Merge vs Nango vs Alloy Automation: 2026 Architecture Comparison

You are staring at a spreadsheet of lost enterprise deals. The pattern is painfully obvious. Prospects love your core product, complete a successful technical evaluation, and then ask the inevitable question: "How does this integrate with our custom Workday setup and our legacy Salesforce instance?"

If your answer is "it is on the roadmap," you have already lost the deal.

As we noted in our evaluation of which unified API is best for enterprise SaaS, Organizations now use an average of 106 different SaaS tools, down from 112 in 2023. That number is not going to zero. Every one of those tools has its own API, its own authentication scheme, its own pagination quirks, and its own rate limit behavior. Consolidating this sprawl is impossible. Instead, B2B buyers now demand strict interoperability. A recent industry survey found that 90% of B2B buyers view integration capabilities with existing technologies as a critical factor in software purchasing decisions.

The integration tax hits engineering teams hardest. You are not just building one API integration—you are building N integrations, each with its own OAuth dance, its own undocumented edge cases, and its own breaking changes that arrive without warning on a Tuesday afternoon. When engineering leaders realize they cannot build and maintain 50 different API connections in-house, they turn to third-party integration infrastructure.

The question is not whether to use integration infrastructure. The question is which architecture aligns with your team's actual constraints. In 2026, this market has fractured into three distinct architectural philosophies: the store-and-sync Unified API (Merge.dev), the code-first integration framework (Nango), and the embedded iPaaS (Alloy Automation).

This guide provides an objective, highly technical breakdown of these three architectures. We will strip away the marketing copy and examine how they handle data normalization, state management, custom enterprise fields, and the inevitable technical debt of scaling B2B SaaS integrations.

The 2026 Integration Architecture Dilemma

Before evaluating vendors, you have to decide what layer of the stack you actually want to abstract.

Do you want a vendor to cache all your customers' third-party data and serve it to you via a single REST endpoint? Do you want a framework that handles the OAuth lifecycle but forces you to write custom TypeScript for every data sync? Or do you want a visual canvas that lets non-developers drag and drop integration workflows?

If you make the wrong architectural choice now, you will hit a hard scalability wall in 12 to 18 months. You will either drown in a maintenance backlog of broken scripts, face massive compliance hurdles during SOC 2 audits, or receive punishing infrastructure bills that scale linearly with your user base.

Three paradigms dominate the 2026 landscape:

Each has legitimate strengths. Each has trade-offs that vendors prefer not to mention on their pricing pages. Let us examine the mechanics, benefits, and failure modes of the three leading paradigms.

Merge.dev: The Store-and-Sync Unified API

What it is: Merge positions itself as the leading Unified API, focusing heavily on speed-to-market. They provide a single API that normalizes data across 220+ integrations in six software categories (HRIS, ATS, CRM, accounting, ticketing, marketing automation). You integrate once with Merge's API, and it handles the translation layer to each third-party provider.

How it works architecturally: Store-and-sync architecture is an integration model where a middleware provider continuously polls third-party APIs on your behalf, normalizes the payload into a predefined schema, and stores a cached copy of your customers' data on their own database infrastructure. Merge's core architecture relies on background data synchronization. Merge stores customer records as part of its sync architecture.

When a user connects their Salesforce or BambooHR account via Merge, the platform initiates a historical sync. Merge's internal workers begin paginating through the upstream API, fetching records, transforming them into their proprietary Common Data Model, and persisting them to their managed database. When your application requests employee data, Merge does not hit the provider in real-time. Instead, it returns cached data from its own datastore.

sequenceDiagram
    participant App as Your Application
    participant Merge as Merge (Cache Layer)
    participant Provider as BambooHR / Workday
    
    Note over Merge,Provider: Background sync (periodic)
    Merge->>Provider: Fetch latest records
    Provider-->>Merge: Employee data
    Merge->>Merge: Normalize + store
    
    Note over App,Merge: Your API request
    App->>Merge: GET /employees
    Merge-->>App: Cached, normalized data

Where Merge Works Well

• Speed to first integration: If you need HRIS or ATS integrations live in a sprint, Merge's pre-built common data models can get you there fast. This approach works well when speed and breadth matter more than depth. Unified APIs like Merge are designed to help teams ship many integrations quickly, as long as customer requirements fit within standardized data models.
• Low engineering lift: Your team does not write integration logic. You do not have to read upstream vendor API documentation, figure out if an API uses cursor-based or offset-based pagination, or implement exponential backoff for rate limits. You simply call Merge's API and get normalized data back.
• Observability tooling: Merge offers advanced observability features to help customer-facing teams identify, diagnose, and resolve integration issues.

