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In 2025, analytics is no longer a side function; it is the backbone of execution in Web3.

Protocols that misprice incentives or publish inaccurate treasury data risk not only bad optics but also capital flight, failed governance, and regulatory scrutiny.

Internal builds can cost nearly $1.8 million over five years, compared to around $900,000 for a SaaS solution.

That gap doesn’t just show up on a balance sheet; it shapes how quickly a protocol sees ROI, how many engineers it ties up, and how much risk it carries when scaling across ecosystems.

For teams running DeFi protocols, DAOs, or multi-chain infrastructure, this decision has become one of the most strategic calls to make. Done right, it accelerates growth and credibility. Done wrong, it drags resources into endless maintenance, leaving gaps in compliance, transparency, and trust.

This blog examines the Build vs. Buy dilemma in the context of Web3’s decentralised and real-time data requirements.

Let’s get started.

Why the Build vs Buy Decision Matters in Web3 in 2025

By 2025, Web3 has become irreversibly multi-chain.

With 89+ Arbitrum Orbit chains, the OP Superchain onboarding new rollups every week, and zk-based ecosystems gaining traction, every serious project now operates across multiple environments.

Treasury flows, governance activity, and liquidity are scattered across dozens of rollups and sidechains. A unified view isn’t optional; it has become the baseline for credible operations.

The hidden costs of Web3 data fragmentation show up quickly in the form of mispriced incentives, incomplete risk models, and governance blind spots.

At the same time, the analytics layer itself has evolved. Teams no longer ask for static dashboards; they expect AI-native pipelines that support natural language queries, predictive anomaly detection, and automated reporting.

But AI doesn’t fix bad data; it multiplies its flaws. Incomplete pipelines or inconsistent indexing don’t just slow down decision-making; they produce misleading outputs at scale, turning analytics into an execution risk instead of an advantage.

The pressure from external stakeholders has also intensified. MiCA in Europe and emerging U.S. digital asset rules are setting compliance baselines that protocols must meet.

Capital allocators now treat data maturity as proof of operational discipline. Teams that can’t demonstrate it often fail the first filter for funding or partnerships.

This is why the build vs buy decision has moved to the centre of protocol strategy. It determines whether teams can:

• React in real time to liquidity shifts, market risks, or governance changes.
• Deploy engineering talent toward product growth instead of constant infrastructure maintenance.
• Earn stakeholder trust with analytics that are transparent, verifiable, and defensible under regulation.

In short, the decision is no longer about tooling preference. It is about execution speed, operational resilience, and long-term legitimacy in a multi-chain, AI-driven world.

To understand what’s truly at stake, it helps to break down the trade-offs directly. Here’s how building in-house and buying managed services compare across the dimensions that matter most in 2025.

Should You Build or Buy Web3 Analytics in 2025? A Side-by-Side Comparison

For most Web3 teams, the build vs buy question is less about ideology and more about trade-offs.

Building in-house offers control and customisation, but it comes with high costs in infrastructure, talent, and maintenance. Buying, on the other hand, delivers speed and ecosystem-grade reliability, but may limit flexibility and carry the risk of vendor lock-in.

Here’s how the two approaches stack up across the dimensions that matter most in 2025:

In our full guide, Building the Data Backbone of Web3, we expand this into a complete decision framework with detailed TCO models and architectural trade-offs across different protocol types.

But cost and architecture are only part of the story. The build vs buy choice also carries deeper strategic implications that shape how protocols govern, allocate resources, and compete in fast-moving markets.

Strategic Implications of Building vs Buying Analytics in Web3

The build vs buy choice in Web3 analytics is not just a technical call; it is a strategic determinant of protocol performance.

It shapes how an organisation governs, how efficiently it deploys capital, how quickly it responds to market changes, and how much bandwidth it preserves for innovation.

In other words, analytics decisions flow through to the very core of how protocols sustain legitimacy and growth in a competitive, multi-chain environment.

Let’s see how this plays out across four critical areas: governance, treasury, market agility, and innovation.

How Analytics Decisions Impact Governance Efficiency in DAOs

DAO-driven ecosystems rely on timely, accurate intelligence. Weak or delayed analytics pipelines can distort outcomes and lead to decisions misaligned with community intent.

