Top 15 LLM Platforms to Optimize AI Inference and Performance

Organizations embracing large language models (LLMs) quickly discover the challenges in deploying and scaling them for real-world applications. These hurdles can seem daunting, especially if you must maximize performance and minimize costs to meet production goals. Fortunately, LLM platforms provide a solution. This article will explore how to make LLM platforms work for you by offering valuable insights on deploying and scaling LLMs with high-speed, cost-efficient inference that maximizes performance and reliability. Additionally, understanding AI Inference vs Training is crucial to optimizing model performance and ensuring smooth deployment.

One of the best ways to harness the power of LLM platforms is with AI inference APIs. Inference APIs provide a straightforward way to start using LLMs in your applications without building and maintaining the underlying infrastructure yourself.

What is LLM Inference, and How Does It Work?

LLM inference is a stage of a large language model in which we use the trained model to apply patterns and rules learned from past data to new, unseen information to make predictions or respond to questions by generating text.

This stаge is of раrаmount imрortаnсe for reаlizing the usefulness of LLMs in рrасtiсаl situаtions beсаuse it trаnsforms аll сomрlex unԁerstаnԁings аnԁ relationships сарtureԁ ԁuring training into асtionаble results or outрuts, Inferenсe with LLMs meаns ԁeаling with lаrge аmounts of ԁаtа through ԁeeр neurаl networks.

Optimizing LLM Inference for Real-Time Applications

This tаsk neeԁs signifiсаnt сomрutаtionаl рower, раrtiсulаrly for moԁels suсh аs GPT (Generаtive Pretrаineԁ Trаnsformer) or BERT (Biԁireсtionаl Enсoԁer Reрresentаtions from Trаnsformers). The рromрtness аnԁ quickness of LLM inference аre imрortаnt for аррliсаtions thаt require resрonses in reаl time: these inсluԁe:

• Interactive сhаtbots
• Automаteԁ trаnslаtion serviсes
• Aԁvаnсeԁ analytics systems

Therefore, LLM inference is not simply about applying a model; it pertains to incorporating these sophisticated AI powers into the very structure of digital services and goods. This improves their working ability and users’ experience.

Benefits of LLM Inference Optimization

Oрtimizing LLM inferenсe саn hаve fаr-reасhing benefits beyonԁ just sрeeԁ аnԁ сost. By enhаnсing the efficiency of these moԁels, businesses аnԁ ԁeveloрers саn асhieve:

• Imрroveԁ User Exрerienсe: Oрtimizeԁ LLMs with imрroveԁ resрonse times аnԁ ассurаte outрuts саn greаtly imрrove user sаtisfасtion. It is esрeсiаlly benefiсiаl in reаl-time аррliсаtions suсh аs сhаtbots, reсommenԁаtion systems, аnԁ virtuаl аssistаnts.
• Resourсe Mаnаgement: Effiсient LLM inferenсe oрtimizаtion leаԁs to better resourсe utilizаtion, аllowing for the аlloсаtion of сomрutаtionаl рower to other сritiсаl tаsks, thereby imрroving overаll system рerformаnсe аnԁ reliаbility.
• Enhаnсeԁ Aссurасy: Oрtimizаtion is аbout аԁjusting the moԁel for better results, ԁeсreаsing mistаkes, аnԁ enhаnсing рreԁiсtion рreсision. This mаkes the outрut more ԁeрenԁаble аnԁ benefiсiаl in ԁeсision-mаking situаtions.
• Sustаinаbility: Less сomрutаtionаl ԁemаnԁs сoulԁ meаn less usаge of energy, which is in line with sustаinаbility goаls аnԁ саn ԁeсreаse the саrbon footрrint from AI oрerаtions.
• Flexibility in Deрloyment: You саn use inferenсe moԁels of LLM thаt аre oрtimizeԁ for different рlаtforms. These inсluԁe eԁge ԁeviсes, mobile рhones, аnԁ сlouԁ environments. This flexibility offers more options for using LLM аррliсаtions in vаrious situations аnԁ mаkes them more versаtile.

How does LLM inference work?

LLM inference operates in two key phases: prefill and decode.

