Nansen vs Arkham: Wallet Labels, Research Workflows and Pricing

Nansen vs Arkham at a glance

Nansen and Arkham both transform raw blockchain data into something more useful than a block explorer, yet their research language is different. Nansen frequently organizes intelligence around wallets as economic actors: Smart Money, funds, profitable traders, token holders, PnL, inflows, DEX activity and behavioral classifications. Arkham organizes much of the experience around addresses grouped into entities, then lets the researcher inspect portfolios, transfers, historical balances, counterparties and graph relationships between those entities.

The platforms overlap enough that the same wallet can be investigated in both. The difference appears in the questions each interface makes easy. Nansen tends to answer “what are high-performing or categorized wallets doing?” Arkham tends to answer “who is this address associated with, what else is connected to the entity, and how did funds move through the network?”

Nansen vs Arkham pricing in 2026

Nansen simplified its consumer subscription structure in 2026. The current product has a Free tier and a single paid Pro tier. Pro costs $49 per month when billed annually, equivalent to $588 per year, or $69 when paid month-to-month. The current Pro package includes full labels and PnL, including Smart Money data, unlimited portfolios, unlimited Smart Alerts, access across supported chains, mobile access and a monthly allocation of API credits.

The Free tier remains meaningful rather than functioning solely as a checkout screen. Users can explore the product, and Free currently supports one active Smart Alert at a time. Premium behavioral labels and higher-volume research are where Pro becomes materially more useful. If the objective is regular wallet profiling, Smart Money research and persistent alerting rather than occasional address lookup, the $49–$69 monthly price becomes the relevant Nansen cost.

Arkham has a different cost decision

Arkham's Intel platform does not currently present the same simple “Free versus $X Pro” decision for ordinary web research. Users can register for the Intel platform and access the explorer, entity profiles, transaction views, Visualizer, Tracer, dashboards, filters and alerts without first buying a Nansen-style premium analytics subscription. That makes Arkham particularly attractive when a researcher needs occasional entity analysis or tracing and cannot yet justify another recurring research bill.

Arkham's commercial cost becomes more relevant when the workflow moves into API integration, higher-volume data consumption, compliance use or another negotiated subscription. Its 2026 Intel API provides entity labels and fund-flow data programmatically, and Arkham currently offers a 30-day API trial through its access process. Public materials do not expose one simple consumer-style monthly API price that should be inserted into a comparison table as though every organization pays the same amount, so the responsible treatment is to request current commercial terms for the intended API workload.

Nansen API pricing is separate from the Pro subscription

Nansen's API can currently be used without a Pro subscription. API credits cost $10 per 10,000 credits, and endpoint consumption depends on the type of data requested. Standard wallet balances, transactions and several token analytics calls are inexpensive compared with proprietary label endpoints. Label retrieval is intentionally expensive because the attribution layer is one of Nansen's core proprietary datasets.

Current account guidance gives Free users a small initial credit allocation and daily refresh, while Pro receives 2,000 API credits per month and a higher API rate limit. This matters if you are evaluating Nansen for a product rather than manual research. The $49 or $69 subscription should not be mistaken for unlimited API access.

A wallet label is not the same as verified attribution

This is the most important analytical rule in the entire comparison. A label is a statement about an address. Attribution is the evidence-backed conclusion that the statement accurately links that address to a person, organization, service, strategy or behavioral cohort. Those two things can overlap, but they are not automatically equivalent.

A blockchain analytics provider can label an address “Exchange,” “Fund,” “Smart Trader,” “Market Maker,” “Bridge,” “Deployer” or with the name of a real-world entity. Each label type carries a different evidentiary burden. “This address interacted with Uniswap” can be established directly from contract interactions. “This wallet belongs to Company X” requires a stronger attribution chain. “This wallet is a Smart Trader” may be a proprietary behavioral classification derived from performance criteria rather than an assertion of real-world identity.

