Together AI Unlocks Predictable, Cost-Effective AI Inference for Open Models

The landscape of AI development is evolving at an unprecedented pace, with large language models (LLMs) becoming integral to everything from sophisticated workflow automation to advanced coding assistants. As businesses increasingly integrate these powerful models into their production environments, a critical challenge has emerged: managing the cost and ensuring the reliability of AI inference at scale. Unpredictable costs and fluctuating performance can quickly derail even the most innovative AI initiatives.

Recognizing this growing need, Together AI has introduced Provisioned Throughput, a groundbreaking service designed to offer reserved inference capacity for open-weight frontier models. This strategic move aims to provide businesses with the predictable performance and transparent pricing that has long been a holy grail in the world of AI infrastructure, fundamentally changing how companies approach their AI inference budgets.

The AI Inference Dilemma: Serverless vs. Dedicated

Before Provisioned Throughput, organizations typically faced a difficult choice when deploying AI models:

• Best-Effort Serverless Inference: This option offers unparalleled convenience and scalability for development and variable workloads. You pay for what you use, and the infrastructure scales automatically. However, it often comes with a trade-off: performance can be inconsistent, with no guaranteed capacity or strict service level agreements (SLAs). For critical production applications, this unpredictability can be a significant hurdle. Imagine an LLM powering a customer service chatbot – latency spikes or service interruptions can directly impact user experience and business operations.

• Dedicated, Managed Infrastructure: At the other end of the spectrum, dedicated infrastructure provides maximum control, consistent performance, and the ability to fine-tune every aspect of the deployment. However, it demands significant investment in terms of setup, management, and operational overhead. This approach is often complex, costly, and requires specialized expertise, making it less accessible for many organizations, especially those without large MLOps teams.

This "inference dilemma" forced businesses to compromise, either sacrificing reliability for convenience or incurring substantial overhead for control. As AI inference costs became a substantial line item for companies, the need for a middle ground – a solution that combines the best of both worlds – became urgent.

Introducing Provisioned Throughput: The Middle Ground

Together AI's Provisioned Throughput service steps into this gap, offering a compelling alternative that merges the simplicity of serverless with the reliability of dedicated infrastructure. It's designed specifically for production workloads that demand consistent performance and predictable costs without the complexity of managing dedicated hardware.

Here's what makes Provisioned Throughput a game-changer:

• Reserved Capacity: Unlike best-effort serverless, Provisioned Throughput guarantees a specific, reserved inference capacity for your chosen open-weight models. This means your applications will always have the necessary resources, eliminating performance variability and ensuring consistent response times, even during peak loads.
• Token-Based Pricing: Emulating the straightforward pricing models of proprietary AI providers, Together AI offers token-based pricing. This simplifies cost forecasting and management, allowing businesses to budget accurately based on their anticipated token usage, rather than grappling with complex GPU hour calculations or unpredictable serverless bills.
• 99% Uptime SLA: For any production workload, reliability is paramount. Provisioned Throughput comes with a robust 99% uptime Service Level Agreement, providing assurance that your AI applications will remain operational and available when your business needs them most. This level of guarantee is crucial for mission-critical systems where downtime is not an option.

This innovative approach positions Provisioned Throughput as a more reliable option for production workloads than traditional serverless offerings, while significantly reducing the operational burden and costs associated with dedicated infrastructure.

How Provisioned Throughput Works: PTUs Explained

The economics of Provisioned Throughput are structured around Provisioned Throughput Units (PTUs). A PTU represents a fixed slice of inference capacity, guaranteeing a specific rate of tokens per minute for a given model. This granular approach allows businesses to right-size their capacity based on their exact needs.

Each PTU is priced at a transparent rate of $0.05 per PTU per minute. What's particularly clever about this system is how it accounts for different token types:

• Input Tokens: The tokens you send to the model for processing.
• Cached Input Tokens: Tokens that might be reused or efficiently handled by the model's internal caching mechanisms.
• Output Tokens: The tokens generated by the model as its response.

Each of these token types consumes capacity at a distinct rate within a PTU. This allows for optimized utilization based on your specific traffic patterns without affecting the underlying SLA.

