Vercel AI SDK vs AWS Bedrock SDK 2026

Two SDKs dominate how JavaScript developers build AI applications in 2026: the Vercel AI SDK and the AWS Bedrock SDK. They solve the same fundamental problem — connecting your application to foundation models — but from completely different starting points, with different priorities, and optimized for different teams.

The Vercel AI SDK is a frontend-first, open-source library designed around streaming UI components and multi-provider model switching. The AWS Bedrock SDK is enterprise-grade infrastructure, built for teams that need VPC isolation, fine-tuning, SLA guarantees, and deep AWS integration. Choosing between them comes down to your team's existing stack, compliance requirements, and how much infrastructure abstraction you want.

TL;DR

Use Vercel AI SDK if you're building on Next.js, Remix, or any serverless platform, want a unified interface across multiple AI providers, need streaming chat UI out of the box, or want to get from zero to demo in under an hour. Use AWS Bedrock SDK if you need VPC isolation, fine-tuning with proprietary data, SLA guarantees, AWS IAM-based auth, or are already running on AWS and want billing consolidated.

Key Takeaways

• Vercel AI SDK supports OpenAI, Anthropic, Google, Mistral, Amazon Bedrock, and 20+ other providers through a unified interface; switching models requires changing one string
• AWS Bedrock SDK provides native access to Claude (Anthropic), Titan (Amazon), Llama, Mistral, and more — but through AWS-specific APIs, not a unified abstraction
• Vercel AI SDK has built-in React hooks (useChat, useCompletion) that handle streaming, loading states, and error recovery; Bedrock SDK requires manual UI integration
• AWS Bedrock runs fully within your VPC; Vercel AI SDK routes through Vercel's infrastructure or directly to providers
• Fine-tuning and continued pre-training are Bedrock-native features; Vercel AI SDK does not support custom model deployment
• Local development experience strongly favors Vercel AI SDK — one env var to switch providers vs. AWS credentials, region config, and IAM setup
• Bedrock pricing is pure pay-per-token with no SDK overhead; Vercel AI Gateway adds a thin proxy layer with optional caching

Pricing Models

Vercel AI SDK Pricing

The Vercel AI SDK itself is free and open-source (Apache 2.0). You pay the underlying model provider directly — OpenAI, Anthropic, Google, etc. Vercel's AI Gateway (optional) adds routing, caching, and observability on top, billed through your Vercel plan.

Component	Cost
AI SDK library	Free
Vercel AI Gateway (Pro)	Included with Pro plan ($20/month)
Model inference	Billed by provider (e.g., Claude Sonnet 4.5: $3/1M input, $15/1M output)
Vercel Functions (to host backend)	Included in free tier up to limits

AWS Bedrock SDK Pricing

AWS Bedrock charges per token with no platform fee. You pay for inference, and optionally for provisioned throughput (for guaranteed performance) and model customization (fine-tuning compute).

Component	Cost
On-demand inference (Claude Sonnet 4.5)	$3/1M input tokens, $15/1M output tokens
Provisioned throughput	$22.40/hour per model unit (1 model unit = specific throughput capacity)
Fine-tuning (Llama 3)	~$0.0008/token for training
Cross-region inference	Same per-token rate, no surcharge
Data Transfer out	Standard AWS rates ($0.09/GB first 10TB)

At equal usage levels, token costs are comparable — both SDKs access the same underlying models at roughly the same token prices. The real cost difference is in infrastructure: Bedrock requires running VPC infrastructure and IAM roles; Vercel AI SDK has near-zero infrastructure overhead.

Supported Models

Vercel AI SDK Providers (2026)

Provider	Models
OpenAI	GPT-4o, GPT-4o mini, o1, o3, o3-mini
Anthropic	Claude 4 Opus, Claude 4 Sonnet, Claude 3.5 Haiku
Google	Gemini 2.0 Flash, Gemini 2.0 Pro, Gemini 1.5 Flash
Amazon Bedrock	All Bedrock-supported models via Bedrock provider
Mistral	Mistral Large, Mistral Nemo
xAI	Grok-2
Together AI	Llama 3.3, Qwen 2.5, FLUX
Replicate	Any Replicate-hosted model
Groq	Llama 3.3 (ultra-fast inference)
Cohere	Command R+

AWS Bedrock Models (2026)

Provider	Models
Anthropic	Claude 4 Opus, Claude 4 Sonnet, Claude 3.5 Haiku
Amazon	Titan Text, Titan Embeddings, Nova Micro/Lite/Pro
Meta	Llama 3.3, Llama 3.2
Mistral	Mistral Large 2, Mistral Small
Cohere	Command R+
Stability AI	Stable Image Ultra
AI21 Labs	Jamba

Key difference: Vercel AI SDK can access non-Bedrock models (OpenAI GPT-4o, xAI Grok) that AWS Bedrock does not offer. Bedrock has exclusive access to Amazon's proprietary Titan and Nova models.

Streaming Support

Both SDKs support streaming, but the developer experience is dramatically different.

