Artificial Intelligence (AI)
Vercel AI SDK Custom Provider Integration
Don't get locked into the major providers. We build custom LanguageModelV1 plugins to connect the Vercel AI SDK to your proprietary or self-hosted models.
- Focus
- Custom AI SDK providers
- Engagement
- Fixed-scope plugin build
- Timeline
- From 2 weeks
- Ownership
- 100% yours
Our process for vercel ai sdk custom provider integration
A fixed four-step path from first call to production, with weekly demos and a hard launch date.
Provider audit
We map the model or gateway you need to reach, its auth model, streaming shape, and the AI SDK surface it has to satisfy.
Deliverable
Capability gap analysis
Interface design
We design the provider against the AI SDK's language-model interface so it behaves identically to a first-party provider at the call site.
Deliverable
Provider interface spec
Build & conformance test
We implement streaming, tool calling, structured output, and error mapping, then test against the same suite the official providers satisfy.
Deliverable
Tested provider package
Publish & hand over
We package it for your registry, document the configuration surface, and hand over the repository.
Deliverable
Published package & docs
See where your project fits.
Book your systems auditOverview
The Vercel AI SDK works beautifully with OpenAI and Anthropic out of the box. But what if you are running a fine-tuned Llama model on your own hardware, or using a niche provider that doesn't have an official package?
Our Vercel AI SDK Custom Provider Integration service builds custom provider plugins using the official LanguageModelV1 specification. We make your proprietary models plug seamlessly into the SDK's standard interface.
Key Capabilities
-
Custom Model Wrappers
We build custom classes that translate the SDK's generic prompt format into the specific token requirements of your self-hosted models. -
Native Feature Support
We implement streaming, tool calling, and multimodal support within the custom provider, ensuring you don't lose the SDK's best features when moving off OpenAI. -
Local Development Setup
We configure your environment to seamlessly route between heavy cloud models in production and smaller, localized models during local development.
Why Partner With Us?
- Total Data Privacy: By connecting the SDK to your self-hosted infrastructure, we ensure your sensitive data never leaves your VPC.
- Seamless DX: Your frontend team doesn't need to learn a new API. They can continue using
useChatwhile the custom backend handles the routing. - Specification Experts: We deeply understand the internal abstractions of the SDK and write clean, typed provider code that passes all specification tests.
Take control of your infrastructure without losing the DX of the Vercel AI SDK. Contact us today.
Common questions
Why would we need a custom AI SDK provider?
Three common reasons: you are calling a model the SDK has no provider for, such as a self-hosted open-weight deployment or a regional vendor; you route through an internal gateway that adds auth, quota, or audit logging and needs to look like a normal provider to application code; or you need behaviour the stock provider does not expose, such as custom retry semantics or request signing. In every case the payoff is that application code keeps using the standard SDK calls and stays portable.
Will a custom provider support streaming and tool calling?
Yes, and that is most of the work. A provider that only implements text generation looks fine until an agent needs tool calls or a UI needs token streaming, at which point it fails in ways that are hard to trace. We implement the full interface, including structured output and correct error mapping, and test it against the same conformance expectations the official providers meet.
Can we keep our application code portable afterwards?
That is the point of doing it this way. Because the provider satisfies the standard interface, your application calls the SDK exactly as it would with any first-party provider, and swapping models later is a configuration change rather than a rewrite.
How long does a custom provider take?
Typically two to three weeks, depending on how far the target API diverges from the shape the SDK expects. A vendor with an OpenAI-compatible surface is fast. A bespoke protocol with unusual streaming semantics takes longer, and we establish which one you have during the audit in the first few days.
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