Prism PHP vs Laravel AI SDK: I Used Both in Production. Here’s My Honest Verdict.

Two packages, one problem — integrating LLMs into a Laravel app. Prism is community-built, elegant, and provider-agnostic from day one. The Laravel AI SDK is official, broad, and deeply integrated with the framework. Here’s the honest comparison: where each shines, where each fails, and which one I’d pick for your specific use case.

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When the Laravel AI SDK shipped with Laravel 13 in March 2026, it created a genuine choice that didn’t exist before. Prism PHP (by TJ Miller and Echo Labs) had been the de facto standard for LLM integration in Laravel for over a year — polished, battle-tested, and elegant. Then the official SDK arrived with first-party framework integration, pgvector support, and the backing of the Laravel team.

I’ve used both in production projects over the past six months. One for a customer-facing AI chat feature. One for a document intelligence pipeline. One for an internal automation tool. This is the honest account of what I found.

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The Fundamental Difference

Understanding what each package is designed for changes how you evaluate them.

Prism PHP is inspired by the Vercel AI SDK — a unified interface that abstracts away provider differences. Its philosophy: your application code should be provider-agnostic. Swap OpenAI for Anthropic for Mistral by changing one line. The fluent builder API is its signature feature.

Laravel AI SDK (laravel/ai) is the official first-party package, built by the Laravel team and designed to feel like a native Laravel feature. It ships with framework integrations that Prism doesn’t have: native pgvector support in the query builder, Artisan commands, and the SimilaritySearch agent tool. Its philosophy: AI as a first-class Laravel citizen.

Neither is wrong. They solve the same core problem with different priorities.

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Prism PHP: The Detailed Picture

Installation and Setup

composer require prism-php/prism
php artisan vendor:publish --tag=prism-config

// config/prism.php — configure your providers
return [
    'providers' => [
        'openai' => [
            'api_key' => env('OPENAI_API_KEY'),
        ],
        'anthropic' => [
            'api_key' => env('ANTHROPIC_API_KEY'),
        ],
        'ollama' => [
            'url' => env('OLLAMA_URL', 'http://localhost:11434'),
        ],
        'mistral' => [
            'api_key' => env('MISTRAL_API_KEY'),
        ],
    ],
];

Text Generation — The Fluent Builder

use Prism\Prism\Facades\Prism;
use Prism\Prism\Enums\Provider;

// Basic text generation
$response = Prism::text()
    ->using(Provider::Anthropic, 'claude-3-5-sonnet-20241022')
    ->withSystemPrompt('You are a helpful Laravel developer assistant.')
    ->withPrompt('What is the difference between hasOne and hasMany?')
    ->asText();

echo $response->text;
echo $response->usage->promptTokens;
echo $response->usage->completionTokens;

The fluent builder is where Prism shines. Every option reads naturally:

$response = Prism::text()
    ->using(Provider::OpenAI, 'gpt-4o')
    ->withSystemPrompt(view('prompts.customer-support'))  // Blade view as prompt
    ->withMessages($conversationHistory)                   // multi-turn conversation
    ->withMaxTokens(1000)
    ->usingTemperature(0.7)
    ->asText();

Provider Switching — The Killer Feature

// Development: Ollama locally — zero API cost
$response = Prism::text()
    ->using(Provider::Ollama, 'llama3.2')
    ->withPrompt($prompt)
    ->asText();

// Production: Anthropic Claude
$response = Prism::text()
    ->using(Provider::Anthropic, 'claude-3-5-sonnet-20241022')
    ->withPrompt($prompt)
    ->asText();

// The rest of the code is identical
// One line changes: ->using(Provider::Ollama, ...) → ->using(Provider::Anthropic, ...)

In practice, I run Ollama locally during development and switch to Claude or GPT-4o in production via an environment variable. The application code never changes:

// config/prism.php or .env-driven helper
$response = Prism::text()
    ->using(
        config('ai.provider'),  // 'ollama' in local, 'anthropic' in production
        config('ai.model'),
    )
    ->withPrompt($prompt)
    ->asText();

Structured Output with Prism

use Prism\Prism\Schema\ObjectSchema;
use Prism\Prism\Schema\StringSchema;
use Prism\Prism\Schema\ArraySchema;