Vercel AI SDK Streaming

The AI SDK was built around streaming from day one. The streamText function returns a ReadableStream compatible with both Node.js and Edge runtimes. The useChat hook handles all streaming state in React:

// Backend route — Next.js App Router
import { streamText } from "ai";
import { anthropic } from "@ai-sdk/anthropic";

export async function POST(req: Request) {
  const { messages } = await req.json();

  const result = await streamText({
    model: anthropic("claude-sonnet-4-5"),
    messages,
    maxTokens: 1024,
  });

  return result.toDataStreamResponse();
}

// Frontend component — React
import { useChat } from "ai/react";

export function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat();

  return (
    <div>
      {messages.map((m) => (
        <div key={m.id}>{m.content}</div>
      ))}
      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
        <button type="submit">Send</button>
      </form>
    </div>
  );
}

AWS Bedrock SDK Streaming

Bedrock supports streaming via the InvokeModelWithResponseStream command. It's more verbose and requires manual parsing of the event stream:

import {
  BedrockRuntimeClient,
  InvokeModelWithResponseStreamCommand,
} from "@aws-sdk/client-bedrock-runtime";

const client = new BedrockRuntimeClient({ region: "us-east-1" });

async function streamFromBedrock(prompt: string) {
  const command = new InvokeModelWithResponseStreamCommand({
    modelId: "anthropic.claude-sonnet-4-5-v1:0",
    contentType: "application/json",
    accept: "application/json",
    body: JSON.stringify({
      anthropic_version: "bedrock-2023-05-31",
      messages: [{ role: "user", content: prompt }],
      max_tokens: 1024,
    }),
  });

  const response = await client.send(command);

  for await (const event of response.body!) {
    if (event.chunk?.bytes) {
      const chunk = JSON.parse(
        new TextDecoder().decode(event.chunk.bytes)
      );
      if (chunk.type === "content_block_delta") {
        process.stdout.write(chunk.delta.text);
      }
    }
  }
}

Verdict: Vercel AI SDK provides a significantly better streaming DX. Bedrock requires knowing each model's specific request/response format.

Edge Compatibility

Vercel AI SDK was designed for edge runtimes. All core functions are compatible with Cloudflare Workers, Vercel Edge Functions, and Deno Deploy — no Node.js-specific APIs.

AWS Bedrock SDK requires the full @aws-sdk/client-bedrock-runtime package, which is not edge-compatible by default due to Node.js dependencies. Running Bedrock from edge functions typically requires either a proxy layer or a lightweight custom fetch-based client.

// Vercel AI SDK — works in Edge Runtime natively
export const runtime = "edge";

export async function POST(req: Request) {
  const result = await streamText({
    model: openai("gpt-4o"),
    prompt: "Hello from the edge!",
  });
  return result.toDataStreamResponse();
}

Local Development

Vercel AI SDK

Set one environment variable and you're running:

ANTHROPIC_API_KEY=sk-ant-...

Switch providers by changing the model import. No IAM, no roles, no credentials file. Use the MockLanguageModelV1 for testing without API calls:

import { MockLanguageModelV1, generateText } from "ai";

const model = new MockLanguageModelV1({
  doGenerate: async () => ({
    rawCall: { rawPrompt: null, rawSettings: {} },
    finishReason: "stop",
    usage: { promptTokens: 10, completionTokens: 20 },
    text: "Mock response",
  }),
});

AWS Bedrock SDK

Local development requires AWS credentials configured via ~/.aws/credentials or environment variables, plus the correct IAM permissions. If your team uses AWS SSO, add token refresh to your local dev workflow. First-time setup is noticeably more involved:

aws configure sso
# or
export AWS_ACCESS_KEY_ID=...
export AWS_SECRET_ACCESS_KEY=...
export AWS_SESSION_TOKEN=...  # if using assumed roles
export AWS_REGION=us-east-1

When to Choose Vercel AI SDK

• Building on Next.js, Remix, Astro, or any serverless platform
• Need to switch between AI providers without rewriting code
• Want streaming chat UI with React hooks out of the box
• Team is TypeScript-first and wants minimal infrastructure
• Building a consumer-facing product where time-to-market matters
• Need edge runtime compatibility (Cloudflare Workers, Vercel Edge)

When to Choose AWS Bedrock SDK

• Already on AWS and want billing in one place
• Need VPC isolation and private networking to models
• Compliance requirements mandate data never leaving your AWS account
• Planning to fine-tune models on proprietary data
• Need provisioned throughput with guaranteed performance SLAs
• Building enterprise workflows deeply integrated with other AWS services (Lambda, SageMaker, S3)

Can You Use Both?

Yes. The Vercel AI SDK includes an official Amazon Bedrock provider (@ai-sdk/amazon-bedrock), so you can use Bedrock's infrastructure while keeping the AI SDK's unified interface and React hooks. This is the best of both worlds for AWS-committed teams who want a better developer experience.

import { createAmazonBedrock } from "@ai-sdk/amazon-bedrock";
import { streamText } from "ai";

const bedrock = createAmazonBedrock({
  region: "us-east-1",
  // Uses standard AWS credential chain automatically
});

const result = await streamText({
  model: bedrock("anthropic.claude-sonnet-4-5-v1:0"),
  prompt: "Best of both worlds",
});

For more on building AI applications, see our Vercel AI SDK vs LangChain comparison, the Claude API developer guide, and our LLM API pricing comparison.

Methodology

This comparison is based on the Vercel AI SDK 4.x documentation, AWS Bedrock SDK v3 documentation, official pricing pages as of March 2026, and community benchmarks from the Vercel and AWS developer forums.