1. Prefill Phase: The user’s input is tokenized into smaller units (words or subwords) and processed by the model. During this stage, the model builds intermediate states (keys and values) that help generate the first token of the response. Since the entire input is known upfront, computations can be parallelized, leading to efficient GPU utilization.
2. Decode Phase: The model generates vector embeddings based on the input and, using prior context, predicts the next token iteratively until a stop condition is met. Unlike the prefill phase, this process is sequential—each token depends on the previous one—making it slower and less efficient for GPUs. Instead of compute performance, decoding speed is limited by memory access, classifying it as a memory-bound operation.

This distinction between parallel prefill and sequential decoding highlights why LLM inference can be both powerful and computationally demanding.

Striking the Right Balance: Speed vs. Accuracy in LLM Inference

The inference process is about producing reliable outputs, and this is where the speed vs. accuracy trade-off comes into play. Higher-quality responses require the model to consider many possibilities, which can get slower and more computationally intensive.

Simplifying the computation facilitates faster responses, but this can ultimately compromise the quality of the output. Finding the best balance between speed and accuracy is a key consideration when designing LLM models.

Unreliable Outputs in LLM Inference

It should also be noted that the process of inference is probabilistic. This means that its results are not always reliable. The model can sometimes give strange outcomes and surprising results since it produces responses based on the input and what it has already learned.

Optimizing Inference: KV Caching

Caching key-value (KV) is a commonly used methodology to optimize the computational efficiency of LLM inference. In the decoding phase, tokens are generated step by step, with each new word depending on previously captured information.

The model remembers this information as key and value tensors. By caching these tensors, the model can eliminate the need to recalculate them every time a new word is generated. This dramatically improves the efficiency and speed of the LLM inference process.

Optimizing Inference: Batching

Batching allows multiple user requests to be grouped into one batch instead of being sent individually. This minimizes the need to load model parameters frequently, reducing inference time. Individual users may have to wait longer for their requests to be processed until the desired set of requests for the batch request has been met.

This drawback can be circumvented through inflight-batching. After one user request has finished processing, that spot in the batch request is filled with new requests without waiting for the entire batch to complete. Thus, it ensures the whole process is:

• Smooth
• Continuous
• GPU-optimized

Optimizing Inference: Model Parallelization

Distributing LLMs over large clusters of GPUs can efficiently handle many user inputs. When the model is partitioned and its memory and compute requirements are split across several instances, LLM inference performance improves significantly through model parallelization.

Optimizing Inference: Model Optimization

Optimization techniques enhance models’ effectiveness by reducing the memory needed to create ideal outcomes. Model distillation and quantization are two methods used to achieve model optimization. In distillation, a larger LLM model is employed to teach a smaller model, which essentially mimics the properties of the bigger one but with less memory or computational power.

Quantization shrinks the model by regulating its activation systems and weightings. Although still yielding comparable results, quantization substantially downsizes a model. These techniques generally result in better models that facilitate increased inference performance.

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15 Essential LLM Platforms for Inferencing and Scaling AI

1. Inference: The OpenAI-Compatible Inference API

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

2. Groq: A Leader in Speed and Performance

Groq is an AI infrastructure company that claims to have developed the world’s fastest AI inference technology. Its flagship product, the Language Processing Unit (LPU) Inference Engine, is a hardware and software platform designed for high-speed, energy-efficient AI processing.

Groq’s LPU-powered cloud service, GroqCloud, allows users to run popular open-source LLMs, such as Meta AI’s Llama 3 70B, up to 18x faster than other providers. Developers value Groq for its performance and seamless integration. The platform supports API access via Groq’s Python client SDK or OpenAI’s client SDK and integrates easily with tools like LangChain and LlamaIndex for building advanced LLM applications and chatbots.

Pricing

Groq’s cloud service charges are based on the number of tokens processed, ranging from $0.06 to $0.27 per million, depending on the model. A free tier is available, making it easy for users to get started.

3. Perplexity Labs: An API for Rapid Access to Open Source LLMs

Perplexity is rapidly emerging as an alternative to Google and Bing. Perplexity Labs offers both an AI-powered search engine and an inference engine.

pplx-api: Perplexity’s Inference Engine

In October 2023, Perplexity Labs launched pplx-api, an API for fast and efficient access to open-source LLMs. Currently in public beta, the API is available to Perplexity Pro subscribers, allowing a broad user base to test and provide feedback for ongoing improvements.