Where Merge Creates Pain

• Data Staleness: Because you are querying a cache, you are never reading real-time data. If a sync job runs every 2 hours, your application is operating on state that is up to 119 minutes old. For use cases where real-time accuracy matters—payroll calculations, compliance audits, real-time ticketing, or financial ledger updates—this eventual consistency is unacceptable.
• Schema Rigidity: Pre-built data models are rigid. To make HubSpot, Salesforce, and Pipedrive look identical, a Unified API must strip away the unique features of each platform. When your enterprise customer has 47 custom Salesforce fields that drive their business logic, those fields either drop entirely or require workarounds via Merge's raw pass-through API—which defeats the entire purpose of buying a unified API.
• Data Residency Risk: By definition, a store-and-sync platform copies your customers' data. If you integrate with an HRIS or ATS, you are suddenly replicating highly sensitive Personally Identifiable Information (PII)—salaries, social security numbers, and home addresses—into a third-party vendor's database. Data is encrypted and SOC 2 compliant, but remains stored until explicitly deleted - which simplifies historical queries but increases compliance scope and data-retention considerations. Every enterprise InfoSec team will heavily scrutinize this threat vector.
• Pricing Mechanics: $65 per Linked Account after. 10 production Linked Accounts included. Each customer connection counts separately: If one customer connects three integrations, you pay three times. The math gets uncomfortable fast. At 200 customers with 3 connections each, you are looking at roughly $39,000/month. If this unit economics trap sounds familiar, it may be time to evaluate Merge.dev alternatives.

Nango: The Code-First Integration Framework

What it is: Nango was built as a direct reaction to the rigidity of store-and-sync Unified APIs. Instead of forcing your data into a vendor's predefined schema, Nango gives you infrastructure and asks you to write code. Nango is an open-source platform for building product integrations. It supports 700+ APIs and works with any backend language, AI coding tool, and agent SDK.

How it works architecturally: A code-first integration framework acts as an execution environment and an auth proxy. It provides managed infrastructure for OAuth token lifecycles and cron-based execution, but requires your engineering team to write, host, and maintain custom TypeScript logic for every individual API endpoint, data transformation, and synchronization job. The runtime provides per-tenant isolation, elastic scaling, automatic retries, and rate-limit handling.

// Example of the code-first maintenance burden in Nango
export default async function fetchSalesforceContacts(nango: Nango) {
  const response = await nango.get({
    endpoint: '/services/data/v58.0/query?q=SELECT+Id,Name,Email+FROM+Contact',
  });
  
  // You must write custom mapping logic for every endpoint
  const mappedData = response.data.records.map(record => ({
    external_id: record.Id,
    full_name: record.Name,
    email: record.Email,
    // Custom field handling requires manual code updates per customer
  }));
  
  await nango.save('Contact', mappedData);
}

Where Nango Works Well

• Full Control: Nango solves the "lowest common denominator" problem completely. You control the exact API calls, the data transformations, and the sync logic. You can map any custom object, hit any obscure endpoint, and format the data exactly how your internal database expects it. If your agent requires tool calls tailored to specific user intent (not generic CRUD actions), Nango is a strong fit. It is code-first, so you define tool calls as typed TypeScript functions.
• Self-Hostable: Open source and self-hostable. Nango is fully open source. Run it on Nango Cloud or self-host on your own infrastructure. This matters immensely for teams with strict data residency requirements who cannot use a managed cloud.
• AI-Assisted Development: Nango's AI builder generates TypeScript integration functions from natural language. Unlike black-box solutions, you get readable code you can review, edit, and version control.

Where Nango Creates Pain

• The Maintenance Tax at Scale: Writing the initial TypeScript sync script is easy. Maintaining hundreds of them is a nightmare. If you support 30 integrations, and each integration needs to sync 5 different objects (Contacts, Companies, Deals, Tickets, Users), your team now owns 150 distinct integration scripts. When third-party APIs deprecate endpoints, change pagination schemas, or alter their rate limits, your engineering team has to rewrite, test, and deploy code. This compounding maintenance burden is a primary reason teams begin evaluating Nango alternatives. One G2 reviewer noted needing to hire professional developers specifically to handle the platform's multiple integrations, finding the ongoing cost significant.
• Custom Fields Require Code Forks: Nango requires developers to fetch custom fields, store metadata, and write custom TypeScript logic for each unique customer requirement. If Enterprise Customer A has a custom field custom_industry_c and Customer B has industry_type_c, your TypeScript logic has to handle both. You end up with massive, convoluted if/else blocks or custom code forks for specific tenants. This does not scale.
• Pricing Complexity: Nango's Starter plan begins at $50/month but strictly includes just 20 connections. After that, you pay $1 per connection, plus metered usage on proxy requests, function compute time, runs, logs, sync storage, and webhooks. Multiple variable dimensions make forecasting hard.