For example, on the MiniMax M3 model, one PTU can handle:

• 138,840 input tokens per minute
• 694,200 cached input tokens per minute
• 23,140 output tokens per minute

This detailed breakdown empowers developers and operations teams to precisely configure their capacity, ensuring efficient resource allocation and predictable performance for their specific model interactions. This level of transparency and control over capacity consumption is a significant advantage for optimizing LLM inference costs.

Unlocking Significant Cost Savings and Enhanced Reliability

One of the most compelling aspects of Provisioned Throughput is its potential for substantial cost reduction. Together AI reports that costs can be significantly lower than proprietary alternatives, potentially slashing expenses by up to 90% compared to models like Claude Opus 4.8.

This dramatic cost efficiency stems from several factors:

• Leveraging Open-Weight Models: Provisioned Throughput focuses on open-weight models, which typically have lower licensing or usage fees compared to their closed-source proprietary counterparts.
• Optimized Infrastructure: Together AI's specialized infrastructure is highly optimized for running these open models efficiently, translating into lower operational costs that can be passed on to users.
• Predictable Billing: The token-based pricing model eliminates the variable and often opaque costs associated with burstable serverless functions or underutilized dedicated hardware, leading to more predictable and manageable budgets.

Beyond cost, the 99% uptime SLA provides a level of reliability previously reserved for costly dedicated setups. This makes Provisioned Throughput an ideal choice for production-grade applications where consistent availability is non-negotiable.

Target Audience and Use Cases

Initially, Provisioned Throughput is available for powerful open-weight models such as MiniMax M3 and GLM-5.2, with availability across North America and EMEA.

This service is particularly well-suited for:

• Production Workloads with Guarantees: Any application where consistent performance, low latency, and high availability are critical, such as real-time analytics, automated content generation, or customer support AI.
• Businesses Migrating from Proprietary Models: Companies looking to reduce their reliance on expensive proprietary APIs can now transition to open-weight models with confidence, knowing they can achieve comparable reliability and predictability at a fraction of the cost.
• Scaling AI Initiatives: As businesses scale their AI adoption, Provisioned Throughput offers a clear migration path for leveraging frontier-quality open models without incurring prohibitive expenses or operational complexities.

While Provisioned Throughput is designed for guaranteed production needs, Together AI still offers:

• Serverless Inference: Ideal for rapid development, experimentation, and workloads with highly variable or infrequent usage where best-effort performance is acceptable.
• Dedicated Inference Solutions: For highly customized requirements or scenarios demanding absolute maximum control over the underlying hardware and software stack.

This tiered offering ensures that Together AI can cater to the full spectrum of AI development and deployment needs.

Why This Matters for the Open Model Ecosystem

The introduction of Provisioned Throughput is a significant development for the broader open-weight model ecosystem. The company notes a substantial shift in traffic, with token volume growing from 30 billion to over 400 trillion tokens per month, much of which has migrated from closed APIs. This demonstrates a clear market demand for open models.

By providing a reliable, cost-effective, and predictable inference solution for these models, Together AI is accelerating their adoption in production environments. This helps to democratize access to advanced AI capabilities, allowing more developers and businesses to innovate without being constrained by the high costs or vendor lock-in often associated with proprietary solutions.

The company highlighted that for MiniMax M3 inference, teams can now run these models at scale with commitments that support business operations. This aligns with their efforts in "Together AI Masters MiniMax M3 Inference," ensuring robust performance for demanding applications. This innovation directly addresses the growing need for cost-effective and reliable AI infrastructure, tackling LLM inference costs through a new approach to capacity management.

Conclusion: A New Era for Open AI Inference

Together AI's Provisioned Throughput marks a pivotal moment in the journey towards democratizing advanced AI. By offering a compelling middle ground between the unpredictability of serverless and the complexity of dedicated infrastructure, it empowers businesses to leverage the power of open-weight models with unprecedented reliability and cost predictability.

For developers and organizations grappling with the escalating costs and performance demands of AI inference, Provisioned Throughput provides a clear, actionable solution. It not only promises significant cost savings but also delivers the peace of mind that comes with guaranteed capacity and a robust SLA, enabling a new era of stable, scalable, and affordable AI innovation.