Use an evidence key instead of treating every label equally

This distinction is also why raw label-count marketing should be interpreted carefully. Nansen currently describes a database containing hundreds of millions of labeled addresses, while Arkham has published counts in the billions for address labels or tags and hundreds of thousands of entity profiles. Those figures are not measurement-equivalent. One platform may count address-level tags, another labeled wallets, another behavioral classifications and another clustered entity records. Bigger numbers do not automatically mean better attribution for your specific wallet.

TokenToolHub's Entity Resolution for Wallets guide is useful background before using either platform for high-stakes attribution. The related Basics of Wallet Clustering guide explains why transaction proximity, common counterparties and behavioral similarity can suggest relationships without proving common ownership.

Question-by-product evidence matrix

Shared wallet investigation fixture: vitalik.eth

A fair Nansen-versus-Arkham investigation should use the same public wallet, the same written questions and the same evidence key. For this methodology, use Ethereum address 0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045, commonly displayed as vitalik.eth. The purpose is not to rediscover a famous identity. It is to test how each product turns one known address into additional research without confusing vendor intelligence with independent proof.

An ordinary Ethereum explorer can independently establish the address, its transactions, token transfers, contract interactions and ENS-facing context. Those facts become the baseline. Nansen and Arkham should then be evaluated only on the additional research value they contribute: labels, related addresses, counterparties, behavioral classifications, entity context, visual flow analysis, alerts, portfolio history and exportable evidence.

Write the investigation questions before opening either product

Record evidence, not screenshots alone

For each finding, record the exact address, chain, label text, date observed, platform, relevant transaction hashes and why the evidence matters. If Nansen labels an address as a Smart Trader, note that this is a Nansen behavioral classification. If Arkham groups several addresses under one entity, record that as Arkham's entity model until another source corroborates the grouping.

For a repeatable comparison, also record whether the same piece of information can be reproduced after logging out, whether it requires Nansen Pro, whether Arkham exposes it in the free Intel account, and whether it is available programmatically through the respective API. The research product is more valuable when a critical finding can be retained and audited rather than disappearing inside a transient dashboard.

Where Nansen is strongest

Nansen's differentiator is not simply that it puts names beside wallets. Its higher-value workflows combine identity-style labels with behavior. A researcher can move from a wallet to PnL, token holdings, DEX trades, counterparties, related wallets and Smart Money classifications, then step outward into token-level flow analysis. This makes it particularly useful when the question begins with market behavior rather than forensic identity.

Suppose a token rallies sharply and you want to know whether experienced traders accumulated before the move. A basic explorer can show transfers and swaps, but it does not naturally tell you which participants have historically been profitable. Nansen's Smart Money layer is designed for that query. Its current API exposes categories including Fund, Smart Trader and several time-window performance classifications, while product workflows use those labels to filter flows, holdings and DEX activity.

Smart Money is a classification, not a guarantee

A wallet categorized as Smart Money can still lose money on its next trade. The useful information is that the wallet met Nansen's behavioral criteria, not that its future decisions deserve imitation. This is especially important when a wallet's historical performance came from strategies, private allocations or hedges that are invisible from the single transaction you are watching.

The same caution applies to PnL. Cost basis can become difficult when assets move between wallets, bridge across chains, originate from OTC transactions or enter through addresses outside the observable cluster. PnL is extremely useful for research, but it should be read as an analytical model rather than a perfect accounting statement about the human behind the wallet.

Token research is where Nansen compounds its advantage

The wallet and token views reinforce each other. A researcher can inspect a token's holders, Smart Money activity and flows, then profile an interesting address without leaving the same analytical environment. This is a strong fit for investment research, market monitoring and finding wallets worth following over time.

If your own workflow starts earlier—with a suspicious contract rather than a known wallet—TokenToolHub's Token Safety Checker can be used first on supported EVM networks to inspect contract-side evidence. Nansen then becomes more useful when the question changes from “what can this contract do?” to “who holds it, who is trading it, and what are labeled cohorts doing?”