The API supports popular LLMs, including:

• Mistral 7B
• Llama 13B
• Code 34B
• Llama 70B

It also features llama-3-sonar-small-32k-online and llama-3-sonar-large-32k-online, based on the FreshLLM paper. These Llama3-based models can return citations, a feature currently in closed beta.

Developer Integration & Cost Efficiency

Perplexity’s API is designed to be cost-effective for deployment and inference, offering significant savings. It is also client-compatible with OpenAI, allowing seamless integration for developers familiar with OpenAI’s ecosystem.

Pricing & Subscription Plans

Perplexity offers flexible pricing with a pay-as-you-go model:

• $0.20 to $1.00 per million tokens, depending on the model size
• Online models incur a flat $5 fee per 1,000 requests

For those needing higher usage limits, the Pro plan costs $20/month or $200/year and includes:

• $5 monthly API credit
• Unlimited file uploads
• Dedicated support

This structured pricing makes Perplexity’s API accessible without upfront commitments, catering to casual users and developers deploying LLM-powered applications.

4. Fireworks AI: A Platform for Open Source AI Applications

Fireworks AI is a generative AI platform that enables developers to use cutting-edge open-source models in their applications.

The platform offers a broad range of language models, including:

• FireLLaVA-13B (a vision-language model)
• FireFunction V1 (for function calling)
• Mixtral MoE 8x7B and 8x22B (instruction-following models)
• Llama 3 70B (from Meta)

In addition to these language models, Fireworks AI supports image-generation models such as Stable Diffusion 3 and XL. All models are accessible via Fireworks AI’s serverless API, which is designed to provide industry-leading performance and throughput.

Pricing and Deployment

Fireworks AI features a pay-as-you-go pricing model, where charges are based on the number of tokens processed:

• Gemma 7B model: $0.20 per million tokens
• Mixtral 8x7B model: $0.50 per million tokens

Users can rent GPU instances (A100 or H100) hourly for on-demand deployments.

Developer-Friendly Integration

The API is OpenAI-compatible, making it easy for developers to integrate it with tools like LangChain and LlamaIndex.

Target Audience & Pricing Tiers

Fireworks AI caters to developers, businesses, and enterprises, offering different pricing tiers:

• Developer Tier: 600 requests/min rate limit and up to 100 deployed models
• Business & Enterprise Tiers: Custom rate limits, team collaboration features, and dedicated support

This structure ensures flexibility and scalability, making Fireworks AI suitable for many use cases.

5. Cloudflare: A Global Network for AI Inference

Cloudflare AI Workers is an inference platform that allows developers to run machine learning models on Cloudflare’s global network with minimal code. It offers a serverless, scalable, and GPU-accelerated solution, enabling developers to use pre-trained models for tasks like text generation, image recognition, and speech recognition—without managing infrastructure or GPUs.

Supported Models

Cloudflare AI Workers provides a curated selection of open-source models across various AI domains, including:

• LLMs: llama-3-8b-instruct, mistral-8x7b-32k-instruct, gemma-7b-instruct
• Vision Models: vit-base-patch16-224, segformer-b5-finetuned-ade-512-pt

Pricing & Free Tier

Cloudflare AI Workers use a pay-as-you-go pricing model, where costs are based on neurons processed (acting as token-like units across different models):

• Free tier: 10,000 neurons per day
• Beyond free usage: $0.011 per 1,000 neurons
• Model-Specific Pricing:
  • Llama 3 70B: $0.59 per million input tokens, $0.79 per million output tokens
  • Gemma 7B: $0.07 per million tokens (both input and output)

With its flexible pricing and diverse model offerings, Cloudflare AI Workers provides an affordable, high-performance solution for AI inference at scale.

6. Nvidia NIM: Access to LLMs Optimized By Nvidia

Nvidia NIM API provides access to a diverse range of pre-trained large language models (LLMs) and AI models, optimized and accelerated by Nvidia’s software stack.

Model Catalog & Capabilities

Through the Nvidia API Catalog, developers can explore and use over 40 models from Nvidia, Meta, Microsoft, Hugging Face, and others, including:

• Text-Generation Models: Llama 3 70B (Meta), Mixtral 8x22B (Microsoft), Nemotron 3 8B (Nvidia)
• Vision Models: Stable Diffusion, Kosmos 2

The NIM API allows developers to integrate these models with minimal code. Hosted on Nvidia’s infrastructure, the API follows a standardized OpenAI-compatible format, ensuring seamless integration into existing workflows.

