Together AI vs Nebius
ComparisonTogether AI and Nebius represent two distinct strategies for capturing the booming demand for AI compute infrastructure. Together AI has built an AI-native cloud optimized for serving open-source models at speed—offering serverless inference, fine-tuning, and self-service GPU clusters that can spin up in minutes. Nebius, spun out of Yandex's international operations, is scaling a full-stack GPU cloud with a European footprint and massive enterprise deals that position it as a hyperscaler challenger.
The competitive landscape shifted dramatically in early 2026. Meta signed a landmark $27 billion infrastructure deal with Nebius, while NVIDIA invested $2 billion in the company and partnered on next-generation AI cloud development. Together AI, meanwhile, has been deepening its open-source research contributions—launching Mamba-3, FlashAttention-4, and new agent-focused tooling—while expanding its own data center footprint across Maryland, Memphis, and Sweden. Both companies now offer NVIDIA Blackwell-generation GPUs, but they serve meaningfully different customer profiles.
This comparison breaks down where each platform excels, from inference pricing and model support to infrastructure scale and geographic reach, helping you determine which provider fits your AI infrastructure needs.
Feature Comparison
| Dimension | Together AI | Nebius |
|---|---|---|
| Primary Focus | Open-source model inference and fine-tuning platform | Full-stack GPU cloud and AI infrastructure at hyperscale |
| GPU Hardware | NVIDIA GB200 NVL72, HGX B200, H200, H100 | NVIDIA GB300 NVL72 (Blackwell Ultra), B200, H100; RTX PRO 6000 |
| Inference Offering | Serverless inference with 110+ tok/sec on reasoning clusters; dedicated endpoints | Managed inference integrated into cloud platform; less model breadth |
| Model Library | Hundreds of open-source models (Llama, Qwen, DeepSeek, Mistral, gpt-oss) | Bring-your-own-model focus; no large hosted model catalog |
| GPU Cluster Access | Self-service Instant Clusters: 8 to 4,000+ GPUs, provisioned in minutes | Bare-metal and cloud GPU clusters; Capacity Blocks with real-time availability API |
| Pricing Transparency | Public per-token inference pricing; GPU clusters from $1.76/hr (H100) to $5.50/hr (B200) | Custom/enterprise pricing; no public rate card for most services |
| Data Center Locations | Maryland, Memphis (US); Sweden (EU) | Mäntsälä, Finland; Paris, France; Kansas City & Vineland, NJ (US); Iceland |
| Data Sovereignty | EU presence via Sweden data center | Strong EU-first positioning with Finnish HQ and multiple European facilities |
| Enterprise Partnerships | $305M Series B; research-driven community | $27B Meta deal; $2B NVIDIA investment; multi-billion Microsoft agreement |
| Data Labeling / Human-in-the-Loop | Not offered | Toloka division provides data labeling and red-teaming at scale |
| Open-Source Contributions | Extensive: RedPajama, Mamba-3, FlashAttention-4, together.compile | Minimal public open-source research output |
| Developer Experience | OpenAI-compatible APIs; Python SDK v2.0; code sandboxes | Capacity Dashboard; public Capacity API; Kubernetes-native workflows |
Detailed Analysis
Inference Speed and Model Ecosystem
Together AI's core advantage is its inference stack. The platform hosts hundreds of open-source models behind OpenAI-compatible API endpoints, meaning developers can switch from proprietary APIs with minimal code changes. Decoding speeds reach 110 tokens per second on dedicated reasoning clusters, and the company continuously optimizes its serving infrastructure with research like FlashAttention-4 and Mamba-3 architectures. For teams building AI agents or applications that rely on fast, cheap inference across multiple model families, Together AI offers unmatched breadth and ease of use.
Nebius takes a different approach. Its inference offering is integrated into the broader cloud platform rather than exposed as a standalone model marketplace. Customers typically bring their own models and deploy them on Nebius infrastructure. This works well for organizations with proprietary models and dedicated ML engineering teams, but presents a higher barrier for developers who simply want fast API access to popular open-source models.
Infrastructure Scale and Enterprise Positioning
Nebius has rapidly emerged as a hyperscaler contender. The $27 billion Meta deal announced in March 2026—covering $12 billion in dedicated capacity and up to $15 billion in additional compute over five years—vaulted Nebius into the top tier of AI infrastructure providers. Combined with NVIDIA's $2 billion strategic investment and a multi-billion Microsoft agreement, Nebius is building toward 2.5 GW of data center capacity by end of 2026, with a CapEx budget of $16–20 billion.
Together AI operates at a fundamentally different scale. Its $305 million Series B funds a focused expansion of its own data centers and inference infrastructure. Together AI isn't competing for hyperscale build-out contracts—it's competing on developer experience and price-performance for open-source AI workloads. This makes the two companies more complementary than directly competitive in many scenarios.