Where Arkham is strongest

Arkham feels more like an intelligence workspace built around entities and movement. Search an address, token, transaction or named entity, then move through portfolio, historical balance and transfer data. Visualizer helps map relationships, while Tracer is designed for following multi-stage movement from source toward subsequent addresses, services or bridges. The result is a natural workflow for investigations where sequence and association matter.

Private labels are particularly useful for researchers who develop their own hypotheses. Arkham allows a user to create private labels and private entities, including grouping multiple addresses across supported address families into a custom entity. Those private designations can override the interface's standard labels for the user's own investigation without requiring a public attribution claim.

Visualizer and Tracer solve different cognitive problems

A transaction table is precise but quickly becomes difficult when dozens of counterparties are involved. Visualizer helps the analyst see connections between addresses or entities as a network. Tracer is better suited to chronological flow following: money leaves one address, splits, passes through another service and continues to a subsequent wallet. Filters reduce unrelated transfers so the investigation can remain centered on the value being traced.

This becomes particularly useful around bridge exploits, exchange flows, treasury movements and stolen-fund investigations. The analyst can follow the public path without manually opening dozens of block-explorer tabs. The graph remains an investigative aid, however; proximity in a graph is not proof that two nodes have the same owner.

Arkham's entity profiles reduce repetitive address work

If an entity profile groups many addresses under a single organization, the researcher can inspect a consolidated portfolio and transfers without reconstructing the cluster manually. That can save significant time when monitoring exchanges, funds, public treasuries or known institutions. It also raises the importance of provenance because a mistaken cluster can propagate a wrong assumption across every aggregated metric.

For this reason, use Arkham's entity attribution as a powerful lead rather than a prohibition against asking questions. When the identity matters materially, inspect the associated addresses, look for first-party confirmation and compare the conclusion with another data source.

Nansen Smart Alerts vs Arkham Alerts

Alerts are one of the strongest reasons to use an analytics platform instead of manually revisiting a wallet every hour. Nansen currently lets Free users keep one active Smart Alert and gives Pro users unlimited Smart Alerts. Its newer AI-assisted alert workflow can create conditions around wallet activity, Smart Money token flows and supported derivatives behavior, with delivery options including Telegram, Discord and Slack.

Arkham Alerts are structured around highly configurable transaction filters. Conditions can include addresses or entities, sending and receiving entities, token, token amount, USD value and chain. Current Arkham guidance supports notifications through email, Telegram and webhooks, making it well suited to operational monitoring or sending activity into another system.

Alert count is only one part of the test. Measure delivery latency, false positives, duplicate notifications and whether a trigger can be recreated after an investigation. A theoretically unlimited alert system that produces too much noise becomes less useful than a smaller set of tightly defined conditions.

Exports, saved investigations and research retention

A research product is more valuable when findings can leave the product. Analysts need transaction lists for reconciliation, wallet records for a case file, or API output for a reproducible notebook. Nansen supports portfolio and CSV-oriented workflows and has historically imposed row limits according to product tier and widget type. Because Nansen substantially restructured its plans in 2026, researchers should confirm the current export limit inside the exact Pro interface instead of relying on an older Professional-plan limit.

Arkham's strongest retention workflow is not merely CSV. Dashboards can group custom metrics, private labels preserve the analyst's own naming layer, Visualizer and Tracer can preserve investigative context, and alerts continue monitoring after the manual session ends. Its API is the stronger route when a team needs systematic extraction rather than occasional manual export.

For procurement, test three things directly: whether the table you care about can be exported, how many rows are included, and whether the exported records preserve identifiers such as chain, transaction hash and address. A visually impressive dashboard is less useful for repeatable research if its underlying evidence cannot be reconstructed later.

Nansen API vs Arkham Intel API

Nansen's current API is unusually accessible for experimentation. Users can create an API key without buying Pro, purchase credits directly, and query endpoints covering wallet balances, transactions, counterparties, related wallets, PnL, token flows, Smart Money and labels. Credit costs vary dramatically: foundational data is cheap, while proprietary label endpoints consume far more credits. This lets a developer decide precisely where Nansen's intelligence adds enough value to justify paying for it.