Alloy Automation: The Embedded iPaaS

What it is: Alloy Automation approaches the problem from an entirely different angle. Originally built for e-commerce automation, Alloy Embedded allows B2B SaaS companies to white-label a visual workflow builder directly inside their application. Alloy has retooled as a horizontal "universal API" platform, providing a comprehensive iPaaS and embedded iPaaS with use cases across all aspects of integration development.

How it works architecturally: An embedded iPaaS provides an iframe or SDK that renders an integration marketplace inside your app. When your user clicks "Connect HubSpot," they are presented with a visual canvas. They can map fields using dropdown menus and construct multi-step workflows (e.g., "When a deal is closed in HubSpot, create a user in our app, then send a Slack message"). Alloy executes these workflows on their own infrastructure.

Where Alloy Works Well

• Visual Workflow Builder: Embedded iPaaS platforms are fantastic for offloading integration work to customer success teams, implementation managers, or the end-users themselves. Engineering simply embeds the SDK, and non-technical staff can build "recipes" or templates.
• White-Label Embedding: The platform is architected to enable software vendors to white-label and embed integrations directly within their own applications, presenting them as native features to end users.
• Commerce Heritage: Alloy provides a specialized connector ecosystem tailored specifically for ecommerce and retail workflows, including integrations with major platforms like Shopify, Amazon, and logistics providers. If your product serves commerce, Alloy's pre-built workflows are a genuine head start.

Where Alloy Creates Pain

• Bidirectional Syncs and Infinite Loops: Visual workflow builders are inherently linear. They are designed for "If X happens, do Y" triggers. If you need to keep a Contact record continuously synced in real-time between your app and Salesforce, visual workflows break down. You have to build complex state management to prevent infinite loops (where an update in Salesforce triggers an update in your app, which triggers an update back in Salesforce, ad infinitum).
• Version Control and Observability: Software engineers hate visual builders for a reason. You cannot easily review a drag-and-drop workflow in a pull request. You cannot run automated unit tests against a visual canvas. When a workflow fails in production because an end-user mapped a string to a boolean field, debugging involves clicking through a UI rather than reading structured logs.
• Vertical Depth Outside Commerce: Alloy's vertical specialization in ecommerce and retail means the platform offers fewer connectors and pre-built workflows for use cases outside this domain compared to horizontal iPaaS platforms. Organizations requiring deep integrations across HR, finance, or ITSM may find gaps.

Prismatic: The Purpose-Built Embedded iPaaS

What it is: If Alloy started in e-commerce and expanded horizontally, Prismatic was designed from day one as a horizontal embedded iPaaS for B2B software companies. Prismatic is purpose-built for B2B software companies, not retrofitted. It targets a specific operational model: engineers build integration templates, solutions engineers configure customer-specific instances, and customer success teams deploy without code.

How it works architecturally: Prismatic's architecture revolves around individual integration flows. Its architecture revolves around individual integration flows. It provides a low-code visual designer and a code-native TypeScript SDK that allows your team (or your customers) to build multi-step integration workflows. Each integration is a distinct artifact - a workflow with triggers, steps, branches, loops, and custom logic.

Your team builds an integration once, then deploys it to multiple customers, each with their own credentials, configuration parameters, and optional workflow overrides. With a B2B integration platform, you build an integration once and deploy it to thousands of customers, each with their own configuration and credentials. This approach is fundamentally different from building one-off integrations for internal use or creating separate integrations for each customer.

