Comparing as AI LLM APIs (Foundation Models)Google Cloud Vertex AI vs Together AI
Google Cloud Vertex AI
Together AI
Core Differences
The fundamental difference lies in their primary focus and architecture. Google Cloud Vertex AI is an end-to-end MLOps platform and an AI agent development ecosystem built atop Google Cloud's extensive infrastructure. It provides managed services for the entire ML lifecycle, from data ingestion to model deployment and monitoring, with an increasing emphasis on building sophisticated, stateful AI agents. Its strength is in offering a unified, governed environment for both traditional ML and generative AI agents, integrating deeply with other Google Cloud services.
Together AI, on the other hand, is an AI Native Cloud specifically engineered for high-performance inference, fine-tuning, and training of open-source models. It's a vertically integrated GPU cloud provider that abstracts away the complexities of managing raw GPUs, offering serverless APIs and dedicated compute for hundreds of open-source models. Its core value is delivering speed, cost-efficiency, and developer-friendly access to the open-source AI landscape, with an OpenAI-compatible API facilitating easy migration.
Verdict by Category
Best for Enterprise MLOps & Governance
Its comprehensive MLOps tooling, deep cloud integration, and enterprise-grade governance are unmatched for large organizations.
Best for AI Agent Development
The dedicated Agent Studio, ADK, and Memory Bank make it a purpose-built platform for complex AI agents.
Best for Open-Source Model Performance & Speed
Engineered from systems research, it offers industry-leading inference speeds and optimized infrastructure for open models.
Best for Cost-Effective Open-Source Inference
Its competitive per-token pricing and efficient GPU utilization make open-source model inference highly economical.
Best for Cloud Ecosystem Integration
Native integration with BigQuery, Colab Enterprise, and other Google Cloud services provides a seamless experience.
Best for GPU Infrastructure Access
Offers on-demand and reserved access to cutting-edge GPUs (H100, B200) with competitive pricing directly.
Editor's Take
Honest opinion from our review team
As an editor evaluating these platforms, I found that Google Cloud Vertex AI truly embodies the enterprise-grade, everything-in-one-place philosophy. The sheer breadth of its capabilities, from comprehensive MLOps to the innovative agent-first approach, is impressive. However, this depth comes with a steep learning curve; navigating the console and understanding the interdependencies of services can be daunting for newcomers to Google Cloud. The recent rebranding to Gemini Enterprise Agent Platform, while signifying a clear strategic direction, also adds a layer of initial confusion. I appreciate the $300 free credits as a valuable sandbox to explore its powerful features without immediate cost pressure.
On the other hand, Together AI felt like a breath of fresh air for open-source enthusiasts and performance-driven developers. Its focus is sharp: make open-source AI fast and accessible. The OpenAI-compatible API is a huge win for quick migrations, and the raw speed of inference is palpable. While its pricing model, like Vertex AI's, requires careful calculation, the transparency around per-token costs for open models is a significant advantage. I found it incredibly efficient for specific tasks like batch inference or fine-tuning a Llama model. It's less about a sprawling ecosystem and more about delivering specialized, high-performance AI compute with a developer-first mindset.
Detailed Comparison
Both Google Cloud Vertex AI and Together AI employ a pay-as-you-go pricing model, leading to inherent complexity as costs are granularly tied to specific services and resource consumption.
- Google Cloud Vertex AI's pricing is deeply integrated into the broader Google Cloud structure, meaning costs are accrued across compute (machine types, accelerators), storage, data transfer, and specific service usage (e.g., pipeline runs, model evaluation, agent runtime, Vector Search, generative AI APIs). While this offers extreme flexibility and fine-grained cost control, it makes total cost estimation challenging without using the pricing calculator or consulting sales, especially for custom model training. The $300 in free credits for new customers is a significant advantage for initial exploration and proof-of-concept development, effectively offering a robust free tier for getting started.
- Together AI also uses pay-as-you-go, but its pricing is more directly tied to model inference (per 1M tokens/image/video), fine-tuning (per 1M tokens processed), and raw GPU compute (per hour). Its transparency for serverless inference on open-source models is a strong point, with clear rates per model. However, dedicated GPU and reserved cluster pricing often requires sales engagement, similar to Vertex AI's custom training. The platform excels in offering highly competitive rates for GPU clusters and efficient inference, often making it more cost-effective for specific open-source model workloads. The lack of a broad free credit system like Google's means users need to budget more directly from the start, though its per-token pricing for many models is very low.
