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Published On Sep 15, 2025
Updated On Sep 15, 2025
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.
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:
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.
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.
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.
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
Why it matters: Building offers deep customisation but carries long-term upkeep and engineering costs that many teams underestimate.
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.
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
Why it matters: Managed analytics infrastructure compresses the time from data to action, enabling protocols to capture market opportunities in weeks rather than months.
Engineering resources are limited. Every sprint allocated to maintaining analytics pipelines is one not spent on building product features or ecosystem tools.
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.
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:
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.
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.
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.
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.
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.
Protocols often reserve in-house resources for components that directly touch their core logic or carry competitive risk. For example:
By keeping these pipelines internal, teams ensure security, sovereignty, and alignment with their unique protocol design.
At the same time, teams increasingly rely on managed services for broad, resource-intensive layers of the stack:
This allows protocols to focus internal teams on high-value tasks while leveraging providers for scalability and reliability.
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
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:
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.
Growth Lead
FAQs
Internal builds can cost nearly $1.8 million over five years, while managed solutions average around $900,000 over the same period.
Building is viable for large, well-funded teams with unique data structures, strong in-house engineering talent, or strict sovereignty requirements.
The main risks are potential vendor lock-in and less flexibility in custom metrics. However, most providers now support hybrid models to reduce this risk.
Key providers include Goldsky, Flipside, Subsquid, and Envio, all of which support multi-chain coverage across rollups and modular stacks.
Because data maturity is seen as proof of operational discipline. Teams that can’t demonstrate reliable analytics often fail the first filter for funding or partnerships.