Where Prismatic Works Well

• Hybrid Low-Code / Code-Native: Most embedded iPaaS platforms force you to choose between low-code tools that limit developers or code-only approaches that leave non-technical teams behind. Prismatic supports both, giving developers full code-native flexibility while empowering non-devs with intuitive low-code tools. Engineers write custom components in TypeScript while product managers configure standard integrations through the visual designer.
• Multi-Tenant Deployment: Traditional iPaaS added multi-tenancy as an afterthought. Prismatic was architected for B2B SaaS; deploy once, configure per customer, and scale across your entire customer-base without performance degradation. The separation between integration templates, customer instances, and configuration is well-designed.
• Embeddable Marketplace: Embed an integration marketplace in your product with just a few lines of code. White-labeling, theming, and SSO make it feel native. Customers can explore your offerings, activate integrations using a sleek config wizard, and access self-serve support tools.

Where Prismatic Creates Pain

• Per-Integration Maintenance Surface: Each integration is a separate workflow artifact. At 50 integrations serving 200 customers, you are managing 50 distinct workflows, each with its own trigger logic, error handling, and data mapping. When an upstream API changes a field name, you update that specific workflow, re-test it, and redeploy it to every affected customer instance. This is more structured than raw TypeScript scripts (Nango), but you still own the mapping logic per integration.
• Connector Coverage Gaps: Prismatic doesn't have a large a catalog of available connectors as competitors. Prismatic is also a bit behind pushing their platform into the agentic AI space. The platform offers around 190+ pre-built connectors. You can build custom connectors with TypeScript, but that shifts engineering effort back onto your team.
• Concurrency and Duration Limits: Does not support 2-way data syncs, LLM tool calls, and scalable webhook processing. Platform concurrency limits (15-minute max workflow duration, 1 GB memory) constrain enterprise-scale operations. For high-volume webhook workloads or long-running data sync jobs, these limits can bite.
• Pricing Opacity: Prismatic offers 3 pricing plans: Scale, Enterprise, and Custom. Each plan requires you to connect with their team and there's no cost figures outlined. Without public pricing, forecasting integration costs at scale becomes a negotiation exercise.

Truto vs Prismatic: Embedded iPaaS vs Declarative Unified API (2026)

If you are evaluating integration infrastructure and have narrowed your options to Prismatic and Truto, you are actually choosing between two fundamentally different architectural layers. The Truto vs Prismatic decision comes down to a single architectural fork: do you need your end-users to visually build custom automation workflows inside your product, or do you need your engineering team to programmatically pull and push normalized data across dozens of third-party platforms to power native features?

This section provides a detailed side-by-side comparison to help you make that call. For the full deep dive, see our dedicated Truto vs Prismatic: Embedded iPaaS vs Declarative Unified API guide.

Quick Comparison Table

Architecture and Intended User - Side by Side

Prismatic is built for a team structure where engineers create integration templates and non-technical staff deploy and configure them for individual customers. Senior devs build complex integrations. Solutions engineers configure customer instances. Customer success teams deploy instances to new customers and handle support issues. The visual workflow builder is the core interface.

Truto is built for engineering teams that want to call one API and get normalized data back across any provider in a category. There is no visual builder for end-users. Instead, the entire integration layer is a set of API endpoints (Unified, Proxy, and Custom) that your backend code calls directly. The "configuration" layer is JSONata expressions and JSON blobs, not drag-and-drop canvases.

The distinction matters: if your product needs an in-app integration marketplace where customers build their own multi-step automations, Prismatic solves that. If your product needs programmatic CRUD access to normalized third-party data to power native features, Truto solves that.

Connector Coverage and Example Endpoints

Consider two common integration targets - Salesforce (CRM) and Notion (productivity) - to see how the architectures differ in practice.

Salesforce in Prismatic: You get a pre-built Salesforce connector that exposes actions like "List Records," "Create Record," and "Update Record." The built in connectors are mostly aggregates over the target systems API such as "List leads". For custom objects, custom SOQL queries, or multi-step Salesforce-specific logic, you write a custom TypeScript component and wire it into a workflow.

Salesforce in Truto: You call GET /unified/crm/contacts?integrated_account_id=abc and get normalized contacts back. The JSONata mapping handles Salesforce's PascalCase fields, SOQL query construction, and custom field detection (__c suffix) without any code. If an enterprise customer has custom Salesforce fields, you apply a tenant-level mapping override - no workflow changes, no redeployment.

Notion in Prismatic: You use the Notion connector's pre-built actions or make raw HTTP calls through the connector's auth context. Multi-step logic (e.g., "fetch database, filter pages, extract properties") is wired together in the visual designer.

Notion in Truto: You call the Proxy API (GET /proxy/databases/:id/query) using the integrated account's managed auth. Truto handles token refresh and request formatting; your code handles the response directly.

