Vertex AI Development Services
Pharos Production delivers Google Vertex AI development services for enterprises building cloud-native AI solutions. Our team works with Gemini models, Agent Builder, AutoML, Vertex AI Pipelines, Model Garden and Feature Store to build production ML systems on Google Cloud. We leverage Vertex AI for the full ML lifecycle - dataset management, AutoML for rapid prototyping, custom training on TPU/GPU, model evaluation, endpoint deployment with traffic splitting and monitoring. Gemini integration through Vertex AI gives enterprises access to Google multimodal models with enterprise controls. Pharos Production brings GCP-native expertise - BigQuery ML for SQL-based model training, Vertex AI Pipelines for orchestration, Vertex AI Search for RAG applications, Agent Builder for conversational AI and Workbench for collaborative experimentation. We build AI systems that integrate naturally with existing Google Cloud infrastructure.
- 6+ Vertex AI projects
- 12+ AI engineers
- 15+ pipelines automated
- 25+ AI projects delivered
- 90+ engineers
- 90+ Clutch reviews
Enterprise-grade AI with responsible governance, data privacy and production-ready deployment
What is Vertex AI development?
What we build with Vertex AI
Gemini model integration
Enterprise applications powered by Gemini Pro, Gemini Ultra and Gemini Flash for text, vision, code and multimodal tasks with grounding, function calling and enterprise controls.
Agent Builder conversational AI
Customer-facing chatbots and internal assistants with Vertex AI Agent Builder - data store agents (RAG), conversational flows and integration with Google Workspace.
AutoML for rapid prototyping
No-code model training for image classification, text classification, tabular prediction and video analysis - from labeled data to deployed model in hours.
BigQuery ML integration
ML models trained directly in SQL on BigQuery data - demand forecasting, customer segmentation, churn prediction and recommendation without data movement.
Custom model training
Training custom PyTorch and TensorFlow models on Vertex AI managed infrastructure - TPUs, A100/H100 GPUs, distributed training and hyperparameter tuning.
Vertex AI Search (RAG)
Enterprise search and RAG with Vertex AI Search - document indexing, hybrid retrieval, grounding with citations and integration with Gemini for answer generation.
Google Vertex AI vs AWS SageMaker vs Azure ML
| Factor | Vertex AI | AWS SageMaker / Azure ML |
|---|---|---|
| Foundation models | Gemini (multimodal), Model Garden (100+ models) | AWS: Bedrock. Azure: Azure OpenAI |
| AutoML | Best AutoML with minimal configuration | AWS: Autopilot. Azure: AutoML (good) |
| BigQuery integration | Native BQML - train models in SQL | AWS: Athena ML (limited). Azure: Synapse ML |
| TPU access | Exclusive TPU access for training | AWS: Trainium. Azure: no custom AI chips |
| Conversational AI | Agent Builder with data stores | AWS: Bedrock Agents. Azure: AI Foundry |
| Data analytics | Tightest analytics integration (BQ, Dataflow) | AWS: Glue/Athena. Azure: Synapse |
| Pricing | Competitive, sustained use discounts | AWS: complex pricing. Azure: comparable |
Pharos Production recommends Vertex AI for organizations on Google Cloud, data-heavy workloads with BigQuery, teams wanting the best AutoML experience and Gemini-first architectures. AWS SageMaker offers more ML engineering features. Azure ML suits Microsoft-centric enterprises.
Limitations: Vertex AI has smaller market share than AWS SageMaker, meaning fewer community resources and third-party integrations. Gemini model quality, while improving rapidly, may trail GPT-4 and Claude on some benchmarks. Vertex AI Pipelines is less mature than SageMaker Pipelines for complex orchestration. TPU programming requires framework-specific code (JAX works best, PyTorch XLA has limitations).
Vertex AI Development Benchmark 2026
Proprietary research based on 12+ Google Cloud AI projects delivered by Pharos Production. Dataset covers Gemini integrations, Agent Builder deployments, AutoML models and custom training pipelines. Methodology (Pharos Verified Delivery): aggregated delivery metrics with GCP performance and cost data. Full report available on request.
Vertex AI projects we delivered
- Vertex AI documentation is fragmented across Google Cloud, Firebase and DeepMind resources - API references, SDK versions and console interfaces change without clear migration guides, slowing development and debugging.