Nansen also supports pay-per-call access through x402 for certain workflows, allowing applications or AI agents to pay in USDC for individual data calls instead of maintaining a conventional subscription. That is useful when an agent runs intermittently and the cost of maintaining a monthly software seat would exceed the actual query volume.

Arkham's 2026 Intel API similarly exposes entity labels and fund-flow intelligence for programmatic use. Its intelligence updates feed now publishes new, changed and deleted address intelligence within minutes rather than through a once-daily update cycle. Arkham has also added x402 support, allowing AI-agent workflows to pay for compatible API requests with USDC.

The APIs are not interchangeable data commodities

An address-balance endpoint is relatively easy to replace because the underlying balances come from public chains. A proprietary Smart Money label, Arkham entity grouping or vendor-generated risk score cannot be reproduced merely by changing the URL. If you build an application around one provider's derived intelligence, treat the taxonomy and attribution model as a product dependency.

This is the point where TokenToolHub's broader Crypto Research Tools Stack becomes relevant. Good research infrastructure combines raw blockchain evidence, contract analysis, wallet intelligence, market context and provider-specific enrichment rather than expecting one vendor to answer every question.

Coverage counts are useful, but they are not a buying decision

Nansen currently describes hundreds of millions of labeled addresses and supports analytics across a broad multi-chain set including Ethereum, Solana, Base, BNB Chain, Arbitrum, Avalanche, Polygon, Optimism, Hyperliquid and newer ecosystems. Arkham publishes much larger raw label or tag counts and hundreds of thousands of entity profiles. Arkham's 2026 product announcements have cited billions of address labels and large numbers of deanonymized traders.

Those numbers sound comparable but are not normalized. An address can carry multiple tags. An entity can contain many addresses. A behavioral label can coexist with an attribution label. A provider can add billions of low-level tags without necessarily having better named-entity coverage for the ten wallets you care about. Procurement should therefore sample your own corpus rather than compare two marketing counters.

Build a 50-address acceptance corpus

Select addresses from the actual work you perform: five exchanges, five bridges, five funds, ten active traders, five protocol treasuries, five token deployers, five obscure wallets and ten previously investigated counterparties. For each address, record whether the platform supplies an entity name, behavioral label, cluster, historical balances, counterparties and alerts. Then record whether those results can be independently corroborated.

This produces a coverage metric that matters. If Nansen resolves 42 of the 50 addresses useful to your trading research and Arkham resolves 35, that tells you more than global label counts. If Arkham clusters addresses more usefully in exploit investigations while Nansen provides much better trader classifications, you may discover that both products belong in different phases of the same workflow.

Related wallets and clustering: use inference carefully

Wallet clustering is valuable because sophisticated actors rarely operate from one address forever. They use deposit addresses, trading wallets, cold wallets, bridges and newly funded accounts. Analytics platforms try to reconnect this fragmented activity through transaction patterns, service relationships, address reuse, deposit behavior and proprietary heuristics.

The danger is turning an inference into an identity fact. Two wallets can receive funds from the same exchange without having the same owner. Several traders can interact with the same contract in similar ways. A project can use a third-party market maker whose wallets should not be merged into the project's treasury entity. Clustering should therefore carry confidence and provenance.

Nansen exposes related-wallet and counterparty workflows that can surface additional addresses around a wallet profile. Arkham's entity model can aggregate multiple addresses under one named entity, and private entities allow a researcher to construct a separate hypothesis without overwriting the public attribution. Those are both powerful features when the analyst remembers that “related” and “same beneficial owner” are different conclusions.

Risk scores, suspicious exposure and compliance use

Arkham added Risk Scores to its Intel API in 2026 as a paid add-on. The system assigns a score from 0 to 100 based on exposure to known or suspected illicit activity and accompanies the score with a briefing describing relevant categories and connected risky addresses. That can be useful for counterparty monitoring and investigative prioritization, especially when combined with Arkham's entity graph.

