Most Scalekit vs Nango evaluations start with a features checklist. The more useful question is architectural:
Nango's own site organizes its platform into three groups: Authorization, an "AI agents" group (MCP & Tools, Triggers, Context Sync), and a "Product Integrations" group that still features Data Sync, Webhooks, and a Unified API – the classic iPaaS building blocks.
Nango's docs describe its newest agent feature, Agent Sessions, as "an optimized interface for AI agents to use integrations" — language that names a UX layer added in front of an existing primitive (Connections), not a ground-up rebuild.
Scalekit didn't retrofit anything. There was no prior sync platform to add tool-calling on top of, the vault, the connector model, and the MCP surface were built together as one runtime for agents to call tools against, from the first release.
That distinction shows up concretely in connector depth, tool bundling, authorization timing, and credential isolation — the sections this article covers next.
The catalog-size numbers land close enough on both sides that they're not the real decision factor — what matters is whether a connector can handle the auth pattern a given system actually requires, and whether its tools are built for the specific operations an agent needs, not a one-to-one wrapper of the underlying API's full surface.
The clearest test is enterprise connectors that require harder, non-standard auth, the pattern behind most production data:
Where Nango is a fair choice: teams that want full, code-level control over every tool definition, and are comfortable writing and maintaining TypeScript action functions per operation, get real ownership from Nango's model. That's a legitimate trade-off, not a gap.
Both platforms scope tool access using short-lived, per-run session tokens: that part is genuinely comparable. The difference is what sits behind the token.
Scalekit's Virtual MCP Servers are a named, persistent definition: pick the tools, set the role, and that object exists for any number of future runs to reference. Each individual agent run mints a short-lived token scoped to that user against the existing definition — "one server definition serves all users," in Scalekit's own description of the feature.
Nango's Agent Sessions have no equivalent saved object. Every session-creation call sends the tenant selector and toolset allow/deny list as inline JSON:
There's no way to name this configuration and reuse it. Nango's docs also state plainly that "there is no way for the agent to ask the user to connect an unconnected integration," and flag Agent Sessions as a feature they're "quickly expanding," consistent with a primitive that's new rather than mature.
None of this makes Nango's tool-calling layer non-functional; teams ship on it today. But a few details in Nango's own docs are the kind of seam you'd expect from adding session scoping to a platform not originally built around identity and authorization.
Scalekit's documentation describes a full BYOK flow: register an encryption key from your own Google Cloud KMS, and Scalekit uses the KMS API for every encrypt and decrypt operation without ever storing the key material itself. You keep the key lifecycle — rotation, revocation, and access grants are entirely yours. Scalekit's own docs are direct about the consequence: "If the key is disabled, destroyed, or IAM access is revoked, Scalekit cannot encrypt new data or decrypt existing records until you restore access." That's a real, self-serve control, not a roadmap promise.
Nango has no equivalent. Its security documentation states that on Nango Cloud, "encryption keys are securely managed by Nango" — one key for the whole deployment. Self-hosted Nango is the same shape at smaller scale: a single operator-supplied NANGO_ENCRYPTION_KEY secures the entire instance, and it cannot be rotated without losing existing encrypted data.
Even without opting into BYOK, Scalekit's default architecture issues one Data Encryption Key per customer environment, with the master key isolated in a separate KMS layer rather than living alongside the database — so the blast radius of a single compromised key stays scoped to one environment rather than the whole platform. This directly relates to the broader challenge of credential ownership across agent tool-calling patterns.
"Less DIY" is a specific, checkable claim, not just a tagline — here's what it means in practice:
For teams building multi-tenant agent products, the difference between single-tenant and multi-tenant tool-calling auth is not cosmetic — it shapes which platform's model fits better from the start.
OpenTelemetry-native tracing is a genuine Nango strength worth stating plainly rather than working around. If distributed tracing into an existing OTel pipeline is a hard requirement today, Nango is ahead here. Understanding agent tool observability more broadly is worth considering when evaluating either platform.
Nango publishes three tiers: Free, Growth, and Enterprise. The free tier is auth-and-proxy only, with no direct integration access. The Growth tier carries a base fee plus usage metrics tied to connected-account count, request volume, and synced-record volume, per Nango's own published pricing page.
Scalekit publishes numeric tiers directly, metered primarily on tool-call volume rather than connected-account count: a model that scales more predictably for products with many end users and moderate per-user call volume than a per-connection metric does.
If you're also comparing other platforms in this space, our overview of Composio alternatives for AI agent tool calling and Arcade.dev alternatives cover similar architectural trade-offs across a wider set of tools.
For teams handling sensitive agent credentials at scale, the patterns described in secure token management for AI agents apply regardless of which platform you choose.
Nango began as a data-sync and API-integration platform and added agent tool-calling on top, most recently through Agent Sessions. Scalekit was built as a tool-calling and authorization runtime for agents from the start, with no prior sync platform underneath it.
Nango's Agent Sessions scope a tenant and toolset per session, but there's no persistent, named bundle object — the toolset selection is JSON your backend reconstructs on every session-creation call. Scalekit's Virtual MCP Servers are defined once and referenced by every subsequent run.
Yes — Scalekit supports BYOK with Google Cloud KMS, and you retain full control of the key lifecycle, including the ability to revoke Scalekit's access at any time. Nango has no equivalent; both Cloud and self-hosted Nango use a platform-managed key.
Nango's runtime ships under the Elastic License 2.0, source-available, with a no-compete clause, not an OSI-approved open-source license, despite marketing language describing it as "fully open source."
Architecturally, yes. Teams that need bulk data sync into their own store alongside real-time, scoped agent tool-calling could use Nango for the former and Scalekit for the latter, similar to how sync platforms and agent-execution layers are commonly paired in this category.