Example: Tally’s In-House Governance Stack

• Tally, a governance platform serving multiple DeFi ecosystems, partnered with LimeChain to build subgraphs on The Graph protocol.
• This gave them custom indexing of governance contracts and real-time visibility into policy data.
• The trade-off was significant: the system required highly specialised engineers, close alignment with governance stakeholders, and continuous updates to track protocol changes.

Why it matters: Building offers deep customisation but carries long-term upkeep and engineering costs that many teams underestimate.

Why Real-Time Analytics Is Critical for Treasury Management

Treasuries underpin sustainability, incentives, and reserves. If treasury decisions rely on incomplete or outdated data, resources can be wasted on programs that fail to retain value.

• A DeFi protocol launched a liquidity incentive program using outdated inflow/outflow data.
• Unaware that most TVL growth came from short-term capital, it overspent on incentives that failed to drive lasting retention.
• By contrast, managed-service solutions can provide real-time TVL composition; some providers now refresh inflow/outflow data every 30 seconds, enabling treasury teams to deploy incentives where they have the most durable effect.

How Managed Analytics Improves Market Agility in Web3

Web3 markets shift rapidly. The ability to anticipate and respond to changes before they cascade can determine whether a protocol remains competitive.

Example: CanDoo’s Managed Analytics Deployment

• CanDoo, a marketplace for tokenised real-world assets, integrated a managed analytics platform to monitor trades, flag fraud, and trigger compliance alerts. The impact was tangible:
  • 40% increase in marketplace liquidity
  • 50% reduction in manual KYC processing time
  • Higher user retention rates

Why it matters: Managed analytics infrastructure compresses the time from data to action, enabling protocols to capture market opportunities in weeks rather than months.

How Analytics Choices Shape Innovation Speed in Protocols

Engineering resources are limited. Every sprint allocated to maintaining analytics pipelines is one not spent on building product features or ecosystem tools.

• During a major launch week, a gaming protocol diverted core developers to fix issues in its in-house indexer.
• The result: product stability suffered, user retention dipped, and roadmap milestones slipped.

With managed infrastructure, product teams stay focused on growth and features while analytics scale in the background.

A well-structured Web3 Developer Stack complements analytics pipelines by reducing infrastructure overhead and equipping developers with the right tools to focus on product innovation.

The chosen analytics path directly shapes governance responsiveness, treasury efficiency, market positioning, and innovation speed. In volatile, multi-chain environments, analytics are not simply a reporting layer; they are a strategic asset.

There are times when the best way to protect that asset is to keep it fully in-house. While managed services remove much of the complexity, certain protocols find that building their own stack is not only viable but strategically preferable.

When Does It Make Sense to Build Web3 Analytics In-House?

Managed analytics services take away much of the operational burden, but there are situations where keeping analytics in-house is the strategically stronger option.

These are not common, and they require significant resources, but for some protocols, the benefits outweigh the trade-offs.

In most cases, the teams choosing this path are well-capitalised, technically mature, and operating in environments where customisation, sovereignty, or long-term cost control are critical.

For them, analytics is not just a support layer; it is a competitive moat, tightly coupled to the protocol’s core logic and mission.

Building internally can provide:

• Deeper customisation for unique data structures and proprietary models.
• Greater sovereignty in regulatory or competitive contexts where data cannot leave the protocol’s control.
• Potential long-term savings for large teams with the capital and talent to absorb the initial build curve.

Yet this path comes with weighty implications. It means accepting the permanent overhead of maintenance, scaling, and compliance.

For teams still willing to make that trade, the decision is usually driven by one of a few specific factors.

Complex or Non-Standard Data Structures in Web3 Protocols

• Not every protocol can rely on standardised analytics frameworks. Projects with highly bespoke smart contract architectures, proprietary transaction flows, or zk-based rollups often produce data that mainstream platforms can’t decode efficiently.
• For example, privacy-preserving L1s frequently require specialised parsing and indexing to ensure accuracy.
• Relying on vendors in such cases often introduces lag, waiting until providers adapt their infrastructure. Building internally eliminates that dependency, giving the protocol full control over compatibility and performance.

