Why a crypto analytics startup still makes sense in 2025
If you think “it’s too late” to launch a crypto analytics startup, the numbers say otherwise.
After the brutal drawdown of 2022, total crypto market cap fell from almost $3T at the end of 2021 to around $850B in December 2022. In 2023 рынок отскочил: by December 2023 capitalization was hovering near $1.7T, and in mid‑2024 it repeatedly crossed $2.4–2.6T, largely fueled by spot BTC ETFs and renewed institutional inflows. Alongside this, various research firms estimate the blockchain analytics / compliance market at roughly $3–4B in 2023 with double‑digit annual growth, driven by regulators, trading firms and DeFi protocols.
All that volatility means one thing: there is a painful, expensive information gap. People are still guessing, while the data is already on‑chain.
That gap is exactly where a modern crypto analytics platform can live — if you build it for a real problem, not for a pitch deck.
Start with pain, not with dashboards
Most founders in this space begin by sketching fancy charts. Wrong direction.
Better question: who is losing money today because they don’t see what’s happening on‑chain? Traders, funds, compliance teams, DeFi protocols, even tax firms. Each segment has its own blind spots.
Real case: The $10M “simple dashboard” mistake
Between 2021 and 2023 I’ve seen several teams raise millions to build generic crypto market intelligence solutions: price charts, volume charts, whale alerts. One of them hit $20K MRR in the bull run… and slid below $3K MRR by Q1 2023. Churn was over 25% monthly.
Why? They built something interesting, not something irreplaceable.
Users told them: “Cool UI, but I can get 80% of this from free tools and Twitter. Why should I pay $99/month?” They had no defensible niche.
Compare that to a tiny team that focused on one problem: “Help small funds detect smart‑money wallet rotations before listings.” They plugged into a crypto data API for trading firms, aggressively filtered wallet cohorts, and sent extremely specific alerts. By 2024 they’ve stayed under 5% monthly churn with just 30 B2B clients — but each client pays four figures.
Same tech stack. Different focus. One survives; one quietly sunsets.
How to find a “bleeding neck” problem in crypto data
Go narrow:
1. Pick a segment: crypto hedge funds, market makers, NFT funds, DeFi treasuries, exchanges, compliance teams, tax firms.
2. Map actual risks: front‑running, wash trading, MEV, rug pulls, sanctions screening, credit risk of counterparties, treasury mismanagement.
3. Watch what they already hack together: internal spreadsheets, custom scripts, a Frankenstein mix of 5 free blockchain data analytics tools.
If your future users have a person whose unofficial job is “manually clean and reconcile on‑chain data every week,” you’ve found a wedge. Your first product is to fire the spreadsheet.
Picking your niche: investors, institutions or “civilians”
Three surprisingly different worlds
The phrase “crypto analytics” hides three almost separate planets:
– On‑chain analytics software for investors
Swing traders, crypto funds, even family offices. They want signals: wallet flows, token unlocks, liquidity shifts, funding rates, perp positioning.
– Compliance & risk analytics
Exchanges, neobanks, OTC desks, fintech apps. They care about sanctions, AML, fraud patterns, and counterparties’ risk scores.
– Builder / protocol analytics
DAOs, DeFi protocols, NFT marketplaces. They need dashboards about retention, cohort behavior, protocol health, token emissions.
Trying to serve all three from day one is suicide. Their data models, UX expectations and sales cycles are completely different.
A realistic approach: pick one type of user, one type of decision you will improve, and one blockchain to start. You can always expand once you’re indispensable to a small group.
Data plumbing: the unsexy core of your startup
You’re not building charts; you’re building a pipeline: nodes → raw data → parsing → normalization → analytics layer → UI & alerts.
Case: Why one startup burned $500K running its own nodes
A 2022–2023 project insisted on running full nodes for 8 blockchains from day one: Ethereum, BNB Chain, Polygon, Solana, Avalanche, Fantom, Arbitrum, Optimism. Very web3‑maxi, very expensive.
Reality check:
– Node infra ate more than 60% of their burn.
– 70% of user queries were Ethereum‑only.
– 90% of features people actually used didn’t need real‑time mempool data.
When funding tightened in 2023, they had to shut down non‑Ethereum chains overnight. Users were confused; the brand never recovered.
A more pragmatic setup would’ve used managed node providers + specialized blockchain data analytics tools for a subset of chains, and only moved to self‑hosted infra when query volume and latency demands clearly justified it.
Non‑obvious decision: embrace “good enough” latency
Many founders over‑optimize for sub‑second freshness. But:
– A DeFi treasury committee making weekly allocation decisions doesn’t need tick‑level feeds.
– A tax analytics client only needs daily snapshots.
– Even a lot of traders are fine with 30–60 second latency if the signals are truly unique.
The place where ultra‑low latency really matters is high‑frequency market making and execution algos — and those clients usually have their own stack anyway.