$response = Prism::structured()
    ->using(Provider::OpenAI, 'gpt-4o')
    ->withSchema(new ObjectSchema(
        name:        'product_analysis',
        description: 'Analysis of a product description',
        properties: [
            new StringSchema('sentiment',   'Overall sentiment: positive, neutral, or negative'),
            new StringSchema('category',    'Product category'),
            new ArraySchema('keywords',     'Key product keywords', new StringSchema('keyword', '')),
            new StringSchema('summary',     'One-sentence summary'),
        ],
        requiredFields: ['sentiment', 'category', 'keywords', 'summary']
    ))
    ->withPrompt("Analyse this product: {$productDescription}")
    ->asStructured();

$data = $response->structured;
echo $data['sentiment'];  // 'positive'
echo $data['category'];   // 'electronics'

Tool Use (Function Calling)

use Prism\Prism\Tool;

// Define a tool that the LLM can call
$weatherTool = Tool::as('get_weather')
    ->for('Get the current weather for a location')
    ->withStringParameter('location', 'The city and country')
    ->using(function (string $location): string {
        // Your actual implementation
        $weather = WeatherService::fetch($location);
        return "Temperature: {$weather->temp}°C, Conditions: {$weather->description}";
    });

$response = Prism::text()
    ->using(Provider::Anthropic, 'claude-3-5-sonnet-20241022')
    ->withTools([$weatherTool])
    ->withMaxSteps(5)  // allow up to 5 tool call iterations
    ->withPrompt('What is the weather in Mumbai and Delhi right now?')
    ->asText();

// Prism handles the tool call loop automatically
echo $response->text;
// "The weather in Mumbai is 32°C and humid. Delhi is 28°C with clear skies."

Multi-Turn Conversations

use Prism\Prism\ValueObjects\Messages\UserMessage;
use Prism\Prism\ValueObjects\Messages\AssistantMessage;

// Build conversation history
$messages = [
    new UserMessage('What is Laravel?'),
    new AssistantMessage('Laravel is a PHP web framework...'),
    new UserMessage('What is its most recent version?'),
];

$response = Prism::text()
    ->using(Provider::OpenAI, 'gpt-4o')
    ->withMessages($messages)
    ->asText();

Multi-Modal: Images and Documents

use Prism\Prism\ValueObjects\Messages\UserMessage;
use Prism\Prism\ValueObjects\Messages\Support\Image;
use Prism\Prism\ValueObjects\Messages\Support\Document;

// Analyse an image
$response = Prism::text()
    ->using(Provider::Anthropic, 'claude-3-5-sonnet-20241022')
    ->withMessages([
        new UserMessage(
            'What is in this invoice? Extract the total amount and vendor name.',
            additionalContent: [
                Image::fromPath('/path/to/invoice.jpg'),
            ]
        )
    ])
    ->asText();

// Pass a PDF document
$response = Prism::text()
    ->using(Provider::Anthropic, 'claude-3-5-sonnet-20241022')
    ->withMessages([
        new UserMessage(
            'Summarise the key points from this contract.',
            additionalContent: [
                Document::fromPath('/path/to/contract.pdf'),
            ]
        )
    ])
    ->asText();

Prism’s Testing Support

use Prism\Prism\Testing\PrismFake;
use Prism\Prism\ValueObjects\Usage;

it('generates a product summary', function () {
    $fake = Prism::fake([
        PrismFake::response(text: 'A high-quality mechanical keyboard with RGB lighting.'),
    ]);

    $result = app(ProductSummariser::class)->summarise($product);

    expect($result)->toBe('A high-quality mechanical keyboard with RGB lighting.');

    $fake->assertCallCount(1);
    $fake->assertPromptContains('keyboard');
});

The PrismFake makes testing LLM-integrated code clean — no API calls, deterministic output, assertion helpers for what was called and with what.

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Laravel AI SDK: The Detailed Picture

Installation

composer require laravel/ai
php artisan vendor:publish --provider="Laravel\Ai\AiServiceProvider"

// .env
AI_DEFAULT_PROVIDER=openai
OPENAI_API_KEY=sk-your-key

Basic Usage

use Illuminate\Support\Facades\AI;

// Text generation
$response = AI::text('Explain Laravel queues in simple terms.');
echo $response->text;

// With system prompt and model
$response = AI::text(
    prompt: 'What is pgvector?',
    system: 'You are a database expert.',
    model:  'gpt-4o',
);

Embeddings and pgvector Integration

This is where the Laravel AI SDK has a clear advantage over Prism. Native pgvector support in the query builder:

// Generate embeddings
$embedding = AI::embed('Search query text')->embeddings[0]->embedding;

// Store as a vector column (in migrations)
$table->vector('embedding', dimensions: 1536)->index();

// Semantic search — native query builder support
$results = DocumentChunk::whereVectorSimilarTo('embedding', $embedding, limit: 5)->get();

Prism has no equivalent to whereVectorSimilarTo. If you’re building RAG pipelines or semantic search, the Laravel AI SDK has this out of the box.