A numerical risk score should still not become an automatic verdict. Indirect exposure can arise through exchanges, bridges, smart contracts and other shared infrastructure. A wallet that interacted with a service later associated with malicious activity is not necessarily controlled by the malicious actor. Investigators should inspect the path, direction, amount, timing and economic context behind the score.

TokenToolHub's Wallet Risk Scanner can provide another public-address view for supported wallets before you decide whether a deeper paid investigation is justified. Use the scan as a screening layer, then escalate to Nansen, Arkham or another specialized system when the missing evidence requires richer entity attribution or flow analysis.

When to verify vendor intelligence with raw chain data

Not every label deserves a second infrastructure stack. If you are tracking a trader's token purchases for personal research, the cost of independent node verification may exceed the value of the conclusion. If you are preparing an institutional report, investigating a security incident or making a decision that depends on one disputed attribution, raw transaction verification becomes much more important.

The workflow is straightforward: record the address and transaction identifiers from the analytics platform, query the underlying chain independently, and confirm the actual transfers, logs or contract calls. This does not independently prove a person's identity, but it separates the public blockchain facts from the vendor's interpretation of those facts.

Teams that do this programmatically can run their own nodes or use a separate RPC provider. Chainstack can fit an optional raw-data verification layer when an investigation pipeline needs direct multi-chain RPC access rather than another derived-intelligence product. The purpose is not to replace Nansen or Arkham; it is to keep the underlying transaction evidence independently queryable.

A practical Nansen + Arkham research workflow

The products are often more useful as complementary lenses than as mutually exclusive databases. Begin with the question and use the cheapest reliable source that can answer it. Escalate only when the missing evidence justifies another tool.

Example: investigating a token accumulation pattern

Start with the token contract rather than searching for a narrative. Confirm the contract on the correct chain and inspect whether there are obvious contract-side issues. Then use Nansen to ask which labeled or Smart Money wallets accumulated, whether flows changed and how relevant holders have historically performed. The strongest output is not “Smart Money bought, therefore bullish”; it is a list of wallets, amounts, timing and classifications that can be checked.

Next, use Arkham when one wallet becomes particularly important. Inspect its entity profile, transfer history, related entity context and Visualizer or Tracer relationships. If the wallet received funding from another cluster before accumulating the token, follow that path. If Arkham groups the wallet under a named entity and Nansen uses a different classification, record both rather than choosing the more exciting label.

Example: investigating a suspicious transfer

Begin with the public transaction and recipient address. TokenToolHub's Wallet Risk Scanner can establish a first screening layer where supported. Arkham becomes useful for tracing onward fund movement through multiple addresses and entities. Nansen becomes useful if counterparties, PnL, behavioral labels or trading history are needed to understand what type of actor the wallet resembles.

The research ends when the question is answered—not when every available dashboard has been opened. An efficient workflow knows when another source would only repeat information already established.

Total cost: subscription price versus analyst time

The obvious Nansen cost is $49 per month on annual billing or $69 month-to-month. Arkham's core web research surface has a lower recurring entry cost for ordinary users, which gives it a significant advantage for occasional investigations. But software price is not the only cost. Analyst time spent reconstructing a workflow manually can exceed the subscription difference quickly.

Suppose a researcher spends three hours each week manually finding profitable token holders, computing wallet performance and revisiting the same wallets for new activity. If Nansen's labels, PnL and Smart Alerts reduce that to one hour, the software does not need to generate a trading profit to have operational value. Conversely, if you investigate wallets only twice per month and Arkham already exposes the entities and flows you need, paying for Nansen Pro may simply duplicate information.

API economics are a separate budget

A manual researcher and a software product should not use the same cost model. Nansen API credits are consumed according to endpoint value, with label endpoints costing far more credits than ordinary balance or transaction calls. Arkham's API is separately commercialized and includes intelligence that is not equivalent to querying a raw RPC node. Build an API budget from actual endpoint calls, not from the monthly price of the human-facing website.

