Executive summary: build vs. buy AI agent integrations in 90 seconds

This framework helps you decide whether to build, buy, or go hybrid for AI agent integrations. You'll evaluate your needs against strategic importance, cost, and complexity. An integration platform gives you pre-built connectors and managed authentication so you can add tools to your agent faster.

• Build when an integration differentiates your core product and you've got dedicated resources for its full lifecycle (maintenance, security, API updates).

• Buy when you need speed to market, broad tool access, and want to hand off the complexity of managed authentication and maintenance.

• Hybrid when you need to build one or two strategic integrations but want a platform for the long tail of other requested tools.

• Use our scorecard: A score of 6-12 suggests build, 13-22 suggests hybrid, and 23-30 suggests buy.

• Key factors: The decision usually comes down to managed authentication complexity, how many tools you need access to, and the high recurring cost of maintenance.

What does build vs. buy mean for AI agent integrations?

Think about this decision in the context of AI agents specifically. It's different from generic SaaS integration.

Building means writing custom code from scratch to connect your AI agent to a single third-party API. Your team owns everything: handling complex authentication flows like OAuth 2.0 for each end-user, mapping data, managing API-specific errors, and keeping up with maintenance as the target API evolves.

Buying means using a third-party integration platform designed for agents. These platforms provide pre-built connectors, managed authentication infrastructure, and developer-friendly SDKs. You can connect your agent to hundreds of tools through a unified framework, and the platform handles the connectivity and security work for you.

Build vs. buy scorecard for AI agent integrations: six decision factors

Evaluate your project against these six factors. This isn't just a technical choice. It's a strategic one that needs to align with your business goals.

Strategic importance: is the integration core IP or a utility?

Does this integration form a core part of your unique value proposition, or is it a utility function users just expect? If the connection itself is your intellectual property, building may be necessary. If the integration is a means to an end, buying gets you there faster.

Tool breadth and depth: do you need one deep tool or many tools?

Does your agent need deep, nuanced functionality with one specific tool to be effective? That might favor building. Or does its value come from broad access to dozens or hundreds of tools (Slack, Jira, Salesforce, Notion) to handle diverse user requests? Broad access requirements strongly favor buying.

Speed to market: how fast do you need to ship new integrations?

How quickly do you need to add new tools to satisfy user demand and stay competitive? Building integrations one by one creates a permanent engineering backlog. That can slow your product roadmap to a crawl.

Authentication and security: can you own OAuth, tokens, and credential storage?

Can you confidently build, secure, and maintain credential storage, OAuth flows, and token refresh mechanisms for potentially thousands of end-users across hundreds of different APIs? Managing authentication at this scale is a massive risk. Specialized platforms solve this out-of-the-box.

Total cost of ownership: what will maintenance cost over time?

The initial build is just the start. Have you accounted for the hidden, recurring costs of monitoring for API changes, deploying fixes, managing versioning, and handling bug reports for every integration you build?

Orchestration and durability: do you need retries and long-running workflows?

Does your agent perform simple, stateless tasks? A direct build might work fine. Or does the agent execute complex, long-running, stateful workflows that must survive failures and retries? The latter requires a durable execution layer, a feature often included in agent-focused integration platforms.

When should you build AI agent integrations?

Buying is often the pragmatic choice. But there are clear scenarios where building makes sense. Building works when the integration is inseparable from your core product value.

• Core business differentiator: The integration is your product. If you're building a proprietary trading agent, the connection to a specific financial data API with your custom algorithms delivers your unique value. This connection can't and shouldn't be outsourced.

• Extreme customization: Your use case requires highly complex, non-standard business logic or data transformations that no existing platform can support. You need to manipulate the API in ways that fall far outside the common 80% of use cases.

• Unique security/compliance: You operate under a strict regulatory framework like FedRAMP or HIPAA that requires complete, auditable in-house control over the entire data path. And no available vendor meets your specific compliance criteria.

• Single, stable integration: Your agent's entire purpose involves connecting to one highly stable internal API that your company controls completely. The maintenance burden stays low and predictable.

When should you buy an AI agent integration platform?

Buy when the scorecard shows a high need for speed, breadth, and security. This lets you focus on what makes your agent unique.

Buying makes the most sense when you want your best engineers building core agent logic and AI capabilities, not handling authentication, security, and long-term maintenance. When you can ship integrations 4-6x faster using a platform, you reduce time to value from months to weeks.

When evaluating platforms, make sure the architecture, whether a Unified API or direct connector model, matches your agent's need for either broad access or deep, tool-specific functionality.

How to calculate integration TCO for AI agents (build vs. buy)

Teams consistently underestimate the true cost of building integrations. Honestly, initial development often accounts for less than 30% of the total cost over the integration's lifespan. The real expense is in ongoing maintenance.