Choosing an SLO like “data is updated within 15–30 seconds” lets you heavily cache, pre‑compute aggregations, and drastically cut infrastructure costs, without harming most use‑cases.
Data modelling: turning raw noise into something a PM can explain
Raw on‑chain events are almost unusable for humans. Your job is to translate:
– Addresses → entities (exchanges, funds, smart money, bots)
– Transactions → behaviors (accumulation, distribution, wash trading, liquidity provisioning)
– Events → narratives (someone is quietly exiting; a protocol is leaking TVL; a whale is rotating out of stablecoins)
Alternative method: start from questions, then design the schema
Instead of mirroring blockchain structures, begin with top questions for your chosen niche:
– Investors: “Who is buying this token, and are they historically profitable?”
– Compliance: “Is this counterparty linked to mixers, hacks, sanctioned entities?”
– Protocols: “Which cohort of users is driving 80% of fees, and where did they come from?”
Then design your warehouse tables and feature store around those questions:
– Labels for address types and entities
– Sequenced user journeys (first deposit → first trade → repeat usage)
– Derived metrics: realized PnL per wallet, historical win‑rate, volatility of behavior, risk score.
This question‑first approach sounds obvious but is rarely done. Most teams ship whatever is easiest to calculate, then spend months explaining why users should care.
Building vs buying: don’t reinvent every wheel
The modern stack lets you assemble a lot from existing pieces.
Crypto data API vs running everything yourself
A practical approach in 2025:
– Use a crypto data API for trading firms or multi‑chain indexers for base data: historical candles, trades, liquidity pools, basic labels.
– Layer your own enrichment: advanced entity clustering, unique signals, proprietary scoring.
– Only run dedicated indexers where you truly need custom parsing or ultrafast data.
This hybrid setup lets you launch in months, not years.
And crucially: your moat isn’t “we also index Ethereum logs.” Your moat is how you interpret them, what patterns you detect, and how tightly you align output with users’ workflows.
Product: from cool charts to decisions and dollars
Metrics that actually move the needle
In the last 3 years, successful analytics products in crypto managed to prove one of these:
– “Using us reduced your fraud / hack losses by X%.”
– “Our signals improved your Sharpe ratio by Y.”
– “We cut your analysts’ manual work by Z hours a week.”
If you can’t connect to money saved, money made, or time reclaimed, you’re a “nice to have” that gets cancelled when markets go red.
Case: A small team that beat bigger names
One 4‑person startup in 2022–2024 focused solely on NFT floor price manipulation detection for mid‑size funds and lending protocols. Big analytics suites could show them sales history, volume, holders. But this team built a specialized model for spoofing, bid‑wall games, and coordinated wash trading on specific marketplaces.
They didn’t even have a public dashboard. Just:
– A Slack bot with real‑time alerts
– Weekly PDF briefs with explanations
– Occasional calls with PMs to walk through cases
Within a year they had fewer than 10 clients but an annual contract value north of $500K. Their competitive edge was extreme relevance, not surface area of features.
Distribution: how people actually find crypto analytics tools
The 2021 strategy “tweet threads + Discord + hope” doesn’t cut it anymore. In the 2022–2024 bear cycle, users got picky, and budgets shrank.
Non‑obvious growth channel: embed yourself in someone else’s product
Partner with:
– Custodians and prime brokers who want extra analytics for their clients.
– Wallets that want risk scores or DeFi safety indicators.
– DeFi dashboards needing deeper on‑chain intelligence.
Offer white‑label crypto market intelligence solutions or widgets. You win distribution; they win differentiation. Your logo may be small, but your revenue doesn’t care.
Some of the fastest‑growing analytics plays in 2023–2024 weren’t standalone SaaS at all — they were quietly powering other apps behind the scenes.
Monetization: from free charts to serious contracts
Most founders default to a $19–$49 subscription. That’s rarely where the real money is.
Layered pricing that actually works
A realistic, non‑gimmicky structure:
1. Free / low‑tier
– Delayed data, limited history
– Great for top‑of‑funnel, research, content marketing
2. Pro / small team
– Full history, alerts, exports
– Think $99–$499/month, targeted at active traders, analysts, small funds
3. Enterprise / API
– SLAs, custom features, dedicated support
– Priced based on seats, data volume, or AUM
– Where you make the bulk of your revenue
In 2023–2024, many analytics projects discovered that while retail interest fluctuated wildly with price cycles, B2B demand — especially from compliance and funds — was far more stable. If your roadmap is 100% retail, you’re tying your fate to Bitcoin’s chart.
Team: who you actually need in the first year
You don’t need a 20‑person organization to ship a credible crypto analytics platform. You need a small, complementary squad.
Minimum viable team composition
– Data engineer / infra – builds and maintains pipelines, queries, storage.
– Quant / data scientist – designs metrics, runs experiments, evaluates signals.
