Vertex AI vs Together AI
ComparisonVertex AI and Together AI represent two fundamentally different philosophies for building and deploying AI. Vertex AI is Google Cloud's full-lifecycle ML and generative AI platform — an enterprise-grade environment that spans data preparation, model training, agent orchestration, and monitoring, all tightly integrated with Google Cloud services. Together AI is an AI-native cloud built around open-source models — optimized for fast, affordable inference and training on community-developed models like Llama, DeepSeek, and Qwen. The choice between them often comes down to whether you need a comprehensive enterprise AI platform with first-party model access, or a lean, high-throughput inference and training layer purpose-built for open-source AI.
Feature Comparison
| Dimension | Vertex AI | Together AI |
|---|---|---|
| Primary Focus | Full-lifecycle ML platform with generative AI, agent building, and MLOps | Fast, affordable inference and training for open-source models |
| Model Access | 200+ models including Gemini, Claude, Llama, and Imagen via Model Garden | 200+ open-source models including Llama, DeepSeek, Qwen, Mixtral, and DBRX |
| Proprietary Models | Google Gemini 2.5 family (Pro, Flash, Ultra) as first-party offerings | No proprietary models; fully committed to open-source ecosystem |
| Inference Pricing | Gemini 2.5 Pro: $1.25–$2.50/M input tokens, $10–$15/M output tokens | Starts at $0.10/M tokens for small models; Llama 3 70B at $0.90/M tokens |
| Agent Building | Agent Engine with managed runtime, memory bank, sessions, A2A protocol, and threat detection | No dedicated agent framework; provides inference endpoints agents can call |
| Fine-Tuning | Supervised fine-tuning, RLHF, and distillation for Gemini and select models | Full and lightweight fine-tuning with private data control and cost/ETA estimates |
| GPU Access | Access via Google Cloud Compute Engine (TPUs and GPUs); no self-serve GPU rental | Instant GPU Clusters: H100 from ~$2.39/GPU/hr on-demand, B200 at $4–$5.50/GPU/hr |
| MLOps Tooling | Pipelines, Model Registry, Feature Store, Evaluation, Experiments, and Monitoring | Minimal; focused on inference and training, not full ML lifecycle management |
| Async / Batch | Batch prediction API for offline workloads | Async processing of up to 30B tokens at up to 50% lower cost |
| Enterprise Features | IAM, VPC-SC, CMEK, audit logging, SCC threat detection, compliance certifications | Private deployments, 99.9% SLA on Enterprise plan, custom regions, priority hardware |
| Ecosystem Lock-In | Deep integration with Google Cloud (BigQuery, GCS, Cloud Run); significant switching cost | Cloud-agnostic; OpenAI-compatible API endpoints for easy migration |
| Free Tier | $300 Google Cloud credits for 90 days; limited free prediction and training hours | $5 free API credit to start |
Detailed Analysis
Platform Philosophy: Enterprise Suite vs. Open-Source Accelerator
Vertex AI is designed as a one-stop shop for enterprise AI. It covers the entire ML lifecycle — from data labeling and feature engineering through model training, evaluation, deployment, and production monitoring. This breadth is its core value proposition: teams that are already on Google Cloud can build, deploy, and govern AI without leaving the ecosystem. Together AI takes the opposite approach: it does fewer things but optimizes them intensely. Its infrastructure is purpose-built for serving open-source models at high throughput and low cost, making it a natural fit for teams that want fast inference without a heavyweight platform.
Model Access and the Proprietary vs. Open-Source Divide
Vertex AI's Model Garden provides access to over 200 models spanning Google's proprietary Gemini family, third-party models like Claude, and open-source options like Llama. This breadth means teams can compare and switch models within a single platform. Together AI hosts a comparable number of models but exclusively from the open-source ecosystem — Llama, DeepSeek, Qwen, Mixtral, and others. Together actively contributes to open model development through projects like RedPajama, which gives it an unusually deep understanding of the models it serves. For teams that need access to Gemini or other proprietary models, Vertex AI is the clear path; for those committed to open-source, Together AI offers deeper optimization and lower pricing.
