Alternative Cloud AI Platforms: IBM watsonx, Oracle OCI, Databricks & Snowflake Deep Dive

AWS, Azure, and GCP dominate the conversation, but they’re not the only players. IBM, Oracle, Databricks, and Snowflake have built compelling AI platforms—each with unique strengths that might be exactly what your enterprise needs.

I’ve worked with clients using all of these platforms. Here’s an honest look at what each offers and when they make sense.

Series Navigation: Part 8: Cloud LLMOps (AWS/Azure/GCP) → Part 9: DIY LLMOps → Part 10: Alternative Platforms (You are here)

Platform Landscape Overview

IBM watsonx: Enterprise AI with Governance Built-In

IBM’s watsonx platform is designed for enterprises that need AI with guardrails. If you’re in a regulated industry (banking, healthcare, government), IBM’s focus on governance and explainability is compelling.

IBM watsonx Architecture

IBM watsonx Implementation

# ibm_watsonx.py
from ibm_watsonx_ai.foundation_models import Model
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as Params
from ibm_watsonx_ai import Credentials

class WatsonxClient:
    """IBM watsonx.ai client for GenAI."""
    
    def __init__(self, api_key: str, project_id: str, 
                 url: str = "https://us-south.ml.cloud.ibm.com"):
        self.credentials = Credentials(url=url, api_key=api_key)
        self.project_id = project_id
        
    def generate(self, prompt: str, model_id: str = "ibm/granite-13b-chat-v2",
                 max_tokens: int = 1000, temperature: float = 0.7) -> str:
        """Generate text using watsonx foundation models."""
        
        params = {
            Params.DECODING_METHOD: "greedy" if temperature == 0 else "sample",
            Params.MAX_NEW_TOKENS: max_tokens,
            Params.TEMPERATURE: temperature,
            Params.TOP_P: 0.9,
            Params.REPETITION_PENALTY: 1.1
        }
        
        model = Model(
            model_id=model_id,
            params=params,
            credentials=self.credentials,
            project_id=self.project_id
        )
        
        response = model.generate_text(prompt=prompt)
        return response
    
    def generate_with_rag(self, query: str, documents: list[str], 
                          model_id: str = "ibm/granite-13b-chat-v2") -> dict:
        """RAG pattern with watsonx."""
        
        context = "\n\n".join([f"Document {i+1}:\n{doc}" 
                               for i, doc in enumerate(documents)])
        
        prompt = f"""<|system|>
You are a helpful assistant that answers questions based on the provided documents.
Only use information from the documents. If you cannot find the answer, say so.
<|user|>
Documents:
{context}

Question: {query}
<|assistant|>"""
        
        response = self.generate(prompt, model_id=model_id, temperature=0)
        
        return {
            "answer": response,
            "model": model_id,
            "documents_used": len(documents)
        }

# Usage
client = WatsonxClient(
    api_key="your-ibm-cloud-api-key",
    project_id="your-project-id"
)

response = client.generate(
    "Explain the key regulations for AI in banking.",
    model_id="ibm/granite-13b-chat-v2"
)

Oracle Cloud: AI for the Enterprise Database

If your data lives in Oracle, OCI’s AI services let you bring AI to your data—including vector search directly in Oracle Database 23ai.

Oracle OCI AI Architecture

Oracle OCI GenAI Implementation

# oci_genai.py
import oci
from oci.generative_ai_inference import GenerativeAiInferenceClient
from oci.generative_ai_inference.models import (
    CohereChatRequest, ChatDetails, OnDemandServingMode
)

class OCIGenAIClient:
    """Oracle Cloud GenAI client."""
    
    def __init__(self, compartment_id: str, config_file: str = "~/.oci/config"):
        self.config = oci.config.from_file(config_file)
        self.compartment_id = compartment_id
        self.client = GenerativeAiInferenceClient(
            config=self.config,
            service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com"
        )
    
    def chat(self, message: str, model_id: str = "cohere.command-r-plus",
             temperature: float = 0.7, max_tokens: int = 1000) -> str:
        """Chat with Cohere Command R+ on OCI."""
        
        chat_request = CohereChatRequest(
            message=message,
            max_tokens=max_tokens,
            temperature=temperature,
            is_stream=False
        )
        
        chat_details = ChatDetails(
            serving_mode=OnDemandServingMode(model_id=model_id),
            compartment_id=self.compartment_id,
            chat_request=chat_request
        )
        
        response = self.client.chat(chat_details)
        return response.data.chat_response.text

# Vector Search with Oracle Database 23ai
import oracledb

class OracleVectorRAG:
    """RAG using OCI GenAI + Oracle Database 23ai Vector Search."""
    
    def __init__(self, genai_client, db_connection):
        self.genai = genai_client
        self.db = db_connection
    
    def search(self, query: str, top_k: int = 5) -> list[dict]:
        """Vector search in Oracle Database 23ai."""
        
        query_embedding = self.genai.generate_embeddings([query])[0]
        
        with self.db.cursor() as cursor:
            cursor.execute("""
                SELECT id, content, 
                       VECTOR_DISTANCE(embedding, TO_VECTOR(:query_vec), COSINE) as distance
                FROM documents
                ORDER BY distance
                FETCH FIRST :k ROWS ONLY
            """, {"query_vec": str(query_embedding), "k": top_k})
            
            return [{"id": row[0], "content": row[1], "score": 1 - row[2]} 
                    for row in cursor.fetchall()]

Databricks: The Data Lakehouse for AI

Databricks is where data engineers and ML engineers converge. If you’re already using Databricks for data, adding GenAI is seamless—and their MLflow integration is best-in-class.