- Gemini model versions and capabilities shift rapidly - features like grounding, function calling and safety filters behave differently between Gemini Pro and Ultra, and Google deprecates model versions with shorter notice periods than competitors.
- GCP AI market share is smaller than AWS and Azure - fewer third-party integrations, community tutorials and Stack Overflow answers exist for Vertex AI, making troubleshooting harder and increasing reliance on Google support.
- Vertex AI pricing for custom model training on TPUs requires committed-use reservations for cost-effective rates - on-demand TPU pricing is 30-50% higher than equivalent AWS GPU instances, and spot/preemptible TPU availability is unpredictable.
- Vertex AI provides the tightest integration between AI and data analytics through native BigQuery ML and Dataflow connectivity.
- Gemini models offer competitive multimodal capabilities with text, vision, code and audio understanding in a single API.
- AutoML on Vertex AI delivers production-quality models with minimal ML expertise - from labeled data to deployed endpoint in hours.
- Pharos Production has delivered 12+ Google Cloud AI projects including Gemini integrations, Agent Builder apps and custom ML pipelines.
- A Vertex AI project starts from $35,000-$70,000 and takes 8-14 weeks depending on model complexity and GCP integration requirements.
Reviews
Independent reviews from Clutch, GoodFirms and Google - verified client feedback on our software projects
Based on 9 verified client reviews
Frequently asked questions
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Vertex AI excels at data-heavy workloads with native BigQuery integration, offers the best AutoML experience, provides exclusive TPU access for training and has the tightest analytics-to-ML pipeline. Choose Vertex AI when your data lives in BigQuery or you are already on Google Cloud.
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Gemini Pro and Ultra are competitive with GPT-4 and Claude on most benchmarks, with particular strength in multimodal tasks (image, video, audio understanding). Gemini Flash offers the best speed-to-quality ratio for latency-sensitive applications.
Model choice depends on specific task requirements.
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BigQuery ML lets you train and serve ML models using SQL queries directly on BigQuery data. It is ideal for analysts who know SQL but not Python - demand forecasting, customer segmentation, churn prediction and recommendation.
Models train on the full dataset without data export.
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Agent Builder creates conversational AI applications with data stores (RAG over your documents), search agents (enterprise search), conversational agents (multi-turn dialogue) and custom tools. It integrates with Gemini for answer generation and supports deployment to web, mobile and Google Chat.
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Gemini integration MVPs start from $35,000-$60,000. AutoML projects range from $30,000 to $80,000.
Enterprise ML platforms with custom training and serving cost $80,000 to $250,000+. GCP infrastructure costs are additional.
Choose your cooperation model
Core software architecture, initial UI/UX, working prototype in 3 months
Software architecture, UI/UX, customized software development, manual and automated testing, cloud deployment
Comprehensive software architecture and documentation, UI/UX design layouts, UI kit, clickable prototypes, cloud deployment, continuous integration, as well as automated monitoring and notifications.
Prices vary based on project scope, complexity, timeline and requirements. Contact us for a personalized estimate.
An approach to the development cycle
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Team Assembly
Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.
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MVP
We’ll design, build, and launch your MVP, ensuring it meets the core requirements of your software solution.
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Production
We’ll create a complete software solution that is custom-made to meet your exact specifications.
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Ongoing
Continuous Support
Our company will be right there with you, keeping your software solution running smoothly, fixing issues, and rolling out updates.
Partnerships & Awards
Recognized on Clutch, GoodFirms and The Manifest for software engineering excellence
Build with Vertex AI
90+ engineers ready to deliver your Vertex AI project on time and within budget
What happens next?
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Contact us
Contact us today to discuss your project. We’re ready to review your request promptly and guide you on the best next steps for collaboration
Same day -
NDA
We’re committed to keeping your information confidential, so we’ll sign a Non-Disclosure Agreement
1 day -
Plan the Goals
After we chat about your goals and needs, we’ll craft a comprehensive proposal detailing the project scope, team, timeline and budget
3-5 days -
Finalize the Details
Let’s connect on Google Meet to go through the proposal and confirm all the details together!
1-2 days -
Sign the Contract
As soon as the contract is signed, our dedicated team will jump into action on your project!
Same day
Our offices
Headquarters in Las Vegas, Nevada. Engineering office in Kyiv, Ukraine.