvements in GPUs, cloud, and tooling to make applied ML for pricing affordable and repeatable.

First go-to-market lessons: Amazon, Alibaba, and the enterprise pivot

We initially tried to build a product for marketplaces and SMBs, but quickly hit API and policy roadblocks. Amazon blocked our usage pattern. We then tried to engage Alibaba, which was more open at first. After lengthy negotiations it became clear that large platform partnerships often involve complex propositions—licenses, co-investment, requests for local branches, or acquisition-style deals. Six months of back-and-forth taught us how fragile and long enterprise conversations can be.

We eventually pivoted to selling directly to enterprise retailers. Why? Enterprises were ready to pay six-figure deals and could justify the ROI from a percent or two of price optimization. For a bootstrapped team, enterprise revenue became a faster path to building a sustainable business—even with longer sales cycles, procurement hurdles, and heavy legal work.

AI in enterprise: the reality check

Applied AI adoption in enterprises is still early. Most firms are careful about what data and models run outside their perimeter, and for good reason: privacy, compliance, and vendor risk matter. Many requests we now see require private deployment, on-premise models, or strict guardrails.

Two practical points to remember:

• Public models and tooling can be fast to prototype with, but enterprises will ask for controlled deployments or fine tuning inside their infrastructure.
• For many functions it is still cheaper to use humans. Replacing support agents with a naive model can be prohibitively expensive at scale because each generated response has a cost. Until models are cheaper and more controllable, human-in-the-loop solutions will often win on price and compliance.

How we use AI inside Competera

We apply AI to improve productivity rather than replace expertise. Typical use cases include:

• Developer copilots for code completion and commenting. These boost front-end productivity but complex back-end context remains hard to automate.
• An internal conversational knowledge base that lives in Slack and answers product, policy, and HR questions instantly.
• Sales productivity: call transcription, CRM extraction, and suggested follow-ups. One sales rep with the right tooling can do the work of many.

We also make strict choices about tooling because our enterprise customers expect compliance. Tools that indiscriminately record calls or send audio to public services can get blocked. A recent example: an advanced meeting-note vendor was banned from several enterprise customers, which directly impacted its adoption inside those organizations.

How we run meetings and keep productivity high

We are a team of roughly 90 to 100 people spread across many countries. Meetings and context switching destroy productivity, so we adopted a set of rules:

• Six-week operating cadence with short sprints: we run three sprint cycles within this cadence, with one week each for planning and retrospectives. Every six weeks the whole company meets to share outcomes.
• Asynchronous communication where possible: record a short video and attach a Q&A instead of scheduling a 60-minute meeting.
• We ban meetings longer than 30 minutes unless there is a clear, exceptional reason.
• Monday executive stand-ups to align priorities for the week; everything else is minimized.

These practices free up execution time, reduce context switching, and let senior engineers focus on shipping.

Pricing lessons I learned: start with ICP, CAC, and LTV

When founders ask me about pricing, I boil it down to three fundamentals: who is your ideal customer profile, what is your customer acquisition cost, and what is the lifetime value of a customer. Get those three wrong and your business will struggle no matter how clever your product is.

Concrete rules I use and share with founders:

• Know your ICP. Pricing and packaging follow who you sell to. Enterprise buyers, mid-market buyers, and SMBs have vastly different expectations and acquisition economics.
• LTV should be at least 3x CAC. If not, you will struggle to grow profitably.
• Payback period for CAC should be under 12 months for healthy growth. If you recover CAC in 5 to 7 months you are likely underpricing; if recovery takes longer than 12 months you are probably underpricing or have inefficient sales.
• If you have fewer than about 100 paid customers, take pricing off the website and talk to customers. Early days are for learning, not SaaS list prices.

Real example: we started with a product priced at $80 and later raised the basic plan to $3,000 per month without losing conversions. Early customers evaluate value differently than you expect. Test, learn, and be willing to increase price if the value is there.

Also remember sales rep economics. If your CAC is large because you require enterprise reps to close deals, your average contract value needs to justify that effort. A typical rule of thumb is that a sales rep should produce at least 4x their cost in revenue; some investors and operators expect 10x.

Bootstrapping versus raising: money buys time

My philosophy is simple: money buys you time. Bootstrapping forces focus and unit economics discipline. But sometimes the problem you are solving requires speed and capital—medical research, hardware, or rapid enterprise expansion can be impossible without investment.

We bootstrapped for years. When we did raise, we treated capital as a way to accelerate a market that was already showing traction. If the market is not yet educated or ready for your solution, raising too early can burn cash without changing adoption. If you sense momentum and can deploy capital effectively, raising makes sense.

M&A and partnerships: get advisors early and be skeptical

Large companies may talk about partnerships, licenses, or investments in flattering terms. In practice, strategic conversations can be used to learn your product, negotiate favorable licensing, or even force acquisition-style outcomes that benefit them more than you. From my experience:

• Hire independent M&A and legal advisors early, even if you are not actively selling. They protect your rights and save time.
• Be careful when advisors have conflicts because the acquiring company is also a client of that advisor. Independence matters.
• Treat strategic partnership conversations with healthy skepticism and document everything.

One blunt lesson: enterprises can kill a startup by leading an endless set of meetings or negotiations that slow you down. Trust only your team and trusted advisors.

Big wins, failures, and a few hard-won hacks

Our biggest win was building software that Fortune 100 retailers use, and displacing legacy vendors. That was validation that a small team with a strong product can beat big incumbents.

Our biggest failure is more subtle: being too open and naive in some strategic conversations. I learned that corporations can be unkind and opportunistic. That taught me to be fiercely protective of the product, data, and IP.

Hacks and rules I live by

• Co-founder matters. Don’t go it alone. A true co-founder is more valuable than a dozen hires. If you can, find a partner who complements you and can survive the emotional and operational roller coaster.
• Protect your health. Work-life balance is not a buzzword. Burnout destroys companies. Encourage people to take time off, exercise, and recharge. You need a healthy team for the long run.
• Hire for soft skills. Hard skills can be taught. Soul skills—commitment, discipline, communication—are much harder to change. In hiring and firing decisions, prioritize character.
• Commit and complete. We expect people to finish what they start. If you assign a task, deliver it. Repeated missed commitments erode trust faster than anything else.
• Use priority tags. When you message someone, include an indicator like urgent/important or not urgent/important. It sets expectations without constant follow ups.
• Don’t remind twice. I ask once. If someone fails to respond, that is signal. Immediate and clear feedback is better than slow, repeated nudging.
• Flexible hours and remote work. Let teams choose the time that suits their life. We hire across countries and focus on outcomes, not clocked hours.

Money can buy you time. Trust only your team. Commit and complete.

Building a successful enterprise SaaS company is hard. Pricing, distribution, and product-market fit are interconnected. If you anchor your decisions in clear ICPs, understand CAC and LTV math, protect your product and IP, hire people with the right DNA, and keep the team healthy, you dramatically increase your odds of winning.

If you want practical playbooks for pricing, M&A prep, or enterprise go-to-market, start by mapping your ICP to the economics: what revenue impact do you deliver, and how fast can you prove ROI to the buyer? That calculation will answer almost every question about packaging, pricing, and sales motion.

Build for repeatability first, and growth will follow.