Migration Services
OpenAI API to Gemini Migration Services
Move OpenAI-powered applications, prompts, tools, and generative AI workflows to Gemini through a structured migration focused on quality, security, and reliability.
Migration Services
Move OpenAI-powered applications, prompts, tools, and generative AI workflows to Gemini through a structured migration focused on quality, security, and reliability.
Why Gemini?
Process and generate content across text, images, audio, video, and documents using Gemini models designed for multimodal workloads.
Connect AI applications with BigQuery, Cloud Storage, Vertex AI, Google Search grounding, and other Google Cloud services.
Choose models based on reasoning quality, latency, context requirements, throughput, and application cost.
Use Google Cloud IAM, regional controls, monitoring, evaluation, safety settings, and managed throughput options for production workloads.
How We Migrate
Review OpenAI models, endpoints, prompts, system instructions, embeddings, tools, assistants, files, safety controls, usage, and dependencies.
Select Gemini models and define the target API, authentication, grounding, tool architecture, safety settings, monitoring, and rollout plan.
Update API calls, message formats, prompts, tool definitions, schemas, embeddings, streaming, error handling, and authentication.
Compare response quality, structured output, tool use, safety, latency, token usage, retrieval results, and business outcomes.
Release Gemini gradually, monitor production behavior, tune prompts and configurations, and retire approved OpenAI integrations.
Why Codimite?
Codimite combines Google Cloud architecture, application engineering, prompt design, AI evaluation, and production governance expertise.
Start Your MigrationComplete API Discovery. We identify model calls, prompts, schemas, embeddings, tools, retrieval workflows, fallbacks, limits, and connected application services.
Model and Feature Mapping. We map each OpenAI model and API capability to the most suitable Gemini or Vertex AI service.
Evidence-Based Evaluation. We test quality, reliability, latency, safety, and cost against representative business tasks before production cutover.
Controlled Application Rollout. We reduce risk through compatibility testing, shadow traffic, feature flags, phased releases, monitoring, and rollback planning.
Platform Comparison
Both platforms support advanced generative AI applications, but Gemini provides deep multimodal and Google Cloud integration for enterprise workloads.
| Area | OpenAI API | Gemini on Vertex AI |
|---|---|---|
| Platform approach | API platform for OpenAI language, reasoning, multimodal, embedding, image, audio, and agent capabilities | Google Cloud generative AI platform built around Gemini, Vertex AI, grounding, evaluation, and enterprise controls |
| Primary API model | OpenAI Responses, Chat Completions, Embeddings, Realtime, and related APIs | Gemini API through the Google Gen AI SDK, REST, Vertex AI, and supported compatibility endpoints |
| Model selection | OpenAI model families selected by capability, latency, context, and price | Gemini model families selected by reasoning, speed, multimodality, context, throughput, and cost |
| Multimodality | Supports text, image, audio, and other model-dependent inputs and outputs | Native support for text, images, audio, video, and documents across eligible Gemini models |
| Context handling | Context limits depend on the selected OpenAI model | Large-context Gemini models support analysis of extensive documents and multimodal inputs |
| Prompt structure | Uses instructions, messages, roles, and API-specific request formats | Uses system instructions, content parts, roles, generation settings, and multimodal inputs |
| Structured output | Supports schema-constrained responses with eligible models and APIs | Supports schema-based structured outputs with eligible Gemini models and APIs |
| Tool use | Function calling and built-in tools connect models to application actions and data | Function calling and supported built-in tools connect Gemini to APIs, data, search, and computation |
| Embeddings | OpenAI embedding models generate vectors for search and retrieval | Gemini and Vertex AI embedding models support retrieval, classification, clustering, and semantic search |
| Grounding | Retrieval and external tools are implemented through OpenAI platform features or application architecture | Supports grounding with Google Search, enterprise data, Vertex AI RAG services, and application tools |
| Agent development | Supports tool-using applications and agent workflows through OpenAI APIs and SDKs | Supports agent development through Gemini tools, Vertex AI Agent Engine, and Google Cloud integrations |
| Streaming and realtime | Supports streaming responses and realtime voice experiences through eligible APIs | Supports streaming generation and Live API experiences with eligible Gemini models |
| Model customization | Options depend on the selected OpenAI model and fine-tuning support | Supports prompt design, tuning for eligible models, grounding, RAG, and custom model options |
| Evaluation | Evaluation can use OpenAI tools, application tests, and custom benchmarks | Vertex AI provides managed generative AI evaluation and custom evaluation workflows |
| Safety controls | Uses OpenAI model policies, moderation, and application-level safeguards | Provides configurable safety settings, content filtering, responsible AI controls, and application safeguards |
| Authentication | Commonly uses OpenAI project API keys and organizational controls | Uses Google Cloud IAM, service accounts, application default credentials, or supported API-key options |
| Data integration | Integrates through APIs, files, retrieval systems, and external application services | Closely integrated with BigQuery, Cloud Storage, Vertex AI Search, databases, and Google Cloud services |
| OpenAI library compatibility | Native support through official OpenAI SDKs | An OpenAI-compatible endpoint can support selected Gemini calls using OpenAI libraries during transition |
| Monitoring and governance | Usage controls, logs, policies, and monitoring depend on OpenAI and application configuration | Google Cloud logging, monitoring, IAM, audit logs, organization policies, and enterprise governance |
| Best suited for | Applications standardized on OpenAI models and APIs | Applications requiring Gemini multimodality, Google Cloud integration, grounding, and enterprise controls |
FAQs
Codimite can migrate model calls, prompts, system instructions, structured outputs, function tools, embeddings, retrieval workflows, streaming responses, agent logic, and connected applications.
No. OpenAI and Gemini offer similar generative AI capabilities, but their models, request formats, tool behavior, safety controls, authentication, and outputs differ and must be mapped carefully.
Often yes. Existing prompts, JSON schemas, and tool definitions provide a useful starting point, but they must be adapted and tested for Gemini's instruction handling, structured output, and function-calling behavior.
Google Cloud provides an OpenAI-compatible endpoint for selected Gemini capabilities, which can reduce initial application changes. Native Gemini APIs are generally better for accessing the full range of Vertex AI and Gemini features.
Documents are typically re-embedded using a Vertex AI embedding model, and vector indexes may need to be rebuilt. Retrieval, ranking, grounding, citations, and prompt assembly are then validated on Google Cloud.
We compare response quality, factual accuracy, structured outputs, tool calls, retrieval results, latency, safety, token usage, cost, and business outcomes before production cutover.
Assess your models, prompts, tools, embeddings, retrieval workflows, and applications to build a practical Gemini migration roadmap.
Start Your Migration