– Product‑minded engineer / designer – turns raw metrics into usable UX.
– Domain‑fluent founder or PM – talks to users, understands trading / compliance / DeFi, and owns “why” behind features.
Trying to outsource all domain expertise is risky. The crypto space changes too fast; you need someone inside the team who genuinely understands MEV, L2s, DEX mechanics, or regulation — not just buzzwords.
Stats and trends from 2022–2024 that should shape your strategy
Let’s ground this guide with a few relevant datapoints from the last three years:
– Market cap volatility
– Dec 2022: crypto market cap ~ $850B
– Dec 2023: roughly doubled to ~ $1.7T
– Mid‑2024: fluctuating near $2.4–2.6T
That swing forced funds and institutions to double‑down on risk management and data‑driven allocation, which increased demand for serious analytics rather than meme sentiment.
– Institutional adoption
From 2022 to 2024, the share of crypto volume on regulated exchanges and via institutional desks steadily grew. Spot BTC ETFs in the US and similar products elsewhere attracted billions in AuM within months. Those flows are heavily compliance‑driven and create demand for tools that can justify decisions to risk committees and regulators.
– Regulatory pressure
In 2022–2023, multiple high‑profile enforcement actions (against mixers, exchanges, lending platforms) pushed KYC/AML and transaction monitoring to the front. This catalyzed growth in specialized blockchain data analytics tools focused on sanctions screening, wallet risk scoring, and investigation workflows.
– DeFi, NFTs & L2s
While the 2021 NFT mania cooled, on‑chain derivatives, restaking protocols, and L2 networks grew throughout 2023–2024. This created more complex data environments: fragmented liquidity, cross‑chain bridges, and restaked collateral. Complexity is your friend: it multiplies opportunities for analytics products that untangle the mess.
Pro‑level lifehacks from the trenches
Lifehack #1: Ship “narrow but deep” features
Instead of yet another universal dashboard, build one feature that saves a specific role 5–10 hours a week. For example:
– “Token unlock calendar with predicted sell pressure per fund, not just raw vesting dates.”
– “Stablecoin flow monitor for exchanges, highlighting unusual inflows by entity label.”
Depth beats breadth. A single workflow‑changing module will sell your product better than twenty lukewarm charts.
Lifehack #2: Audit your own signals like a trader
If you serve traders or funds, backtest your signals and show hard numbers:
– Hit rate
– Average move after signal
– Drawdown periods
Even a modest but honest edge is far more convincing than vague marketing language. In a 2023 survey among professional crypto traders (various industry reports), consistent methodology and transparent backtesting outperformed “AI hype” in perceived value by a wide margin.
Lifehack #3: Make export & integration a first‑class feature
Pros rarely live inside your UI. They:
– Pull data into their own notebooks and dashboards.
– Join it with off‑chain metrics (order books, OTC flows, macro data).
– Plug it into trading or risk systems.
If you want serious clients, treat your API, webhooks and CSV/Parquet exports as product features, not afterthoughts. For many funds, your public UI is just a demo for the real product: programmable access.
Putting it all together: a realistic 12‑month roadmap
Here’s how you might structure your first year, avoiding most of the traps seen in 2022–2024.
Step‑by‑step plan
1. Month 1–2: Problem discovery
– Talk to 20–30 potential users in a single niche (e.g., small crypto funds or DeFi treasuries).
– Document concrete decisions they make and where they’re flying blind.
2. Month 2–4: Data spine
– Stand up base infra using reliable node providers and a crypto data API for trading firms or indexers.
– Build a minimalist warehouse with only the tables needed to answer the top 5–10 user questions.
3. Month 4–6: First “sharp” product
– Ship one or two focused analytics modules tightly aligned with those decisions.
– Integrate alerts (email/Slack) and simple exports.
– Onboard 3–5 design‑partners with heavy hand‑holding.
4. Month 6–9: Iterate and prove value
– Measure outcomes (PnL improvement, time saved, risk reduced).
– Kill or rework any feature that users don’t touch weekly.
– Polish reliability: uptime, data freshness, correctness.
5. Month 9–12: Monetize and specialize
– Formalize pricing tiers; sign your first real contracts.
– Decide whether your future is investor analytics, compliance, or protocol health — and focus your brand and roadmap accordingly.
By the end of the first year, your goal isn’t to cover “all of crypto.” It’s to be the default, go‑to crypto analytics platform for a small but valuable slice of the ecosystem.
Final thoughts
A guide to building a crypto analytics startup in 2025 boils down to a few blunt truths:
– Data is abundant; insight and integration are scarce.
– Infrastructure is easier than ever; distribution and focus are the hard parts.
– Big players chase breadth; you can win with depth and specificity.
If you’re willing to obsess over one user type, one painful decision, and one set of on‑chain signals — and you’re ready to dig through three years of messy market cycles to understand what actually matters — there’s still plenty of space to build something enduring in crypto analytics.