If a product merely needs balances and transactions, buying expensive proprietary labels for every request is wasteful. Query raw chain data for commodity facts and reserve Nansen or Arkham intelligence for the cases where entity or behavioral enrichment actually changes the output.

Migration and exit constraints

Human researchers have relatively low platform lock-in if they preserve their own case notes. An address remains an address, and public transactions remain available regardless of which analytics interface you use. Lock-in increases when your notes contain platform-specific labels without the underlying transaction evidence, or when saved dashboards and private entities exist only inside one account.

API integrations create stronger dependencies. A Nansen Smart Money classification has no guaranteed one-to-one equivalent in Arkham. An Arkham entity ID, risk score or proprietary address cluster has no guaranteed Nansen equivalent. Migrating the endpoint therefore does not migrate the semantics of your product.

Store provider name, observation time and original label alongside normalized fields in your own database. Instead of saving only “entity = Fund X,” retain “provider = Arkham, vendor entity = Fund X, observed = date, corroboration status = unresolved/confirmed.” That small design choice preserves provenance and allows later corrections without rewriting history.

When neither Nansen nor Arkham should be your only source

Who should choose Nansen?

Nansen is the clearer fit for a trader, fund researcher or market analyst who repeatedly begins with behavioral questions. If you want Smart Money inflows, profitable-wallet classifications, token-holder behavior, wallet PnL, counterparties and alerts in one workflow, the current Pro price is far more accessible than Nansen's previous premium structure.

It is also a strong choice when wallet research needs to connect quickly to token-level analysis. Instead of starting from one known entity, you can start from a token, find notable holders or labeled cohorts, then move into wallet profiles. That direction—from market opportunity toward addresses—is central to Nansen's value.

The case is weaker if you mainly need occasional named-entity lookups or visual flow tracing. Use the Free account first. Upgrade only when premium behavioral labels, unlimited alerts, portfolio workflows or repeated use save enough time to justify the recurring charge.

Who should choose Arkham?

Arkham is particularly compelling for researchers who think in entities, transfers and investigative graphs. If you start from a known wallet or organization and need to understand associated addresses, counterparties, balances, historical movements and downstream fund flows, the workflow is direct. The ability to create private labels and entities also lets a researcher preserve hypotheses without pretending they are platform-wide facts.

The free-access nature of the core Intel surface is strategically important. Journalists, independent sleuths, retail researchers and small security teams can perform meaningful entity investigations before committing to commercial API access. That makes Arkham a sensible first stop even for users who later decide Nansen Pro adds better behavioral context.

Arkham becomes a different procurement decision when the Intel API, risk scoring or large-scale intelligence ingestion is required. At that point, request current API commercial terms and test the specific data fields your organization needs rather than extrapolating the cost of the web interface.

A practical buying checklist

Conclusion: Nansen and Arkham answer different parts of the wallet-research problem

The most useful conclusion from a Nansen vs Arkham comparison is that the products should not be reduced to “which one has more wallet labels?” Label quantity is only one part of a research system. The analyst needs coverage, context, provenance, monitoring, exportability and a workflow that turns an address into an answer without hiding uncertainty.

Nansen's clearest advantage is behavioral intelligence. Smart Money classifications, wallet PnL, holdings, DEX activity, token flows and related analytics make it well suited to questions about how experienced or historically profitable market participants are behaving. Its current Pro restructuring also changes the buying decision substantially: $49 per month on annual billing or $69 month-to-month is much more accessible for an individual researcher than Nansen's older professional pricing.

Arkham's advantage is the entity-investigation experience. Search an address, inspect its entity context, follow transfers, visualize relationships, trace multi-stage flows, build private labels and create monitoring around the case. The fact that meaningful Intel functionality is available through a free account means researchers can establish whether Arkham solves their recurring problems before making a separate API procurement decision.

The products also define intelligence differently. Nansen's behavioral classifications can answer questions a conventional entity database cannot answer naturally: which wallets have historically traded profitably, what Smart Money is buying, or how labeled cohorts are moving through a token. Arkham's entity graph can make a complicated movement of funds far easier to understand than scanning isolated wallet profiles one by one.