Agent Development and Orchestration
This is where Vertex AI pulls significantly ahead. Its Agent Engine provides a managed runtime for production AI agents with features that Together AI simply doesn't offer: session management, long-term memory banks, the Agent-to-Agent (A2A) protocol for multi-agent coordination, built-in threat detection via Security Command Center, and agent identity management through IAM. Developers can deploy agents using a single CLI command via Google's ADK. Together AI is not an agent platform — it provides the inference endpoints that agents call, but the orchestration, memory, and lifecycle management must come from other tools like LangChain, CrewAI, or custom frameworks.
Inference Performance and Cost Efficiency
Together AI's core competitive advantage is inference economics. By specializing in open-source model serving and investing heavily in inference optimization (including custom kernels and speculative decoding), Together consistently delivers some of the lowest per-token prices in the market — often 3–5x cheaper than comparable Vertex AI endpoints for the same open-source models. Together's async batch processing can handle up to 30 billion tokens at up to 50% reduced cost, making it particularly compelling for high-volume workloads. Vertex AI's inference pricing is competitive for its proprietary Gemini models but tends to carry a premium for open-source model hosting compared to specialized providers.
GPU Infrastructure and Custom Training
Together AI's Instant GPU Clusters let teams provision GPU nodes (H100, H200, B200) in minutes with self-serve tooling, starting from a single 8-GPU node for as little as three days. Pricing starts around $2.39/GPU/hr for H100s on-demand. Vertex AI provides GPU and TPU access through Google Cloud Compute Engine, which offers massive scale but with a more traditional cloud provisioning model. For teams running large-scale custom training jobs on open-source models, Together's GPU cloud offers a more streamlined and often more affordable path. For teams that need TPU access or are training within the Vertex AI ecosystem, Google's infrastructure remains unmatched.
Integration and Ecosystem Considerations
Vertex AI's deep integration with Google Cloud services — BigQuery for data, Cloud Storage for artifacts, Cloud Run for serving, and IAM for security — creates a cohesive development experience but also meaningful switching costs. Together AI deliberately minimizes lock-in: its API endpoints are OpenAI-compatible, meaning applications can switch between Together, OpenAI, and other compatible providers with minimal code changes. This portability is a significant advantage for teams that want to avoid vendor dependency or that operate across multiple cloud environments.
Best For
Building Production AI Agents
Vertex AIVertex AI's Agent Engine provides managed runtime, memory, session management, and A2A protocol — a complete agent infrastructure stack that Together AI doesn't offer.
High-Volume Open-Source Inference
Together AITogether's optimized infrastructure delivers significantly lower per-token costs for open-source models, with async batch processing handling up to 30B tokens at reduced rates.
Enterprise ML with Full Lifecycle Management
Vertex AIPipelines, Feature Store, Model Registry, Experiments, and Monitoring give Vertex AI comprehensive MLOps capabilities that Together AI doesn't attempt to replicate.
Fine-Tuning Open-Source Models
Together AITogether offers streamlined fine-tuning with cost estimates, ETA predictions, and deep optimization for the open-source models it specializes in, at competitive pricing.
Multi-Cloud or Cloud-Agnostic AI
Together AIOpenAI-compatible endpoints and no cloud ecosystem dependency make Together AI the better choice for teams avoiding vendor lock-in or operating across AWS, Azure, and GCP.
Using Google Gemini Models in Production
Vertex AIGemini models are only available through Google's platforms. Vertex AI provides first-party access with the lowest latency, highest rate limits, and enterprise SLAs.
Startup Prototyping on a Budget
Together AILower per-token costs, simpler pricing, and no cloud platform overhead make Together AI more accessible for startups and small teams iterating quickly on open-source models.
Regulated Industry AI Deployment
Vertex AIGoogle Cloud's compliance certifications (SOC 2, HIPAA, FedRAMP), VPC Service Controls, CMEK, and integrated audit logging provide the governance framework regulated industries require.
The Bottom Line
Vertex AI and Together AI are complementary more than they are competitors. Vertex AI is the right choice for enterprises that need a comprehensive AI platform — particularly those building production agents, requiring access to Gemini models, or already invested in the Google Cloud ecosystem. Its Agent Engine, MLOps tooling, and security governance are unmatched by Together AI. Together AI wins on inference economics and open-source model specialization. If your workload centers on serving or fine-tuning open-source models at scale, Together delivers better throughput at lower cost, with the added benefit of cloud-agnostic portability. Many sophisticated AI teams use both: Vertex AI for orchestration, governance, and proprietary model access, and Together AI as a high-performance inference backend for open-source models.