The Mobile App Support SDK Evaluation Checklist Engineering Teams Actually Use
Most SDK evaluations start with a feature matrix. You compare what each platform can do, pick the one with the most checkboxes, and spend the next six months discovering the gaps the vendor didn't mention.
For in-app AI support SDKs, that pattern is especially expensive. The SDK that looks complete on a marketing page often ships a WebView wrapper, holds your data hostage on their servers, and breaks the moment your users go offline. You don't find out until your engineering team is three months in and your users are leaving one-star reviews.
This checklist is what engineering teams use after they've been burned once. It covers the evaluation criteria that actually matter in production — the ones that don't fit neatly into a comparison table.
SDK Footprint and Integration Complexity
The first question isn't "what can this SDK do?" It's "what does shipping this SDK do to our app?"
Binary size: Ask for the installed size, not the download size. Native SDKs for in-app AI support typically add 500KB–2MB to your binary. WebView-based alternatives often add 20–40MB because they ship a full browser runtime. For apps where every megabyte affects download conversion, this matters.
Integration surface: How many lines of code does it take to ship a working prototype? The best SDKs need under 100 lines of Swift or Kotlin. If the getting-started guide requires more than a day of integration work for a basic implementation, something is wrong with the architecture.
Platform coverage: If the SDK only works on iOS or only on Android, you're making a platform decision for your whole support operation. Native SDKs that work on both platforms let you ship the same support experience to all your users without maintaining two separate implementations.
UI customization: Can you make the chat interface match your app's design system, or does it look like a third-party widget dropped into your UI? Native SDKs that use platform UI primitives (SwiftUI, UIKit on iOS; Jetpack Compose, Views on Android) integrate cleanly. WebView-based solutions can't be customized without breaking the integration.
Deployment Model and Data Ownership
This is where most teams get surprised. The marketing page will tell you the SDK "connects to your backend." What it won't tell you is what happens to your users' data in the meantime.
Where does the data go? Some SDK vendors route every user message through their own servers — even for teams that want to stay self-hosted. You think you're running a private deployment, but your users' conversations are going somewhere else. Verify this before you sign anything.
LLM provider independence: If the SDK only works with one LLM provider, you're locked into that provider's pricing, latency, and uptime. Good SDKs let you choose: OpenAI, Anthropic, Ollama, or any OpenAI-compatible API. You should be able to switch providers without re-integrating the SDK.
Self-hosting support: The MIT/AGPL-licensed SDKs let you run everything on your own infrastructure. That means your user messages never leave your network, you control the data retention policy, and you're not dependent on a vendor's uptime. For regulated industries or teams with strict data residency requirements, this isn't optional.
API key management: Does the SDK require you to ship your LLM API keys in the client binary? That's a security risk. The SDK should call your own backend, and your backend should call your LLM provider. The SDK itself should never have direct access to your LLM credentials.
Operator Controls and Approval Workflows
In-app AI support isn't just about answering questions. It's about what the agent is allowed to do on behalf of the user. Without strong operator controls, you're deploying an automated system that can make changes you didn't approve — and you'll discover the failure mode in a support ticket, not in your monitoring dashboard.
Approval flow granularity: Can you control approval at the function level? A well-designed SDK lets you mark individual functions as `autonomous` (run without approval), `approval_required` (user must confirm), or `disabled` (never run). This means sensitive operations like cancellations or data exports require explicit user consent, while routine queries run instantly.
Session logging and trace visibility: When something goes wrong — the agent gives wrong information, makes a bad recommendation, or runs the wrong function — can you reconstruct exactly what happened? The SDK should log every message, every tool call, every approval decision, and make it accessible in an operator dashboard. Without this, you're flying blind.
Policy configuration at runtime: Can you change approval policies without shipping an app update? The best SDKs let you configure what the agent can do from your backend dashboard, without requiring users to update their app. When you discover a problem on a Friday afternoon, you need to be able to shut down a function immediately, not wait for an App Store review.
Multi-step confirmation: Some actions require more than a single "Allow/Deny" prompt. If the agent needs to execute a sequence of operations — verify identity, then update record, then send confirmation — can the SDK model that workflow with proper user confirmation at each step?
Maintenance Burden and Vendor Lock-In
The integration is the easy part. The question you should ask is: what does this look like in 18 months?
SDK update frequency: How often does the vendor ship updates? If the SDK hasn't been updated in 6 months, that's a sign the project is abandoned — and abandoned SDKs become security liabilities in mobile apps.
Breaking changes policy: When the vendor ships a major version update, how do they handle migration? Good SDKs maintain backward compatibility within a major version. Bad SDKs require you to re-integrate from scratch every time they decide to rewrite their API.
Support channel and response time: When something breaks at 2am on a Saturday, what happens? Open-source SDKs with active communities have community support (GitHub issues, Discord). Vendor-backed SDKs should have a clear SLA. Know what you're getting before you need it.
Community size and ecosystem: An SDK used by 50 teams is less likely to have weird edge-case bugs than one used by 5. Check the GitHub star count, the number of open issues, and the recency of the last commit. An active community is a proxy for long-term viability.
The Questions to Ask Before You Commit
Before you sign any contract or open any pull request:
1. "Show me the binary size for a working hello-world integration." If the vendor can't answer this, they're hiding something.
2. "Can I self-host everything, including the conversation data?" If the answer is "we handle that for you" — your data is going to their servers, whether they say so explicitly or not.
3. "Can I switch LLM providers without re-integrating the SDK?" Provider independence means you're not locked into one company's pricing or reliability.
4. "How does the operator see what the agent did in a conversation?" Trace logs aren't optional. You need to be able to reproduce every decision.
5. "What happens when a function behaves badly on a Friday night?" Can you disable it from a dashboard, or do you need to ship an app update?
How to Use This Checklist
Don't try to evaluate everything at once. Start with the questions that are non-negotiable for your specific situation:
- If you're in a regulated industry → start with data ownership and self-hosting.
- If you have a small engineering team → start with integration complexity and maintenance burden.
- If you're replacing a broken incumbent → start with operator controls and trace visibility.
The SDK that wins on every dimension doesn't exist. The SDK that wins on the dimensions that actually matter to your team — that's the one you want.
Or see how ResolveKit compares on these criteria:
- Native SDK vs Web Widget: Why Embedded Support Wins — the architectural difference that matters for production apps
- Self-Hosted Customer Support AI with ResolveKit and Your Own LLM — data ownership, LLM independence, and operator controls in one package