Part of the SD Times 100 2026 series. See the full SD Times 100 2026 list for every category and honoree.
For two decades, “integration” meant connecting Application A to Application B and calling it done. In 2026, that definition no longer holds. Every enterprise now runs a web of SaaS platforms, internal microservices, data lakes, and increasingly autonomous AI agents that need to read, write, and act across all of it in real time. The companies recognized in the Integration, APIs & Connectivity category of the SD Times 100 aren’t just plumbing vendors anymore. They’re building the connective tissue for what we’re calling the Era of Agentic Infrastructure: a software landscape where APIs are consumed as often by machines making decisions as by humans writing code.
For engineering and development leaders, this category deserves more scrutiny than it’s traditionally gotten. The integration layer is where outages start, where security incidents originate, and where technical debt compounds invisibly until a board-level incident forces a reckoning. Get it right, and your organization moves faster than its API sprawl would suggest is possible. Get it wrong, and every new product initiative inherits the sins of the last five years of “we’ll fix the integration later.”
Why This Category Matters Now
Three forces are converging on integration and API teams simultaneously, and any one of them would be disruptive on its own.
AI agents are becoming API consumers. Historically, APIs were designed for predictable, scripted consumption: a frontend calls a backend, a backend calls a third-party service, with known request shapes and known failure modes. Agentic AI systems consume APIs differently. They explore. They retry in unexpected ways. They chain calls dynamically based on a model’s reasoning rather than a developer’s pre-written logic. This is forcing API teams to think about discoverability (can an agent find and understand this endpoint without a human reading the docs first?), rate-limiting strategies that assume burstier and less predictable traffic, and authorization models that can scope an agent’s permissions tightly enough to avoid catastrophic mistakes.
API sprawl has outpaced governance. Most engineering organizations of any real size now have hundreds to thousands of internal and external APIs, and a meaningful percentage of them are undocumented, unowned, or duplicated. This isn’t a hypothetical risk; it’s the single most common root cause cited in post-incident reviews involving “an API we didn’t know was still being called.” Without active API governance, integration debt becomes integration risk.
Connectivity has become a competitive differentiator, not just a cost center. Customers and partners increasingly evaluate vendors on how easily their systems integrate with the rest of a buyer’s stack. A best-in-class product with a painful integration story loses deals to a merely good product that plugs in cleanly. This has pushed companies to invest in developer-facing API experience — documentation, SDKs, sandbox environments — as a go-to-market asset, not an afterthought.
The Different Segments Inside This Category
“Integration, APIs & Connectivity” is really several distinct disciplines that get lumped together because they all sit at the seams between systems. Development leaders should understand each segment separately, because the buying criteria and the team that owns the decision differ significantly.
API management and gateways. This is the traditional core of the category: tools that let organizations design, publish, secure, rate-limit, monitor, and monetize APIs. Companies like Kong and WSO2 sit here, providing the gateway layer that sits in front of APIs to enforce policy and collect telemetry. As API consumption patterns shift toward agentic and machine-to-machine traffic, gateway vendors are adding capabilities specifically aimed at authenticating and rate-limiting non-human callers.
Service mesh and cloud-native connectivity. As organizations decompose monoliths into microservices running on Kubernetes, the question of how those services discover and securely talk to each other becomes its own discipline. Solo.io and Gravitee operate in this layer, where the conversation is less about exposing an API to external developers and more about reliable, observable, policy-controlled traffic between internal services.
iPaaS and workflow automation. Integration Platform as a Service tools exist to connect SaaS applications without custom code, and this segment has expanded dramatically as the number of SaaS tools inside a typical company has exploded. Boomi represents the enterprise end of this spectrum, with deep governance and large-scale data integration capabilities, while Zapier represents the democratized end, where business users and citizen developers build their own automations without engineering involvement. Development leaders increasingly need a point of view on which integration problems belong in code, which belong in iPaaS, and which should be handed to business teams entirely.
Universal and federated data connectivity. CData Software occupies a specific and increasingly important niche: providing standardized drivers and connectors so that virtually any data source, from a legacy on-prem database to a modern SaaS API, can be queried and integrated using familiar tools like SQL or ODBC/JDBC. This matters enormously for data and analytics teams who need to pull from sources that were never designed to be queried programmatically.
Communications and embedded APIs. Twilio represents a different but related category: APIs that let developers embed entire capabilities — messaging, voice, video, verification — into their own products without building that infrastructure themselves. As AI-powered products increasingly need to communicate with end users across channels, this segment is seeing renewed relevance.
API specification, design, and SDK generation. Speakeasy represents one of the most important emerging segments in this year’s list: tools that take an OpenAPI specification and automatically generate idiomatic, type-safe SDKs in multiple languages, along with documentation that’s increasingly written to be consumed by AI agents and code-generation tools as much as by humans. As AI coding assistants become a primary consumer of API documentation, the quality and machine-readability of an API’s spec has become a direct driver of developer adoption.