Google Cloud Vertex AI Pros & Cons
Pros
- Access to 200+ models including Gemini, Claude, and open models like Gemma in one platform
- Combines full MLOps lifecycle tooling with modern agent-building capabilities
- Agent2Agent (A2A) protocol support enables interoperability across different agent platforms
- Deep native integration with BigQuery and the broader Google Cloud ecosystem
- $300 in free credits for new customers to explore the platform
- Backed by Google's infrastructure and named a leader in multiple analyst reports
Cons
- Recently rebranded from Vertex AI to Gemini Enterprise Agent Platform, which can confuse teams referencing older documentation or tutorials
- Pricing is spread across many separate tools and services, making total cost estimation more complex than flat-rate competitors
- Custom model training costs require a sales estimate or pricing calculator rather than transparent self-serve rates
- Deep feature set and agent-first restructuring add a learning curve for teams new to the Google Cloud ecosystem
- Some advanced governance and enterprise features are gated behind Google Cloud sales conversations
Together AI Pros & Cons
Pros
- OpenAI-compatible API makes migrating from closed-model providers straightforward
- Transparent per-model, pay-as-you-go pricing across 200+ open-source models
- Vertically integrated GPU cloud offers competitive on-demand and reserved rates
- Backed by deep systems research, including FlashAttention and other efficiency breakthroughs
- Full-stack coverage from inference to fine-tuning to raw GPU compute in one platform
- Proven at scale with customers like Cursor, Zoom, Quora, and ElevenLabs
Cons
- Pricing spans many separate model and product pages, making total cost estimation more complex than flat-rate competitors
- Dedicated GPU and reserved cluster pricing largely requires contacting sales rather than transparent self-serve rates
- Focus on open-source models means access to closed frontier models like GPT or Claude isn't the platform's core strength
- Fine-tuning costs vary significantly by model size and technique, requiring careful comparison before committing
- Provisioned throughput and PTU-based pricing has a learning curve for teams new to capacity-based billing
AI Verdict
In the rapidly evolving landscape of artificial intelligence, Google Cloud Vertex AI (now the Gemini Enterprise Agent Platform) and Together AI represent two distinct yet powerful approaches to AI development and deployment. Vertex AI, a comprehensive offering from Google, positions itself as the unified platform for enterprise-grade MLOps and AI agent development. It provides a full lifecycle solution, from data preparation and custom model training to deployment, monitoring, and, crucially, a new agent-first architecture. Its strengths lie in its deep integration with the broader Google Cloud ecosystem, offering over 200 models (including Gemini, Claude, and Gemma) through its Model Garden, and robust MLOps tooling like Model Registry and Pipelines. Vertex AI is ideal for large enterprises looking to build, deploy, and govern complex AI solutions and intelligent agents at scale, leveraging Google's infrastructure and established governance capabilities. Its new Agent Studio and Agent Development Kit (ADK) are key differentiators for building sophisticated, stateful AI agents.
Conversely, Together AI emerges as the full-stack AI cloud optimized for open-source models. Rather than offering proprietary frontier models, Together AI focuses on making the best open-source models (like Llama, DeepSeek, Qwen) incredibly fast, affordable, and easy to consume. Its core value proposition revolves around high-performance serverless inference, efficient fine-tuning, and access to a vertically integrated GPU cloud with competitive rates for H100, H200, and B200 hardware. Together AI shines for developers and organizations that prioritize flexibility, cost-effectiveness, and the ability to leverage the latest advancements in the open-source AI community. Its OpenAI-compatible API significantly lowers the barrier for migrating existing applications from closed-model providers, making it a strong contender for those seeking an agile, performant, and transparent platform for open-source AI.
The key differentiator between the two is their fundamental philosophy: Vertex AI offers a managed, enterprise-grade, agent-centric platform with both Google's and third-party models, deeply embedded within a cloud ecosystem. Together AI provides a high-performance, developer-focused cloud for open-source AI models, emphasizing speed, cost-efficiency, and hardware access.
Frequently Asked Questions
QWhat is the primary difference in model access between Vertex AI and Together AI?
Vertex AI (Gemini Enterprise Agent Platform) offers access to a broad range of Google's proprietary models (like Gemini) and select third-party models (like Claude, Gemma) within its managed MLOps and agent-building ecosystem. Together AI specializes in providing high-performance, cost-effective access to a vast catalog of popular open-source models (like Llama, DeepSeek, Qwen) through its optimized inference cloud and GPU infrastructure.
QWhich platform is better for building custom AI agents with memory and tools?
Google Cloud Vertex AI's Gemini Enterprise Agent Platform is specifically designed for building custom AI agents. Its Agent Studio, Agent Development Kit (ADK), and Memory Bank provide dedicated features for designing, testing, deploying, and managing complex, stateful intelligent agents with tool-use capabilities, making it the stronger choice for this specific use case.
QCan I use my existing OpenAI-compatible code with either platform?
Yes, Together AI offers a direct OpenAI-compatible API, making it straightforward to migrate existing applications and codebases that were built for OpenAI's API. While Vertex AI also offers APIs for its generative models, they typically follow Google Cloud's client library patterns and are not directly drop-in compatible with OpenAI's API without code changes.
QHow do their pricing models compare for a startup with limited budget?
For a startup, Vertex AI offers a generous $300 in free credits, providing a no-cost entry point to explore its extensive features. However, understanding and managing long-term costs across its many services can be complex. Together AI offers transparent, pay-as-you-go per-token pricing for many open-source models, which can be highly cost-effective for specific inference workloads. For raw GPU access, Together AI often presents competitive rates, but without a large initial credit, direct costs begin immediately.