Top 10 LLM Inference Providers of 2026

LLM inference is the process of running a trained language model to generate responses. For businesses, the inference provider affects response speed, model access, operating costs, scalability, and how much infrastructure the engineering team must manage.

Some platforms provide ready-to-use APIs for open-weight models, while others offer custom model deployment, serverless GPUs, dedicated infrastructure, or access to multiple providers through one API. The right choice depends on your workload, model requirements, budget, performance expectations, and deployment preferences.

What Is LLM Inference?

LLM inference happens whenever a trained large language model processes an input and generates an output. For example, when a user asks a chatbot a question, the model uses inference to interpret the prompt and produce a response.

Training is the process of teaching a model by exposing it to large datasets. Inference is what happens after training, when the model is used inside an application. Every chatbot response, coding suggestion, document summary, search answer, or AI agent action requires inference. For a broader look at how businesses build and manage AI agents, see our enterprise AI agent platforms comparison.

Inference providers supply the infrastructure needed to run these models. Depending on the platform, this may include hosted APIs, optimized inference engines, GPU servers, autoscaling, model deployment tools, monitoring, and security controls.

Providers use different pricing methods. Some charge per input and output token, while others charge by request, GPU second, GPU hour, or reserved capacity. The pricing model can significantly affect the total cost of an application, especially when traffic increases.

Quick Comparison of the Best LLM Inference Providers

The 10 Best LLM Inference Providers of 2026

The platforms below serve different infrastructure needs. Some focus on hosted model APIs, while others provide the tools required to deploy and manage custom inference workloads.

1. Baseten

Baseten is a managed inference platform for deploying and scaling machine learning models in production. It supports pre-optimized model APIs as well as custom model deployments, giving teams more control than a basic third-party API.

Its features include autoscaling, model deployment, GPU configuration, and support for production workloads. Baseten is suitable for companies that need to serve proprietary or fine-tuned models without building the entire serving infrastructure themselves.

Best for: Teams that need managed custom model deployment, autoscaling, and production control.

Pricing: Baseten offers model-specific token pricing for hosted APIs. Example rates include approximately $0.10 per million input tokens and $0.50 per million output tokens for GPT OSS 120B. Dedicated deployments are charged according to compute usage, while enterprise plans use custom pricing.

Limitation: Custom deployments may require engineering knowledge of model serving, GPU selection, and infrastructure configuration.

2. Fireworks AI

Fireworks AI provides managed inference for open and open-weight models through serverless APIs and dedicated deployments. Its catalog includes language, vision, audio, and other model types.

The platform supports streaming, structured outputs, function calling, and model customization. Dedicated deployments can provide more predictable capacity and performance for larger workloads.

Best for: Teams that want managed open-model inference with customization and production features.

Pricing: Serverless inference is billed per token, while dedicated deployments are charged by GPU usage. Prices vary by model and service tier. Some current model listings start around $0.30 per million input tokens and $1.20 per million output tokens.

Limitation: Pricing, availability, and limits vary across models and deployment tiers, so buyers must compare the exact model they plan to use.

3. Together AI

Together AI combines inference, fine-tuning, and model development tools in one platform. It provides access to a broad catalog of open-weight models through serverless APIs and dedicated deployments.

The platform also supports embeddings, batch processing, fine-tuning, and OpenAI-compatible API workflows. This makes it useful for teams that want to test multiple models and later customize them for production.

Best for: AI teams that need open-model access, fine-tuning, and model development capabilities.

Pricing: Serverless inference is priced per million input and output tokens. Dedicated inference and provisioned throughput use separate pricing models based on reserved capacity, model choice, and required throughput. Fine-tuning and training are charged separately.

Limitation: Its broad feature set can make pricing and deployment decisions more complex than using a basic hosted API.

4. Groq

Groq provides inference through specialized hardware designed for fast model execution. Its GroqCloud API gives developers access to supported models without requiring them to manage the underlying infrastructure.