Neither advantage turns a label into proof. If Nansen says a wallet belongs to a behavioral class, preserve that as Nansen's classification. If Arkham groups an address into an entity, preserve Arkham as the source. Then ask what can be independently corroborated. Public transactions, contract calls and token transfers can be checked directly. Real-world ownership may require first-party disclosure, signed proof or strong independent sourcing.

This discipline becomes especially important when the platforms disagree. The wrong response is to choose whichever label sounds more authoritative. Record both outputs, inspect the evidence that is publicly reproducible and leave the identity unresolved if necessary. Uncertainty is a valid research result.

The shared wallet fixture in this guide is designed around that principle. Start from a known public address such as vitalik.eth, write the questions first, and record what each product adds beyond an ordinary explorer. Does one platform expose more useful counterparties? Does another classify the wallet behavior more meaningfully? Can the suggested related wallets be corroborated? Can an alert monitor the event you care about? Can you retain the evidence after the dashboard session ends?

If your work begins with a token rather than a wallet, a different sequence can be more efficient. Inspect the contract first. TokenToolHub's Token Safety Checker can provide contract-side context on supported EVM networks. Once the question becomes holder behavior and capital flows, Nansen becomes more relevant. Once a specific entity or flow path becomes important, Arkham can add an investigation lens.

If your work begins with an unfamiliar public wallet, use TokenToolHub's Wallet Risk Scanner as an initial screening step where supported. That can help determine whether there is enough activity or risk context to justify opening a deeper paid research workflow. A screening tool and an intelligence platform solve different levels of the problem.

For a full-time trader, researcher or fund analyst, Nansen Pro is easier to justify when Smart Money and PnL questions recur every day. The software is not valuable because it knows that transactions exist; block explorers already do that. It is valuable when proprietary classifications and integrated analytics reduce the time required to find and prioritize meaningful actors.

For an investigator, journalist or security researcher following named entities and transfers, Arkham can be the more natural starting point. Its entity profiles, Visualizer, Tracer, dashboards and private labels map well to case-building. The free entry point also lowers the cost of keeping it available for occasional deep investigations even if another platform handles day-to-day market research.

For a software team, compare the APIs separately from the web products. Nansen uses a published credit system with higher costs for proprietary intelligence such as labels. Arkham's Intel API has become more capable in 2026, including near-real-time intelligence updates and optional risk scoring, but its commercial procurement should be based on the API workload rather than assumptions about free web access.

Finally, preserve the raw evidence underneath the enrichment layer. If an attribution materially affects a report, counterparty decision or security response, query the underlying transactions yourself or through an independent RPC source. A provider such as Chainstack can fit that separate raw-data verification path when programmatic blockchain access is required. Derived intelligence should add meaning to the chain, not replace the chain as the source of transaction facts.

The practical buying decision is therefore straightforward. Start with Arkham if you need an inexpensive way to investigate entities and fund flows. Add Nansen Pro when recurring Smart Money, behavioral labels, wallet PnL and token-flow research save enough analyst time to justify the subscription. Use both when the same investigation genuinely needs two different lenses, and treat disagreement between them as a reason to investigate further rather than a nuisance to hide.

FAQs

References and primary documentation

• Nansen product and on-chain intelligence platform
• Nansen Pro features and current subscription pricing
• Nansen API access, credits and rate limits
• Nansen address-label API documentation
• Arkham Intel platform
• Arkham Intelligence Platform documentation
• Arkham Alerts documentation
• Arkham Private Labels and Entities documentation
• Arkham Intel API
• Ethereum explorer record for the public vitalik.eth investigation fixture

Prices, plan limits, label counts, chain coverage, alert capabilities, API credit rules and export behavior can change. Verify current account-level limits before purchase. Wallet labels and entity clusters are research signals, not automatic proof of beneficial ownership, legal identity, criminal activity or investment quality. Preserve the underlying transaction evidence and clearly distinguish verified facts from vendor attribution and analyst inference.