Leveraging In-House Analytics Expertise in Web3 Teams

• Some protocols already employ dedicated data engineering and DevOps teams. For these organisations, the cost of standing up pipelines internally can be absorbed into existing operations.
• The payoff is granular control over system design, custom metric creation, and integrations with internal governance or treasury systems.
• Yet, even here, trade-offs remain: maintaining uptime across multi-chain pipelines, handling protocol upgrades, and optimising for performance demand constant attention.
• What looks efficient on paper can quickly turn into a recurring allocation of scarce engineering cycles that could otherwise accelerate product features.

Why Data Sovereignty Drives In-House Analytics Decisions

• In highly competitive or regulatory-heavy verticals, data control is not optional; it is a necessity.
• Protocols dealing with tokenised securities, on-chain credit scoring, or proprietary economic models cannot afford for critical data to be processed outside their own environment.
• Full control of pipelines, storage, and schemas ensures that sensitive information stays within the protocol’s perimeter, reducing both competitive exposure and compliance risk.
• In such cases, outsourcing isn’t just a trade-off; it’s an unacceptable liability.

When Long-Term Budgets Justify Building Analytics Internally

• When capital and time-to-market pressures are less acute, in-house builds can become more appealing.
• Protocols with multi-year runways, stable talent pipelines, and predictable growth trajectories may accept the slower build curve in exchange for total ownership.
• Over a five-year horizon, some large-scale protocols even reduce the total cost of ownership this way, since vendor fees scale aggressively with usage.
• However, this path only works when leadership is prepared to absorb the engineering drag as a permanent feature of operations.

The Hidden Costs of Building Web3 Analytics In-House

• Even when the above conditions are met, the cost of building in-house is rarely captured in the initial budget.
• Every chain added, every compliance mandate, and every governance change introduces fresh complexity. Each sprint spent debugging or optimising pipelines is a sprint not spent advancing the product, strengthening the ecosystem, or improving governance.

The result is that protocols often underestimate the true, compounding cost of internal analytics stacks and costs that only become visible when they begin scaling into multi-chain environments. That’s why more teams in 2025 are shifting the burden of scaling and maintenance to specialised providers.

This is where managed services change the equation. Instead of absorbing the permanent overhead of maintenance and scaling, teams can shift that burden to specialised providers.

For most protocols in 2025, this path delivers faster insights, lower long-term risk, and the ability to keep engineering talent focused on growth.

Why Managed Web3 Analytics Services Are Essential in 2025

While in-house builds offer control and sovereignty, the reality for most teams in 2025 is that managed analytics services unlock greater leverage.

These solutions compress time-to-market, reduce long-term overhead, and allow scarce engineering talent to stay focused on protocol growth.

For DAOs, DeFi protocols, and L2 ecosystems operating in volatile, multi-chain environments, buying isn’t just about convenience; it is often the only way to stay competitive.

How Managed Analytics Delivers Faster Time to Insight

• Markets and governance cycles move in days, not quarters. Setting up internal pipelines often takes months of work: deploying nodes, designing schemas, building ETL, and validating indexers.
• Managed providers offer ready-to-use APIs, dashboards, and developer tools that shrink this timeline from months to weeks or even days.
• The speed advantage matters: protocols with rapid visibility into treasury flows, user activity, or liquidity shifts can respond strategically while others are still building out their data foundations.

The Cost Advantage of Managed Web3 Analytics vs In-House Builds

• The economics are increasingly clear. Internal builds can cost nearly $1.8 million over five years, while managed solutions typically average around $900,000 over the same horizon.
• For many protocols, that difference can fund incentive programs, grants, or product development initiatives with a direct impact on growth.
• Subscription models also provide cost predictability, enabling better treasury planning. While fees can scale with usage, the ability to budget against known tiers is often more sustainable than absorbing the hidden and variable overhead of internal teams.

How Managed Web3 Analytics Supports Compliance and Reporting Standards

• With regulations like MiCA in Europe and increasing oversight in the U.S., protocols are under pressure to produce auditable, institution-grade reporting.
• Many vendors now offer compliance-ready features such as standardised schema design, automated audit logs, and verifiable data trails.
• For DAOs managing treasuries or protocols engaging institutional partners, outsourcing these pipelines reduces the risk of reporting gaps and provides credibility when facing regulators or investors.