Here's a model for a single, moderately complex integration like Salesforce. This assumes a senior engineer's fully-loaded cost is around $100/hour.

API churn drives the biggest hidden cost. APIs change constantly, and each change requires your team to stop work, diagnose the issue, and deploy a fix. This is a permanent tax on your engineering resources. Building even three or four integrations gets painful fast.

Hybrid approach: build core integrations and buy the long tail

Build vs. buy doesn't have to be binary. The most effective product teams adopt a hybrid strategy.

First, identify your "crown jewel" integrations. These are the one or two connections that are truly strategic and fundamental to your intellectual property. Dedicate your in-house talent to building and owning these completely. They're a core part of your product.

For everything else, use an integration platform. Buy the "long-tail" of integrations your users demand: Slack, Notion, Google Drive, Jira, and the hundreds of other tools they use daily.

Buying the long tail instantly gives you the broad coverage your sales team needs to close deals and your users need to stay productive. And it doesn't distract your core engineering team. This hybrid model gives you deep control where it matters most and broad, scalable coverage everywhere else.

Build vs. buy score: calculate your recommendation

Use this table to get a quick, data-driven recommendation. For your project, rate each factor from 1 (Strongly Favors Build) to 5 (Strongly Favors Buy).

How to interpret your build vs. buy score

• 6-12: You have a clear case to Build. Your needs are highly specific and strategic.

• 13-22: A Hybrid approach likely works best. Build your core and buy the rest.

• 23-30: You have a clear case to Buy. Prioritize speed, breadth, and focus.

Conclusion: choose build, buy, or hybrid for AI agent integrations

The right integration strategy frees your team to focus on what they do best: building intelligent, capable, and reliable AI agents. Evaluate your needs against total cost of ownership and strategic importance. Then you can confidently choose the path, Build, Buy, or Hybrid, that gets you where you want to go.

Next steps: add integrations or estimate build vs. buy TCO

Explore our tools directory: See the 850+ integrations you can add to your agent today.

Frequently asked questions

How long does it take to build a production-ready AI agent integration?

For a moderately complex API like Salesforce or HubSpot, teams should expect 6-8 engineer-weeks for the initial build, testing, and deployment. This doesn't include the significant ongoing time commitment for maintenance.

What is the biggest hidden cost of building AI agent integrations?

Ongoing maintenance. API endpoints get deprecated, authentication methods evolve, new security standards emerge, and rate limits change. Each integration you build creates a new, permanent tax on your engineering team's time. That's time they're not spending on your core product.

Can you use LangChain or open-source libraries instead of buying integrations?

Yes, but they're best for prototyping, not production. Open-source libraries give you basic connectors to get started. But they don't solve the hard production-grade challenges: managed authentication for your end-users, credential management, observability, security compliance, and guaranteed uptime. They're a starting point, not a complete solution for a commercial product.

When should we choose a hybrid approach for AI agent integrations?

Choose hybrid when 1–2 integrations are core to your product differentiation, but you still need many "long-tail" tools. Build the crown jewels and buy the rest.

How many integrations do we need before "buy" usually makes sense?

If you need more than a handful (often 5–10+) and expect ongoing requests for new tools, buying typically wins because of compounding auth and maintenance overhead.

What's the hardest part of building AI agent integrations in production?

Managed authentication at scale (OAuth flows, token refresh, secure credential storage) plus ongoing API churn and monitoring.

Is a unified API or direct connectors better for AI agents?

Unified APIs work best for broad coverage and consistent interfaces. Direct connectors work better when you need deep, tool-specific features and faster access to new endpoints.

Can we meet enterprise compliance requirements if we buy an integration platform?

Often yes, if the vendor supports your required controls (SOC 2, data isolation, audit logs). If you need strict regimes like FedRAMP/HIPAA with full in-house control, building may be required.

What should we build ourselves even if we buy an integration platform?

Build the workflows and business logic that are proprietary: your agent's decisioning, policies, and domain-specific transformations. Offload connectivity, auth, and maintenance.

Do AI agents need a durable execution layer for integrations?

If workflows run long or require retries and state (multi-step tasks across tools), durability matters. For simple stateless calls, it's less critical.

Why can't we just use standard automation tools like Zapier or Make?

Standard tools like Zapier are designed for linear, "if-this-then-that" automation triggered by events. AI Agents require bidirectional communication, the ability to poll for status updates, and dynamic authentication handling (allowing the agent to ask the user for permission mid-task). Standard iPaaS tools often lack the developer-first SDKs required for agentic control loops.

Does buying a platform mean we store end-user data on your servers?

Not necessarily. Modern agent integration platforms often use a passthrough architecture. Credentials may be stored securely (encrypted), but the actual data payload (e.g., the content of a Slack message or Salesforce record) flows directly from the provider to your agent without being persisted on the platform's database. Always check the vendor's SOC 2 Type II report and data retention policies.