Data Flow, Real-Time Behavior, and Webhooks

Prismatic workflows are event-driven: a trigger fires (webhook, schedule, or API call), and the workflow executes its step chain. Polling triggers will poll an external API on a schedule that you set (for example, "every 5 minutes"), and if new data is available since the last time it polled, a full execution will run so your flow can process the data. For webhook-triggered flows, Prismatic generates a unique URL per instance that the third-party app sends events to.

Truto's data flow is request-response by default. Your application makes an API call, Truto proxies it in real time, transforms the response, and returns it. No data is stored between requests. For event-driven use cases, Truto normalizes incoming provider webhooks into canonical record:* events using JSONata configuration, then delivers them to your registered endpoints with signed payloads. The two inbound patterns - account-specific webhook URLs and environment-level fan-out - cover both granular and broad event ingestion.

The practical difference: Prismatic's workflow model is better suited for multi-step orchestration ("when X happens in Jira, update Salesforce and notify Slack"). Truto's model is better suited for direct data access and normalized event streams that your backend logic consumes.

Rate Limiting and Retry Strategies

Prismatic handles rate limits at the platform level. Prismatic's containerized, auto-scaling infrastructure is built for high-concurrency workloads. Whether you're processing a single webhook or syncing a large number of records, the platform scales horizontally to meet the demand. Rate limit handling, automatic retries, and queue management are platform-level features. This means Prismatic absorbs and retries rate-limited requests on your behalf, which is convenient but also means you have less visibility into the actual state of the upstream API.

Truto takes the opposite approach: radical transparency. When an upstream API returns an HTTP 429, Truto passes that status directly to your application, while normalizing the provider's rate limit metadata into standardized headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset) per the IETF specification. Your backend gets accurate, real-time backpressure signals and can implement its own retry strategy - exponential backoff, priority queuing, or graceful degradation - rather than depending on a middleware queue you cannot inspect.

Neither approach is universally better. Platform-managed retries reduce boilerplate. Transparent pass-through gives you control and prevents silent failures where a middleware layer silently drops or delays traffic without your application knowing.

Maintenance Burden and Operational Model

This is where the architectural difference compounds over time.

At 100+ integrations, this distinction becomes dramatic: The Prismatic model gives you maximum flexibility per integration - you can build arbitrarily complex logic for each one. The Truto model gives you maximum consistency and minimum maintenance surface area - but it constrains you to operations that fit the unified CRUD paradigm.

With Prismatic, adding a new integration means building a new workflow (or cloning and modifying an existing one), testing it, deploying it, and monitoring it independently. At 100 integrations, your team manages 100 separate artifacts. When you improve error handling, you update each workflow individually.

With Truto, adding a new integration means adding JSON configuration and JSONata mapping expressions - data, not code. The same generic engine that handles existing integrations handles the new one without a deployment. When the pagination logic is improved at the engine level, every integration benefits immediately.

The trade-off is real: Prismatic lets you build arbitrarily complex per-integration logic (multi-step orchestration, conditional branching, custom error recovery). Truto constrains you to patterns that fit the unified CRUD model, but eliminates the per-integration maintenance surface entirely. For complex event-driven workflows, Prismatic wins. For standardized data access at scale, Truto wins.

The Hidden Costs at Scale: Pricing and Maintenance

When evaluating these three architectures, you must project your costs 24 months into the future. The unit economics of integration platforms often punish B2B SaaS companies for growing. Pricing is where architecture decisions compound.

Here is how the economics diverge at different customer counts:

Note: Merge calculated at $650 base + $65 per additional linked account beyond 10. Nango base is $50 + $1/connection; compute, storage, and request overages not included. Alloy at $50/connection after 10 free.

Beyond raw SaaS subscription costs, consider the hidden architectural taxes:

The Linked-Account Penalty (Merge) Store-and-sync platforms charge based on "Linked Accounts." If you have a product-led growth (PLG) motion and thousands of SMB users connect their tools, your monthly bill will explode, regardless of how much API volume those users actually consume. You are paying a premium for the vendor to store data you might not even be querying.

The Engineering Salary Tax (Nango) Code-first platforms have cheaper baseline software costs, but the true cost is hidden in your payroll. The "+ usage" qualifier on Nango is where surprises live. The compute time variable is particularly nasty. Because your integration logic runs as TypeScript functions on Nango's runtime, you don't fully control how much compute time a sync consumes. Furthermore, if maintaining 200 TypeScript sync scripts requires one full-time senior backend engineer, the actual cost includes a $160,000+ base salary, plus the opportunity cost of that engineer not building your core product.