Why Managed Services Scale Better Across Multi-Chain Ecosystems

• In 2025, multi-chain is the default state. Managed providers such as Goldsky, Flipside, Subsquid, and Envio now support 50+ rollups, sidechains, and modular stacks out of the box, coverage that would take most internal teams years to replicate.
• Integrating this level of coverage internally would require permanent teams of engineers and significant infrastructure spend. Buying ensures broad ecosystem visibility without the need to reinvent pipelines for each new chain.
• For protocols expanding across multiple rollups, the breadth of coverage alone can determine whether analytics scales in step with the product.

Managed services shift analytics from a bottleneck into a growth enabler. They reduce operational drag, free internal teams from maintenance, and deliver a level of resilience that is difficult for most protocols to replicate.

For organisations competing in fast-moving, multi-chain environments, managed analytics are often the difference between reacting to change and leading through it.

But the most resilient teams in 2025 aren’t choosing only one path. Instead, they combine the strengths of both approaches, building where sovereignty or customisation is critical, and buying where speed and scale deliver leverage.

The Hybrid Playbook for Web3 Analytics: What Works in 2025

By 2025, the build vs buy debate has largely converged on a middle ground. Most leading protocols don’t commit exclusively to one approach; they design hybrid stacks that combine the control of in-house infrastructure with the speed and coverage of managed services.

This blended model reflects both the realities of multi-chain operations and the strategic need to optimise scarce engineering capacity.

Which Analytics Components Should Protocols Keep In-House?

Protocols often reserve in-house resources for components that directly touch their core logic or carry competitive risk. For example:

• Risk management dashboards in lending protocols, where latency and precision are critical.
• Custom data models for on-chain credit scoring or proprietary trading strategies.
• Governance monitoring, where DAOs want to verify vote data against their own internal nodes.

By keeping these pipelines internal, teams ensure security, sovereignty, and alignment with their unique protocol design.

Which Analytics Layers Are Best Outsourced to Managed Services?

At the same time, teams increasingly rely on managed services for broad, resource-intensive layers of the stack:

• Cross-chain coverage across rollups, sidechains, and appchains.
• Compliance-grade reporting, where vendors integrate standardised schema and audit-ready logs.
• Ecosystem dashboards enabling community members to explore metrics without requiring internal data engineering capacity.

This allows protocols to focus internal teams on high-value tasks while leveraging providers for scalability and reliability.

Examples of Hybrid Analytics Adoption in Web3 Protocols

• DeFi Treasuries: Many protocols now run public dashboards on platforms like Dune v2 or Flipside to ensure transparency, while maintaining private data marts in-house to analyse sensitive financial flows.
• L2 Ecosystems: Rollups such as Arbitrum Orbit chains invest in native economic analytics internally, but depend on external providers to give their developer communities easy-to-use, standardised datasets.
• DAOs: Governance platforms frequently combine internal subgraphs for decision-critical voting data with external analytics for community reporting and transparency.

In practice, the most resilient teams in 2025 are not asking ‘build or buy?’; they are asking ‘what should we build, and what should we buy?’

The real advantage comes from how that mix is executed because analytics is no longer just infrastructure, it is the engine of legitimacy and growth

Turning Data into a Strategic Advantage

In Web3, analytics has shifted from being a reporting layer to becoming a protocol’s competitive edge.

The choice between building in-house and buying managed services is not just about infrastructure efficiency; it determines how a protocol proves legitimacy, captures opportunities, and sustains growth.

Teams that treat analytics as a core strategic asset are already pulling ahead. They use data pipelines not just to measure outcomes but to:

• Strengthen governance: ensuring community votes reflect real activity.
• Optimise treasury use: deploying incentives where they create durable value.
• Stay market-ready: reacting to liquidity shifts and compliance demands in real time.
• Accelerate innovation: keeping engineers focused on features instead of maintenance.

For teams ready to design a resilient data foundation, our free guide “Building the Data Backbone of Web3” provides checklists, design blueprints, and reference architectures for protocols building at scale.

And for those prepared to operationalise, our Web3 Data Analytics service delivers these principles as production-ready infrastructure, so your team can focus on growth while we handle the data backbone.

Astha Baheti

Growth Lead

Astha Baheti is the Growth Lead at Lampros Tech, a Blockchain development company helping businesses thrive in the decentralised ecosystem. With an MBA in Marketing and hands-on experience in digital marketing and content strategy, she brings expertise in crafting clear, impactful communication that aligns business goals with audience needs. At Lampros, Astha focuses on translating complex Web3 concepts into accessible narratives that drive engagement and awareness.

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