Deployment Options

• Hosted API: Developers can prototype and test applications for free.
• Production-Ready Deployment: Models can be deployed on-premises or in the cloud using Nvidia NIM containers.

Pricing & Free Tier

Nvidia offers both free and paid tiers:

• Free tier: 1,000 credits for initial testing
• Paid pricing (based on tokens processed and model size):
  • Gemma 7B: $0.07 per million tokens
  • Llama 3 70B: $0.79 per million output tokens

With scalable deployment options and flexible pricing, the Nvidia NIM API provides a powerful solution for integrating state-of-the-art AI models into applications.

7. Together AI: Affordable Inference for Open-Source Models

Together AI provides high-performance inference for 200+ open-source LLMs, offering sub-100ms latency, automated optimization, and horizontal scaling—all at a lower cost than proprietary solutions.

Key Features & Advantages

Together AI’s infrastructure handles:

• Token caching, model quantization, and load balancing allow developers to focus on prompt engineering and application logic rather than infrastructure management.
• Seamless model switching: Developers can run parallel inference jobs and swap between models like Llama 3, RedPajama, and Falcon with just a few lines of Python—without managing separate deployments or CUDA configurations.

Why Companies Choose Together AI

• Up to 11x more affordable than GPT-4 (when using Llama 3)
• 4x faster throughput than Amazon Bedrock
• 2x faster than Azure AI

Pricing & Access

Together AI offers a free tier and flexible pay-per-token or GPU usage pricing for its serverless options.

Combining cost efficiency, speed, and scalability, Together AI is a powerful choice for developers leveraging open-source LLMs.

8. OpenRouter: An Inference Marketplace

OpenRouter is an inference marketplace that provides access to over 300 models from top providers through a unified OpenAI-compatible API. This API enables seamless integration with models from:

• OpenAI
• Anthropic
• Google
• Bedrock, etc

Why do companies use OpenRouter? Companies choose OpenRouter because it provides simple access to multiple AI models through a single API interface. The platform offers automatic failovers and competitive pricing while eliminating the need to integrate and manage various provider APIs separately.

OpenRouter Pricing: Pay-as-you-go model with specific pricing listed for each model.

9. Hyperbolic: Affordable and Accessible AI Compute

Hyperbolic is an AI inference platform that provides affordable GPUs and accessible computing for AI researchers, developers, and startups, enabling them to build and scale AI projects efficiently.

Why Companies Choose Hyperbolic

• Cost Savings: It offers AI model inference for Base, Text, Image, and Audio generation at up to 80% lower cost than traditional providers without compromising quality.
• Affordable GPU Access: Provides the most competitive GPU pricing compared to major cloud providers like AWS.
• Decentralized GPU Network: Partners with data centers and individuals with idle GPUs, ensuring cost-effective and scalable computing.

Pricing & Plans

• Free Base Plan: Designed for startups and SMEs, offering high throughput and advanced features.
• Premium Plans: Tailored for academic research and enterprise use, providing enhanced capabilities.

With low-cost AI inference, flexible GPU access, and a decentralized approach, Hyperbolic is an ideal solution for those looking to scale AI affordably.

10. Replicate: Best for Prototyping and Experimentation

Best For:

Rapid prototyping and experimenting with open-source or custom models.

Replicate is a cloud-based platform that simplifies machine learning model deployment and scaling. It packages and deploys models efficiently using Cog, an open-source tool. The platform supports a variety of models, including:

• LLMs: Llama 2
• Image Generation: Stable Diffusion
• Other Applications: Text generation, image processing, music generation, and more

Why Companies Use Replicate

• Ideal for quick experiments and MVP development
• Thousands of pre-built open-source models for diverse AI applications
• Simple setup, start running models with just one line of code

Pricing

Replicate follows a pay-per-inference pricing model, ensuring cost-effective scaling based on usage.

With its ease of use, flexibility, and vast model library, Replicate is a powerful choice for AI experimentation and rapid development.

11. SambaNova Cloud: Optimized for High-Throughput Applications

SambaNova Cloud delivers exceptional AI performance using custom-built Reconfigurable Dataflow Units (RDUs), achieving 200 tokens per second on the Llama 3.1 405B model—10x faster than traditional GPU-based solutions.