Streaming

// Laravel AI SDK streaming
$stream = AI::text(prompt: $prompt, model: 'gpt-4o', stream: true);

foreach ($stream as $chunk) {
    echo $chunk->text;
    ob_flush();
    flush();
}

// Prism streaming
$response = Prism::text()
    ->using(Provider::OpenAI, 'gpt-4o')
    ->withPrompt($prompt)
    ->asStream();

foreach ($response as $chunk) {
    echo $chunk->text;
}

Both support streaming. The API surface is slightly different but equivalent in capability.

The SimilaritySearch Agent Tool

use Laravel\Ai\Tools\SimilaritySearch;
use App\Models\DocumentChunk;

$agent = AI::agent()
    ->model('gpt-4o')
    ->system('You are a helpful assistant. Search documents before answering.')
    ->tools([
        SimilaritySearch::usingModel(DocumentChunk::class, 'embedding')
            ->returning(['content', 'section_title'])
            ->limit(5),
    ]);

$response = $agent->ask('What is the refund policy?');

This is genuinely impressive. The agent automatically decides when to search, what to search for, and how to combine results — with a first-class integration that Prism doesn’t have a direct equivalent for.

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Head-to-Head Comparison

Provider Support

Provider	Prism PHP	Laravel AI SDK
OpenAI	✅	✅
Anthropic	✅	✅
Ollama (local)	✅	✅
Mistral	✅	✅
Google Gemini	✅	✅
Groq	✅	Limited
xAI (Grok)	✅	Limited
Cohere	✅	Limited
DeepSeek	✅	Limited

Prism supports more providers out of the box. The Laravel AI SDK focuses on the major providers with deeper integration.

Feature Comparison

Feature	Prism PHP	Laravel AI SDK
Text generation	✅ Fluent builder	✅ Simple facade
Structured output	✅ Schema-based	✅
Tool use	✅	✅
Multi-turn conversations	✅	✅
Streaming	✅	✅
Multi-modal (images)	✅	✅
Embeddings	✅	✅
pgvector integration	❌	✅ Native
SimilaritySearch agent tool	❌	✅
Blade view prompts	✅	❌
Ollama local development	✅ Seamless	✅
Testing fakes	✅ PrismFake	✅
Provider switching via config	✅ Simple	✅
Community packages	Growing	Early
Documentation quality	Excellent	Good

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What I Actually Found in Production

Where Prism Won

Project: Customer-facing AI chat with multiple providers

We needed to A/B test Anthropic vs OpenAI responses for a customer support chat feature. We also needed local development to cost nothing.

Prism was clearly the right choice. Provider switching is its core feature. The fluent builder made prompts readable and maintainable. PrismFake made testing fast and reliable. We ran Ollama locally, Anthropic in staging, and OpenAI in production — zero code changes.

The Blade view as system prompt feature is underrated:

->withSystemPrompt(view('prompts.support-agent', [
    'company'  => $company->name,
    'products' => $company->products->pluck('name'),
    'tone'     => $company->brand_tone,
]))

Dynamic, maintainable, version-controlled system prompts as Blade views. Prism’s most quietly useful feature.

Project: Multi-provider fallback

When one provider is rate-limited or down, fall back to another:

function generateWithFallback(string $prompt): string
{
    $providers = [
        [Provider::Anthropic, 'claude-3-5-sonnet-20241022'],
        [Provider::OpenAI,    'gpt-4o'],
        [Provider::Ollama,    'llama3.2'],
    ];

    foreach ($providers as [$provider, $model]) {
        try {
            return Prism::text()
                ->using($provider, $model)
                ->withPrompt($prompt)
                ->asText()
                ->text;
        } catch (\Exception $e) {
            Log::warning("Provider {$provider->value} failed, trying next...");
            continue;
        }
    }

    throw new \RuntimeException('All AI providers failed.');
}

Clean, readable, provider-agnostic. This would be more verbose with the Laravel AI SDK.

