When is it better to standardise on one SDK versus supporting multiple SDKs in an AI gateway setup?

Why This Matters for Security Teams

Choosing one SDK or several is not just an engineering preference. In an ai gateway, the SDK defines how applications authenticate, route, retry, and surface model-specific behaviour. If the gateway abstracts too much, teams lose access to provider capabilities they may need. If it abstracts too little, security, observability, and change control become fragmented across the stack.

This is also a Non-Human Identity problem. SDKs often carry the workload credentials, token exchange logic, and request context that determine what the gateway can safely do on behalf of an agent or application. The wrong SDK strategy can create hidden credential sprawl, inconsistent policy enforcement, and brittle failover paths. That is why guidance from NIST Cybersecurity Framework 2.0 still matters here: governance, asset visibility, and identity control need to extend into the integration layer, not stop at the perimeter.

NHI Management Group’s research on the DeepSeek breach shows how quickly exposed AI infrastructure and credentials can become an operational incident when access patterns are not tightly governed. In practice, many security teams discover SDK drift only after a provider outage, a failed audit, or a credential exposure has already forced an emergency migration.

How It Works in Practice

The practical question is whether the gateway can fully normalise the behaviour you need across providers. If it can, standardising on one SDK usually reduces integration risk. A single SDK means one credential flow, one observability model, one place to enforce policy, and fewer places where secrets can leak. It also makes it easier to centralise workload identity and apply consistent controls for logging, token handling, and request signing.

Multiple SDKs make sense when provider-specific features are not portable or when resilience requires true cross-provider failover. That is common in environments using function calling, structured output, tool routing, or model-specific safety features that the gateway cannot translate cleanly. The key is to decide whether the gateway is doing simple pass-through, or whether it is expected to mediate identity, retries, request shaping, and policy enforcement across heterogeneous backends.

For identity-sensitive AI workloads, the operational pattern should be: a gateway issues or brokers short-lived access, the SDK holds only ephemeral credentials, and authorization is evaluated at runtime. That aligns with the direction of NIST Cybersecurity Framework 2.0 and the NHIMG view of Ultimate Guide to NHIs — Standards, where the control objective is not just access, but traceable, revocable machine access. In practice, teams should ask:

• Does one SDK cover the providers and features needed today without translation loss?
• Can the gateway enforce identity, routing, and policy consistently across all SDK paths?
• Are failover and fallback paths tested with real credentials, not just synthetic traffic?
• Will multiple SDKs create separate secret stores, telemetry formats, or patch cycles?

These controls tend to break down in multi-team platforms where each product group pins its own SDK version and the gateway becomes a thin wrapper instead of the policy enforcement point.

Common Variations and Edge Cases

Tighter standardisation often increases short-term dependency on the gateway, requiring organisations to balance simplicity against provider lock-in and feature loss. That tradeoff is real, especially when one provider exposes capabilities that materially improve latency, safety, or tool-use quality.

There is no universal standard for this yet, but current guidance suggests a split model works best in mature environments: one primary SDK for the majority of traffic, plus narrowly approved secondary SDKs for genuinely non-portable features or resilience testing. That keeps the blast radius small while preserving escape hatches.

Edge cases usually involve regulated workloads, agentic systems, or teams with strict separation of duties. In those environments, multiple SDKs can be justified if each SDK is wrapped by the same gateway policy and secret lifecycle. The State of Secrets in AppSec is a useful reminder that fragmentation is expensive: the more SDKs in play, the more likely teams are to accumulate secret sprawl, inconsistent rotation, and delayed remediation. Standardise by default, diversify only when a specific business or resilience requirement cannot be met another way.

Related resources from NHI Mgmt Group

• Should organisations combine multiple AI security frameworks or standardise on one?
• Why is routing AI tasks across multiple models often better than using one model everywhere?
• How do organisations decide whether to standardise on one AI provider or support multiple providers?
• How should security teams govern AI agents that can invoke multiple tools in one session?