Groq is particularly useful for real-time assistants, voice interfaces, coding tools, and AI agents where response speed directly affects the user experience. It also provides an OpenAI-compatible API.

Best for: Latency-sensitive applications that use models supported by Groq.

Pricing: Groq uses per-token pricing with separate input and output rates depending on the model. The provider publishes current model prices and rate limits in its documentation.

Limitation: Groq’s usefulness depends on whether the required model and capabilities are available on its platform.

5. Cerebras

Cerebras offers inference through its wafer-scale hardware architecture. Its service is designed for applications that need high generation throughput and fast responses from supported models.

The platform provides API access to open-weight models and supports OpenAI-compatible integration. It may be useful for coding assistants, agentic applications, and high-volume text-generation workloads.

Best for: Applications where high output throughput is a major requirement.

Pricing: Cerebras offers free trial credits, self-serve pay-per-token access, and custom enterprise plans. Published model examples include rates such as $0.10 per million input tokens and $0.10 per million output tokens for Llama 3.1 8B. Rates vary by model and should be checked before purchase.

Limitation: Cerebras is not a general-purpose GPU platform, and its suitability depends on its supported models, limits, and commercial availability.

6. Modal

Modal is a serverless compute platform that lets developers run custom workloads on cloud GPUs. It provides more control over containers, dependencies, hardware, and deployment logic than a standard model API.

Teams can package an inference application, select GPU resources, and configure scaling based on demand. Modal is useful for custom inference servers, batch processing, research workloads, and applications with variable traffic.

Best for: Engineering teams deploying custom inference workloads with flexible GPUs and serverless scaling.

Pricing: Modal charges for compute usage. Example GPU rates include approximately $0.001097 per second for an H100 SXM5, $0.000694 per second for an A100 80 GB, and $0.000222 per second for an L4. Its Starter plan is listed at $0 per month and includes free compute credits.

Limitation: Modal requires more engineering involvement because users manage much of the model-serving and deployment architecture.

7. Replicate

Replicate provides an API for running a wide range of machine learning models across language, image, audio, video, and other categories. Developers can access models without setting up their own GPUs, dependencies, or serving environments.

Replicate also supports custom deployments for teams that need more control. Its simple API and broad model catalog make it useful for prototyping and product development.

Best for: Developers who want simple API access to many models without managing infrastructure.

Pricing: Pricing is model-specific. Some models charge by runtime and hardware, while others use token, image, video, or output-based pricing. For example, DeepSeek R1 has been listed at $0.01 per 1,000 output tokens, while image and video models use separate output-based rates.

Limitation: Cold starts, hardware requirements, availability, and performance can differ significantly between models.

8. RunPod

RunPod is a GPU cloud platform offering dedicated GPU instances and serverless inference. Its Pods product supports teams that want to manage their own environments, while RunPod Serverless provides containerized endpoints that can scale with demand.

RunPod supports different GPU types and custom inference frameworks such as vLLM. It offers more infrastructure control than a typical token-based API, making it suitable for teams running open models or custom containers.

Best for: Teams that need flexible GPU infrastructure and control over model deployment.

Pricing: RunPod offers hourly and per-second pricing. Example rates include approximately $4.59 per hour for an H200 Pod, $2.72 per hour for a serverless A100, $1.75 per hour for an L40S, and $1.10 per hour for a serverless RTX 4090. Final costs depend on GPU type, region, storage, and worker configuration.

Limitation: Users may need to manage containers, model serving, storage, monitoring, and operational maintenance.

9. OpenRouter

OpenRouter is a model gateway that provides access to models from multiple AI providers through one API. It allows developers to compare models, route requests, use fallbacks, and avoid integrating separately with every upstream provider.

The platform is useful for teams that want flexibility in model selection or want to reduce dependence on one inference vendor. However, the underlying model execution is still handled by upstream providers.

Best for: Teams that need multi-provider model access, routing, and a unified API.