The Workflow Execution Overhead (Alloy) Embedded iPaaS platforms often meter based on "workflow tasks" or executions. Visual workflows do not refactor the way code does. If you use a visual builder to sync 50,000 historical records, and the platform counts each record as a separate execution, a single customer onboarding event can consume your entire monthly quota.

Truto: The Zero-Code, Zero-Retention Alternative

If store-and-sync poses security risks, code-first creates technical debt, and visual builders lack engineering rigor, what is the alternative?

Truto represents a fourth architectural paradigm: the declarative, zero-retention pass-through proxy.

Instead of caching data (Merge), requiring you to write TypeScript (Nango), or building visual workflows (Alloy), Truto normalizes data across hundreds of SaaS platforms into common data models using a declarative configuration layer. Every integration is defined as JSON configuration and JSONata transformation expressions. There is zero integration-specific code in the runtime.

sequenceDiagram
    participant App as Your Application
    participant Truto as Truto (Pass-Through)
    participant Provider as Salesforce / Workday
    
    App->>Truto: GET /employees
    Truto->>Provider: Real-time API call
    Provider-->>Truto: Raw response
    Truto->>Truto: JSONata transform<br>(normalize to common model)
    Truto-->>App: Normalized, live data
    Note over Truto: No data stored

Key Architectural Differences

1. Zero Data Retention Truto operates purely as a pass-through proxy. When you request data, Truto translates your request into the provider-specific format, fetches the live data directly from the third-party API, transforms the response in-flight, and delivers it to your application. Your customer's data never lands in Truto's storage. This eliminates the massive security and compliance overhead that Merge's sync architecture creates. For a deeper look at why this matters, see our guide on zero data retention for AI agents.

2. Zero Integration-Specific Code Where Nango requires TypeScript per integration and Merge bakes logic into its backend, Truto's runtime is entirely generic. Adding a new integration means adding configuration data, not writing and deploying code. Your runtime logic remains completely agnostic to the underlying provider, fundamentally flattening the maintenance curve.

3. Per-Customer Custom Object Mapping Truto solves the "lowest common denominator" problem using a 3-level configuration override system. If an enterprise customer has a highly specific Salesforce architecture, you do not need to fork your code. You simply apply a tenant-level JSONata mapping override that links their custom fields to your unified model. The mapping happens declaratively at the proxy layer.

4. Transparent Rate Limit Handling A critical architectural distinction is how platforms handle upstream API constraints. Truto does not magically absorb or infinitely retry rate limit errors. When an upstream API returns an HTTP 429, Truto passes that error directly to the caller, while normalizing the upstream rate limit information into standardized headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset) per the IETF specification. This radical transparency ensures your backend services have accurate, real-time backpressure signals to implement proper exponential backoff, rather than relying on a black-box middleware queue that might silently drop traffic.

5. Predictable Volume-Based Pricing Truto does not charge per linked account. Your bill does not scale linearly with your customer count, which means your product team is never in the awkward position of discouraging users from connecting tools to keep costs down.

Strategic Wrap-Up: Which Architecture Wins in 2026?

The decision ultimately comes down to your engineering team's constraints and your target market. There is no universal winner, only a best fit for your specific requirements.

Choose Merge if you need to ship a prototype by next week, your integration requirements align neatly with standard data models, your use case tolerates eventual consistency, and your InfoSec team is comfortable replicating customer data into a third-party vendor's database cache.

Choose Nango if your engineering team wants full control, you have the headcount to maintain a growing TypeScript integration codebase, and you value the ability to self-host to avoid managed cloud compliance issues.

Choose Alloy if you are building an e-commerce or marketing tool where non-technical users need to construct simple, linear "If X then Y" automated workflows, and you want to empower customer success to build integrations.

Choose Prismatic if you need an in-app integration marketplace where solutions engineers configure customer instances and end-users can self-serve, and your team benefits from mixing visual low-code workflows with TypeScript custom components.

Choose Truto if you are building enterprise B2B SaaS and need the speed of pre-built normalized schemas, the flexibility to map complex custom objects per-tenant, and the strict security guarantees of a zero-data-retention architecture.

The integration infrastructure you pick today will either accelerate or bottleneck your product for years. Make the decision based on architecture, not the vendor with the best landing page.