Geographic Reach and Data Sovereignty
For organizations subject to European data residency requirements, Nebius holds a clear structural advantage. Headquartered in the Netherlands with its primary data center in Finland, Nebius was purpose-built around European operations. It became the first cloud provider to operate NVIDIA GB300 NVL72 (Blackwell Ultra) systems in Europe, deployed at its Mäntsälä facility. Additional European presence in Paris and Iceland strengthens its data sovereignty positioning.
Together AI entered the European market in September 2025 with a Sweden-based data center, but its geographic footprint remains US-centric with facilities in Maryland and Memphis. For EU-regulated workloads, Nebius currently offers more geographic options and a longer track record of European operations.
Developer Tools and Workflow Integration
Together AI excels in developer ergonomics. Its OpenAI-compatible API means existing codebases can migrate with a URL change. The Python SDK v2.0 provides strongly-typed interfaces, and built-in code sandboxes let LLMs execute generated code safely. The platform also launched ThunderAgent and together.compile at its AI Native Conf, signaling deeper investment in agentic infrastructure tooling.
Nebius focuses on infrastructure-level developer experience: a public Capacity API provides real-time GPU availability data, Capacity Blocks allow predictable resource planning, and the platform supports Kubernetes-native deployment workflows. These tools serve ML platform teams managing large-scale training runs rather than individual developers making API calls.
Data Labeling and the Full AI Stack
A unique differentiator for Nebius is its Toloka division, which provides human-in-the-loop data labeling and, increasingly, red-teaming services for foundation models. This means Nebius can serve customers across the full AI development lifecycle—from data preparation through training to inference—without requiring third-party labeling vendors. Together AI offers no equivalent service, focusing exclusively on the compute and model-serving layers.
Open-Source Research and Community
Together AI is one of the most active corporate contributors to open-source AI research. The RedPajama dataset project provided critical training data for the open-source community. More recently, Mamba-3 introduced a state-space model architecture that outperforms Mamba-2 and rivals Transformers on key benchmarks, while FlashAttention-4 pushes inference efficiency further. These contributions build goodwill and attract developers to the platform organically.
Nebius's open-source footprint is minimal by comparison. The company's value proposition is infrastructure and operational excellence rather than research leadership. For organizations that prioritize working with a provider deeply embedded in the open-source AI ecosystem, Together AI is the clear choice.
Best For
Rapid Prototyping with Open-Source Models
Together AITogether AI's serverless inference across hundreds of models with OpenAI-compatible APIs lets developers prototype and iterate without infrastructure overhead.
Large-Scale Model Training (1,000+ GPUs)
NebiusNebius's hyperscale infrastructure, Blackwell Ultra clusters, and enterprise capacity planning tools are built for massive distributed training workloads.
EU-Regulated AI Workloads
NebiusWith its European HQ, Finnish data center running GB300 NVL72, and multiple EU facilities, Nebius offers superior data sovereignty guarantees.
Cost-Effective Inference at Scale
Together AITransparent per-token pricing, optimized serving infrastructure, and dedicated endpoints make Together AI the price-performance leader for inference workloads.
Full AI Lifecycle (Data → Training → Deployment)
NebiusNebius uniquely combines GPU cloud, managed inference, and Toloka data labeling/red-teaming in a single vendor relationship.
Building AI Agents and Applications
Together AICode sandboxes, agent tooling (ThunderAgent), and fast multi-model inference make Together AI purpose-built for agentic application development.
Enterprise GPU Cloud with Custom Contracts
NebiusNebius's enterprise sales motion, proven by the Meta and Microsoft deals, suits organizations negotiating multi-year, multi-billion-dollar compute commitments.
Startup or SMB AI Development
Together AISelf-service provisioning, public pricing, and no-commitment serverless tiers make Together AI far more accessible for smaller teams.
The Bottom Line
Together AI and Nebius are not direct substitutes—they occupy different positions in the AI infrastructure stack. Together AI is the better choice for developers and teams that want fast, affordable access to open-source models through clean APIs. Its serverless inference, transparent pricing, and deep open-source research contributions make it the natural home for application builders working with Llama, Qwen, DeepSeek, and similar model families. If your primary need is inference and fine-tuning rather than raw GPU clusters, Together AI delivers more value per dollar with less operational complexity.
Nebius is the stronger choice for organizations operating at enterprise or hyperscale, particularly those with European data residency requirements or needs that span the full AI lifecycle from data labeling to large-scale training. The Meta and NVIDIA partnerships validate Nebius's infrastructure at a scale that Together AI isn't targeting. If you're negotiating multi-year compute contracts, need Blackwell Ultra hardware in Europe, or want integrated data labeling services, Nebius is the more capable platform.
For many organizations, the practical answer may involve both: Nebius for heavy training workloads and EU-compliant infrastructure, and Together AI for fast, cost-effective inference and rapid model experimentation. The AI compute market is large enough that these two providers can thrive without directly competing for the same workloads.