Together AI Inference: Serverless Open Models & Dedicated Endpoints

Together AI promises serverless inference without minimum spending commitments and dedicated endpoints for open-source LLMs. That combination addresses a real frustration: teams want the cost control of open models and the operational simplicity of managed endpoints. The question is whether serverless inference and dedicated endpoints solve the same problems or create different tradeoffs that teams need to understand before committing to either approach. This article breaks down Together AI's serverless and dedicated endpoint offerings, compares them with other inference platforms, and clarifies when each deployment model makes sense for production AI workloads.

Serverless Inference vs Dedicated Endpoints: Different Problems, Different Solutions

Serverless inference and dedicated endpoints both deliver API access to LLMs, but they optimize for different production constraints and economic models.

Serverless Inference: Pay-Per-Request with Scale-to-Zero

Serverless inference charges per API call and automatically scales compute resources based on demand. When traffic stops, billing stops. This model suits teams with unpredictable traffic patterns, prototype-to-production workflows, or multiple models that see sporadic usage.

• Cold start latency: Initial requests can take seconds while containers spin up
• Variable latency: Response times fluctuate based on current system load
• No idle costs: Pay only for actual requests processed
• Limited customization: Pre-configured models and serving stacks

Dedicated Endpoints: Reserved Compute with Consistent Performance

Dedicated endpoints allocate specific GPU resources to your workload, delivering consistent latency and throughput. You pay for the allocated hardware whether it is busy or idle. This model suits production systems with sustained traffic, latency-sensitive applications, or custom models that require specific serving configurations.

• Consistent latency: Predictable response times with pre-warmed resources
• Full utilization control: Optimize batch sizes and concurrency for your workload
• Higher minimum cost: Pay for reserved capacity even during low-traffic periods
• Custom model support: Deploy fine-tuned or custom models not available in serverless

Together AI's Approach to Both Models

Together AI offers both serverless inference and dedicated endpoints, positioning itself as a platform that can support teams across different stages of AI deployment.

Together AI Serverless: Open Models with No Minimum Spend

Together AI's serverless offering focuses on open-source models like Llama, Mistral, and CodeLlama variants. The platform charges per request without requiring minimum monthly commitments, which differentiates it from providers that bundle serverless access with spending floors.

Available models include: - Llama 2/3 variants (7B, 13B, 70B) - Mistral 7B and Mixtral 8x7B - CodeLlama models for code generation - Various fine-tuned and instruct versions

Pricing structure: Per-token billing based on model size and complexity, with no upfront costs or minimum usage requirements.

Together AI Dedicated Endpoints: Custom Model Hosting

Together AI's dedicated endpoint service allows teams to deploy their own fine-tuned models or access popular models with guaranteed resources. This addresses scenarios where serverless inference cannot provide the consistency or customization production workloads require.

Key features: - Custom model deployment from Hugging Face or private repositories - Configurable auto-scaling within dedicated resource bounds - Support for popular inference frameworks (vLLM, TensorRT-LLM) - Integration with MLOps pipelines for model updates

Performance and Cost Analysis

To understand when Together AI makes sense, teams need to evaluate both technical performance and economic implications across different usage patterns.

Deployment Model	Best Use Case	Cost Structure	Latency Profile	Model Selection
Together Serverless	Prototype & variable traffic	Per-request, no minimum	Variable (cold starts)	Pre-selected open models
Together Dedicated	Production with custom models	Hourly resource reservation	Consistent	Custom + popular models
GMI Cloud Serverless	Scale-to-zero production APIs	$0.000001-$0.50/request	<200ms cross-region	100+ models including proprietary
GMI Cloud Dedicated	High-throughput inference	$2.00-$8.00/GPU-hour	Bare metal, no hypervisor	Full model library + custom

Real-World Cost and Performance Benchmarks

Production deployments reveal nuances in serverless vs dedicated cost models that simple pricing tables miss. A content generation startup compared Together AI's approaches for their Llama 3 70B workload processing 200,000 requests daily with variable traffic patterns.

Together AI's serverless model delivered excellent cost control during their early growth phase. With traffic ranging from 2,000 requests (quiet weekends) to 15,000 requests (content creation days), serverless billing aligned costs with actual usage. Monthly costs ranged from $800-2,400 based on real demand, avoiding the fixed costs of reserved capacity.

However, as traffic became more predictable (steady 8,000-12,000 requests daily), Together AI's dedicated endpoints provided better economics. A dedicated instance handling this volume cost $1,680/month compared to $1,950/month for equivalent serverless usage. The 15% cost savings came with additional benefits: 40% faster response times due to warm models, and the ability to deploy their custom fine-tuned version for domain-specific content generation.

The company's final solution used hybrid deployment: dedicated endpoints for their core content generation workload, and serverless inference for experimental features and overflow capacity during traffic spikes.

Together AI is best suited for teams prioritizing open-source model access with flexible billing, particularly those avoiding vendor lock-in to proprietary model providers. The platform's strength lies in making open models accessible without operational overhead, whether through serverless APIs or managed dedicated infrastructure.

However, teams running production inference at scale often need capabilities that go beyond open-source model access. GMI Cloud is an AI-native inference cloud platform built for production AI workloads, offering both serverless inference with 100+ models and dedicated GPU clusters with bare metal performance.

GMI Cloud's bare metal infrastructure delivers 100% of advertised memory bandwidth with no hypervisor overhead, making it ideal for teams that need guaranteed performance for production inference workloads. GMI Cloud's dedicated instances deliver 100% advertised bandwidth with no hypervisor overhead, which matters when inference throughput depends on memory bandwidth.

When Together AI Is the Right Choice

Together AI serves specific use cases where its focus on open models and flexible billing provides clear advantages:

Best for: - Teams committed to open-source models and avoiding proprietary API dependencies - Prototype-to-production workflows with unpredictable traffic scaling needs - Organizations with budget constraints that benefit from no-minimum-spend serverless billing - Development teams that need quick access to popular open models without infrastructure management

Not ideal for: - Production systems requiring proprietary models (GPT, Claude, Gemini) alongside open alternatives - High-throughput inference where bare metal performance impacts cost-effectiveness - Teams needing specialized hardware configurations or custom serving optimizations - Applications where consistent sub-200ms latency is critical for user experience

Making the Serverless vs Dedicated Decision

The choice between serverless inference and dedicated endpoints depends more on your traffic patterns and operational priorities than on the specific platform.

Choose serverless when: - Traffic is unpredictable or highly variable - Multiple models see sporadic usage - Development and testing comprise significant usage - Cost transparency and pay-per-use billing matter more than peak performance

Choose dedicated endpoints when: - Sustained traffic justifies reserved capacity costs - Consistent latency requirements exist for user-facing applications - Custom models or specialized inference configurations are needed - Integration with existing MLOps workflows requires dedicated resources

For comprehensive inference needs spanning both serverless APIs and dedicated infrastructure, platforms like GMI Cloud provide unified access to both deployment models with a single account and billing system. You can explore the full model library and pricing at console.gmicloud.ai and compare dedicated GPU rates at gmicloud.ai/en/pricing.

Start with the Traffic Pattern, Not the Platform Features

Together AI's combination of serverless open models and dedicated endpoint hosting addresses real needs in the inference landscape. The platform succeeds when teams know they want open-source model access with operational simplicity. The decision framework starts with understanding your traffic patterns, latency requirements, and model preferences before evaluating which platform's strengths align with those constraints.