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Where Laravel AI SDK Won

Project: Document intelligence with semantic search

The pgvector integration was the deciding factor. Instead of wiring up pgvector queries manually (which requires raw SQL or a third-party package), the Laravel AI SDK gave us:

// One line to store embeddings
$table->vector('embedding', dimensions: 1536)->index();

// One line to search
DocumentChunk::whereVectorSimilarTo('embedding', $queryEmbedding, limit: 5)->get();

And the SimilaritySearch agent tool meant the AI could decide what to search for rather than us hardcoding the retrieval logic. For a document Q&A system, this is a significant advantage.

Project: Internal automation agent

For a task where the AI needed to call multiple tools in sequence — search a database, make an API call, write a result back — the agent abstraction in the Laravel AI SDK felt more natural:

$agent = AI::agent()
    ->model('gpt-4o')
    ->system('You process customer refund requests. Search the order, validate it, then process the refund.')
    ->tools([
        SimilaritySearch::usingModel(Order::class, 'embedding'),
        new ValidateRefundEligibilityTool(),
        new ProcessRefundTool(),
    ]);

$result = $agent->ask("Process refund for order #INV-2026-4521");

The agent loop handles multi-step tool calling automatically. Prism can do this with ->withMaxSteps(), but the Laravel AI SDK’s agent abstraction felt slightly more intentional for agentic use cases.

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The Honest Verdict

Choose Prism PHP when:

• You need provider flexibility. Prism supports more providers and switching is its core feature
• Local development with Ollama matters. The developer experience is seamless
• You’re building multi-turn conversations. The message builder is cleaner
• You want Blade views as prompts. A genuinely useful feature for dynamic, maintainable prompts
• Your team values community packages. Prism has a growing ecosystem
• You need providers the Laravel AI SDK doesn’t support (Groq, xAI, Cohere, DeepSeek)

Choose Laravel AI SDK when:

• You’re building RAG or semantic search. Native pgvector integration is a clear win
• You want the SimilaritySearch agent tool. No equivalent in Prism
• Framework integration depth matters. Being first-party means deeper integration over time
• You want Artisan commands and framework primitives
• You prefer official support and Laravel’s long-term backing

Can you use both?

Yes — and it’s not a bad idea for complex applications. Use the Laravel AI SDK for embeddings, pgvector, and agent tools. Use Prism for text generation where you want provider flexibility and the fluent builder API. They don’t conflict.

// Laravel AI SDK for embeddings and RAG
$embedding = AI::embed($text)->embeddings[0]->embedding;
$chunks    = DocumentChunk::whereVectorSimilarTo('embedding', $embedding)->get();

// Prism for text generation with the retrieved context
$response = Prism::text()
    ->using(Provider::Anthropic, 'claude-3-5-sonnet-20241022')
    ->withSystemPrompt($systemPromptWithContext)
    ->withPrompt($userQuestion)
    ->asText();

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The Decision Framework

Are you building RAG, semantic search, or vector-based features?
├── Yes → Laravel AI SDK (native pgvector) — Prism can't match this
└── No
    │
    Do you need to run against 3+ providers or switch providers frequently?
    ├── Yes → Prism (provider-agnostic is its core strength)
    └── No
        │
        Is local development with zero API cost important?
        ├── Yes → Both support Ollama, but Prism's DX is slightly better
        └── No
            │
            Do you value official Laravel backing and long-term framework integration?
            ├── Yes → Laravel AI SDK
            └── No → Either — pick based on API style preference

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Final Thoughts

Six months ago, Prism was the only serious choice for LLM integration in Laravel. The Laravel AI SDK changed that — not by being better in every dimension, but by owning the vector/embedding space completely and providing first-party framework depth that a community package can’t match.

Prism is not going away. It’s actively maintained, has excellent documentation, supports more providers, and its fluent builder remains the most readable LLM API in the PHP ecosystem. For applications that are primarily about text generation — chatbots, summarisation, classification, content generation — Prism is still the better daily driver.

The Laravel AI SDK wins on infrastructure integration. If your application involves document intelligence, semantic search, or RAG pipelines, the pgvector integration and SimilaritySearch tool justify the choice on their own.

If you’re starting a new project today and you’re not sure: start with Prism for its ease of use and documentation quality. Add the Laravel AI SDK when you need pgvector. They work fine together.