Agent-native tooling and execution. Trigger.dev reflects the newest wave in this category: infrastructure purpose-built for running long-lived, reliable background jobs and workflows that AI agents and automated systems depend on, where traditional request-response API patterns don’t fit the job.
In practice, mature engineering organizations are not picking one tool from this category; they’re assembling a layered stack. A typical enterprise pattern looks like this: a service mesh or internal gateway handles east-west traffic between microservices; a public-facing API gateway handles external partner and customer traffic, applying authentication, rate limiting, and analytics; an iPaaS layer connects SaaS systems that don’t need custom integration code; and increasingly, an SDK generation and spec-management layer ensures that every API, internal or external, has a consistent, well-documented, AI-readable contract.
The shift worth watching closely is how API documentation and design are being re-architected for a dual audience. Until recently, API docs were written for human developers reading a portal. Now they need to be equally legible to an LLM-based coding agent that’s trying to figure out, without a human in the loop, how to call an endpoint correctly. This has real implications for how engineering teams write specs, name parameters, and structure error responses. Ambiguity that a human developer could shrug off and resolve via Slack becomes a hard failure for an autonomous agent.
Another practical pattern is the rise of API governance as a discipline with real tooling behind it, rather than a wiki page nobody updates. Engineering leaders are increasingly expected to answer questions like “how many APIs do we have,” “who owns each one,” and “which ones are still being called” with actual data, not guesses. That accountability is driving adoption of API catalogs and governance layers that sit alongside the gateway and mesh tools already in place.
For development managers building or refreshing their integration stack, a few questions cut through vendor marketing quickly:
- Does it support both human and agentic consumption patterns? Ask any API management vendor directly how their authentication, rate limiting, and monitoring change when the caller is an AI agent rather than a predictable application. Vague answers are a signal.
- What’s the actual time-to-first-call for a new developer or agent? The gap between “we have an API” and “someone unfamiliar with it can successfully call it in under ten minutes” is one of the best predictors of integration friction down the line.
- How does it handle governance at scale? Ownership, deprecation, and discoverability matter more than any individual feature once you’re past a few dozen APIs.
- Does it reduce custom code, or just move where the custom code lives? Some iPaaS and integration tools genuinely eliminate engineering work; others just relocate the complexity into proprietary configuration that’s harder to debug and version than code would have been.
The 2026 Honorees in Integration, APIs & Connectivity
- Boomi — Enterprise iPaaS with deep data integration and governance capabilities for large, complex environments.
- CData Software — Universal data connectivity drivers and connectors for querying and integrating virtually any data source.
- Gravitee — API management and event-native gateway platform built for hybrid and streaming architectures.
- Kong — API gateway and service connectivity platform spanning cloud-native, hybrid, and AI-driven traffic.
- Postman — The widely adopted API platform for designing, testing, documenting, and collaborating on APIs.
- Solo.io — Service mesh and AI gateway connectivity for cloud-native and Kubernetes environments.
- Twilio — Embedded communications APIs for messaging, voice, and customer engagement.
- WSO2 — Open-source-rooted API management and integration platform with strong governance tooling.
- Zapier — No-code automation and integration platform connecting thousands of SaaS applications.
- Speakeasy (2026 Addition) — Automated, AI-ready SDK and documentation generation from OpenAPI specifications.
- Trigger.dev (2026 Addition) — Reliable background job and workflow infrastructure built for agentic and long-running automation.
Frequently Asked Questions
What’s the difference between API management and an iPaaS platform? API management tools (like gateways) focus on exposing, securing, and governing APIs that other systems call into. iPaaS platforms focus on connecting multiple SaaS applications together, often without writing custom integration code at all. Many organizations need both: a gateway for APIs they own and expose, and an iPaaS layer for connecting third-party tools they don’t control.
Why are AI agents changing how companies think about API design? AI agents consume APIs without a human reading the documentation first, which means specs need to be unambiguous, well-typed, and consistently structured so a model can correctly infer how to call an endpoint. Poorly documented or inconsistent APIs that a human developer could work around become much harder for an autonomous agent to use reliably.
Do we still need a service mesh if we already have an API gateway? Usually yes, because they solve different problems. A gateway typically governs traffic coming in from outside the organization (north-south traffic), while a service mesh governs traffic between internal microservices (east-west traffic). Larger, more decomposed architectures tend to need both.
How do we get control over API sprawl without slowing teams down? The organizations that do this well treat API governance as infrastructure, not bureaucracy: automated discovery and cataloging of existing APIs, clear ownership assigned at creation time, and lightweight policy checks built into CI/CD rather than manual review gates. The goal is visibility, not approval friction.
What should we prioritize first if we’re starting from a low-maturity integration stack? Start with discovery and documentation before adding new tooling. Most organizations don’t have an integration tooling gap; they have a visibility gap, where nobody can say with confidence which APIs exist, who owns them, and which ones are safe to deprecate. Solve that first, then layer in gateway, mesh, or iPaaS tools based on what the discovery process reveals.
This article is part of the SD Times 100 2026 series exploring the categories and companies shaping software development this year. Read the full SD Times 100 2026 list for the complete roundup.