Pricing: OpenRouter offers free access to selected models and paid plans with a platform fee. Its pay-as-you-go plan currently lists a 5.5% platform fee, while model usage is charged according to the selected provider and model. Business and enterprise plans have different terms.

Limitation: Latency, availability, privacy policies, and rate limits can vary between upstream providers and models.

10. AWS Bedrock

Amazon Bedrock is a managed AWS service that provides access to foundation models from different providers. It integrates model access with AWS identity, security, networking, monitoring, and governance tools.

Bedrock supports model inference, agents, knowledge bases, guardrails, and other generative AI capabilities. It is especially useful for enterprises that already use AWS and want to keep AI workloads within their existing cloud environment.

Best for: Enterprises that need managed model access with AWS integration, governance, and security controls.

Pricing: Bedrock pricing varies by model and inference mode. Customers can use on-demand token pricing, batch inference where available, or provisioned throughput for reserved capacity. Each model has separate input and output rates, so buyers should use the AWS pricing calculator for an accurate estimate.

Limitation: Pricing, model availability, regional support, permissions, and related AWS services can make Bedrock more complex to evaluate.

How to Choose an LLM Inference Provider

Choosing an LLM inference provider requires more than comparing the lowest token price. Start by confirming that the platform supports the exact model, context length, input type, and capabilities your application needs. Some providers support text-only models, while others offer vision, audio, structured outputs, function calling, or agent features.

Next, evaluate performance under realistic conditions. Compare time to first token, total response latency, tokens generated per second, concurrency limits, rate limits, and cold-start behavior. A provider with a low price may become expensive if it cannot handle your traffic or delivers inconsistent response times.

Pricing should also be assessed according to your workload. Token-based pricing is easier to estimate for hosted APIs, while GPU-based pricing may be more economical for high-volume or continuously running workloads. Include input tokens, output tokens, idle GPU time, storage, networking, platform fees, and reserved-capacity costs in the calculation.

Consider how much infrastructure control your team needs. A managed API is usually easier to integrate and maintain, while platforms such as Modal and RunPod provide more control over GPUs, containers, model versions, and serving frameworks. That control can be valuable, but it also creates additional engineering and operational responsibilities.

Finally, review reliability and governance requirements. Check uptime commitments, autoscaling, monitoring, data retention, privacy policies, compliance certifications, regional availability, access controls, and support options. Before making a long-term decision, run the same model and workload across two or three shortlisted providers and compare performance, reliability, and total cost.

FAQs

What is an LLM inference provider?

An LLM inference provider supplies the API, hardware, infrastructure, or deployment platform required to run a trained language model and generate responses.

What is the difference between an LLM API and an inference platform?

An LLM API provides a ready-to-use endpoint for model requests. An inference platform may also include model deployment, GPU selection, autoscaling, fine-tuning, containers, and monitoring.

Which LLM inference provider is the cheapest?

There is no single cheapest provider for every workload. Costs depend on the model, input-to-output ratio, traffic volume, deployment type, hardware, and platform fees.

Which provider offers the fastest LLM inference?

Groq and Cerebras are designed for high-speed inference on supported models. Actual performance depends on the model, concurrency, region, and workload, so teams should benchmark the exact endpoint they plan to use.

Should I use a managed API or deploy my own model?

A managed API is usually better for fast integration and lower operational overhead. Custom deployment may be more suitable when you need proprietary weights, specific GPUs, data residency, or greater control over performance and cost.

Final Verdict

There is no universal winner among LLM inference providers because they solve different infrastructure problems. Baseten is a strong option for managed custom model deployment, while Fireworks AI and Together AI are suitable for open-model access and customization. Groq and Cerebras are worth evaluating for speed-sensitive workloads. Modal and RunPod provide more control over custom GPU infrastructure, while Replicate offers a simple way to access many models. OpenRouter is useful for multi-provider routing, and AWS Bedrock is a practical choice for enterprises that need model access within their AWS environment. Before choosing, test the same model across shortlisted providers and compare total operating costs under realistic traffic conditions.