Databricks AI Architecture

Databricks Implementation

# databricks_genai.py
from databricks.sdk import WorkspaceClient
from databricks.sdk.service.serving import ChatMessage, ChatMessageRole

class DatabricksGenAI:
    """Databricks Mosaic AI client."""
    
    def __init__(self):
        self.client = WorkspaceClient()
    
    def chat(self, messages: list[dict], endpoint: str = "databricks-dbrx-instruct",
             max_tokens: int = 1000, temperature: float = 0.7) -> str:
        """Chat with foundation models on Databricks."""
        
        chat_messages = [
            ChatMessage(
                role=ChatMessageRole[msg["role"].upper()],
                content=msg["content"]
            ) for msg in messages
        ]
        
        response = self.client.serving_endpoints.query(
            name=endpoint,
            messages=chat_messages,
            max_tokens=max_tokens,
            temperature=temperature
        )
        
        return response.choices[0].message.content
    
    def vector_search(self, index_name: str, query: str, 
                      num_results: int = 5) -> list[dict]:
        """Search the vector index."""
        
        results = self.client.vector_search_indexes.query_index(
            index_name=index_name,
            columns=["id", "content", "metadata"],
            query_text=query,
            num_results=num_results
        )
        
        return [{
            "id": r.id,
            "content": r.content,
            "score": r.score
        } for r in results.result.data_array]

# Using Databricks SQL for RAG
from databricks import sql

def rag_with_databricks_sql(query: str, connection) -> str:
    """RAG using Databricks SQL and ai_query function."""
    
    with connection.cursor() as cursor:
        cursor.execute(f"""
            WITH relevant_docs AS (
                SELECT content, 
                       ai_similarity(embedding, ai_embed('{query}')) as score
                FROM documents
                ORDER BY score DESC
                LIMIT 5
            ),
            context AS (
                SELECT concat_ws('\n\n', collect_list(content)) as ctx
                FROM relevant_docs
            )
            SELECT ai_query(
                'databricks-dbrx-instruct',
                concat('Context: ', ctx, '\n\nQuestion: {query}\n\nAnswer:')
            ) as answer
            FROM context
        """)
        
        return cursor.fetchone()[0]

Snowflake: AI Where Your Data Lives

Snowflake’s Cortex AI brings LLMs directly into SQL. No data movement, no complex pipelines—just call AI functions on your data.

Snowflake Cortex Architecture

Snowflake Cortex Implementation

# snowflake_cortex.py
import snowflake.connector
from snowflake.snowpark import Session

class SnowflakeCortexAI:
    """Snowflake Cortex AI client."""
    
    def __init__(self, connection_params: dict):
        self.session = Session.builder.configs(connection_params).create()
    
    def complete(self, prompt: str, model: str = "llama3.1-70b",
                 temperature: float = 0.7) -> str:
        """Generate text using Cortex Complete."""
        
        result = self.session.sql(f"""
            SELECT SNOWFLAKE.CORTEX.COMPLETE(
                '{model}',
                '{prompt.replace("'", "''")}',
                {'temperature': {temperature}}
            ) as response
        """).collect()
        
        return result[0]['RESPONSE']
    
    def rag_query(self, question: str, docs_table: str,
                  text_column: str = "content") -> str:
        """Complete RAG pipeline in Snowflake - single SQL query!"""
        
        result = self.session.sql(f"""
            WITH query_vec AS (
                SELECT SNOWFLAKE.CORTEX.EMBED_TEXT_768('e5-base-v2', '{question}') as qe
            ),
            relevant_docs AS (
                SELECT {text_column},
                       VECTOR_COSINE_SIMILARITY(embedding, (SELECT qe FROM query_vec)) as score
                FROM {docs_table}
                ORDER BY score DESC
                LIMIT 5
            ),
            context AS (
                SELECT LISTAGG({text_column}, '\n\n') as ctx
                FROM relevant_docs
            )
            SELECT SNOWFLAKE.CORTEX.COMPLETE(
                'llama3.1-70b',
                CONCAT(
                    'Based on the following context, answer the question.\n\n',
                    'Context:\n', ctx, '\n\n',
                    'Question: {question}\n\n',
                    'Answer:'
                )
            ) as answer
            FROM context
        """).collect()
        
        return result[0]['ANSWER']

# Usage
cortex = SnowflakeCortexAI({
    "account": "your-account",
    "user": "your-user",
    "password": "your-password",
    "warehouse": "COMPUTE_WH",
    "database": "AI_DB",
    "schema": "PUBLIC"
})

# RAG on your data - it's just SQL!
answer = cortex.rag_query(
    "What were our top selling products last quarter?",
    docs_table="SALES_REPORTS"
)

Platform Comparison Summary

Key Takeaways

• IBM watsonx: Best for enterprises needing AI governance, compliance, and hybrid deployment
• Oracle OCI: Ideal if your data lives in Oracle DB—native vector search in 23ai is powerful
• Databricks: The choice for data teams who want unified data engineering + ML + GenAI
• Snowflake: Perfect for SQL-first teams—Cortex brings AI directly into your queries
• Multi-cloud strategy: Databricks and watsonx work across clouds; Snowflake is cloud-agnostic SaaS

References & Further Reading

• IBM watsonx Documentation – ibm.com/docs/watsonx
• Oracle OCI Generative AI – docs.oracle.com
• Databricks Mosaic AI – docs.databricks.com
• Snowflake Cortex – docs.snowflake.com
• Oracle Database 23ai Vector Search – docs.oracle.com

Using one of these platforms? Share your experience on GitHub or connect on LinkedIn.