Key Features

• High Throughput: Processes complex models efficiently, eliminating bottlenecks for large-scale applications.
• Energy Efficiency: It consumes less power than conventional GPU infrastructures.
• Scalability: Scales AI workloads seamlessly without performance trade-offs or excessive costs.

Why Choose SambaNova Cloud?

SambaNova is built for high-throughput, low-latency AI inference and training. Its custom hardware, including the SN40L chip and dataflow architecture, enables it to handle substantial parameter models without GPUs’ latency and throughput limitations.

SambaNova Cloud offers a scalable, efficient, high-performance alternative to traditional AI infrastructure for businesses and researchers pushing AI boundaries.

12. DeepInfra: Hosting Large AI Models

Best for: Cloud-based hosting of large-scale AI models.

DeepInfra offers a robust platform for running large AI models on cloud infrastructure. It's easy to use for managing large datasets and models. Its cloud-centric approach is best for enterprises needing to host large models. Why do companies use DeepInfra? DeepInfra's inference API takes care of servers, GPUs, scaling, and monitoring, and accessing the API takes just a few lines of code.

It supports most OpenAI APIs to help enterprises migrate and benefit from the cost savings. You can also run a dedicated instance of your public or private LLM on DeepInfra infrastructure. DeepInfra Pricing Usage-based, billed by token or at execution time.

13. Anyscale: End-to-End AI Development and Deployment

Best for: End-to-end AI development, deployment, and high-scalability applications.

Anyscale is a cloud-agnostic platform designed to scale compute-intensive AI workloads, from model training and inference to batch processing. It is the company behind Ray, the open-source AI compute engine used by industry leaders like Uber, Spotify, and Airbnb to power their AI platforms.

Why Companies Choose Anyscale

• Enterprise-Grade Security & Governance: Provides admin, billing, and compliance controls for secure AI deployment.
• Cloud & Hardware Agnostic: Works with any cloud, accelerator, or software stack.
• Expert Support: Direct access to Ray, AI, and ML specialists for optimization and troubleshooting.

Pricing

Anyscale follows a usage-based pricing model, with enterprise plans available for large-scale AI operations.

With scalability, flexibility, and enterprise-ready features, Anyscale is a powerful solution for organizations building AI at scale.

14. HuggingFace: The Go-To for NLP Projects

Best for: Beginners and professionals looking to start and scale Natural Language Processing (NLP) projects.

Hugging Face is a leading open-source AI community that enables developers to build, train, and share machine learning models and datasets. It is best known for its Transformers library, which simplifies working with state-of-the-art NLP models.

Why Companies Use Hugging Face

• Extensive Model Hub: Access 100,000+ pre-trained models, including BERT, GPT, and Stable Diffusion.
• Seamless Integration: Works with multiple programming languages and cloud platforms, including AWS and Google Cloud.
• Scalable APIs: Easily extend AI capabilities through Hugging Face’s inference endpoints and model-serving solutions.

Pricing

• Free for basic use.
• Enterprise plans are available for advanced features and scalability.

With its vast model library, collaborative tools, and flexible deployment options, Hugging Face is a top choice for developers and businesses working with NLP and AI applications.

15. Lamini: An Enterprise-Grade LLM Platform

Lamini is a full-stack LLM platform designed to streamline model selection, fine-tuning, and deployment for enterprise development teams. It simplifies adapting open-source LLMs with proprietary company data, ensuring high accuracy, efficient inference, and flexible deployment options.

Key Features

• Enhanced Model Accuracy: Supports memory tuning and compute optimizations, enabling users to fine-tune any open-source model with organization-specific data.
• Flexible Deployment: You can deploy models in private clouds (VPC), data centers, or through Lamini’s managed infrastructure.
• Optimized Inference: Efficiently runs LLM inference on GPUs, supporting user-owned hardware to ensure high-throughput, low-latency performance.
• End-to-End Model Lifecycle Support:
  • Compare and experiment with models in the Lamini Playground.
  • Tune models with proprietary data.
  • Securely deploy models across various environments.
  • Leverage REST APIs, a Python client, and a Web UI for seamless integration.

With powerful fine-tuning tools, deployment flexibility, and scalable inference capabilities, Lamini is an excellent solution for enterprises looking to customize and operationalize LLMs with their data.

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Start Building with $10 in Free API Credits Today!

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

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