Imagine this: you have built an AI app with an incredible idea, but it struggles to deliver because running large language models (LLMs) feels like trying to host a concert with a cassette player. The potential is there, but the performance? Lacking.
This is where inference APIs for open LLMs come in. These services are like supercharged backstage passes for developers, letting you integrate cutting-edge AI models into your apps without worrying about server headaches, hardware setups, or performance bottlenecks. But which API should you use? The choice can feel overwhelming, with each promising lightning speed, jaw-dropping scalability, and budget-friendly pricing.
In this article, we cut through the noise. We’ll explore five of the best inference APIs for open LLMs, dissect their strengths, and show how they can transform your app’s AI game. Whether you are after speed, privacy, cost-efficiency, or raw power, there is a solution here for every use case. Let’s dive into the details and find the right one for you.
1. Groq
groq
Groq is renowned for its high-performance AI inference technology. Their standout product, the Language Processing Units (LPU) Inference Engine, combines specialized hardware and optimized software to deliver exceptional compute speed, quality, and energy efficiency. This makes Groq a favorite among developers who prioritize performance.
Some New Model Offerings:
- Llama 3.1 8B Instruct: A smaller but remarkably capable model that balances performance and speed, ideal for applications that need moderate capability without incurring high compute costs.
- Llama 3.1 70B Instruct: A state-of-the-art model that rivals proprietary solutions in reasoning, multilingual translation, and tool usage. Running this on Groq’s LPU-driven infrastructure means you can achieve real-time interactivity even at large scale.
Key Features
- Speed and Performance: GroqCloud, powered by a network of LPUs, claims up to 18x faster speeds compared to other providers when running popular open-source LLMs like Meta AI’s Llama 3 70B.
- Ease of Integration: Groq offers both Python and OpenAI client SDKs, making it straightforward to integrate with frameworks like LangChain and LlamaIndex for building advanced LLM applications and chatbots.
- Flexible Pricing: Pricing is based on tokens processed, ranging from $0.06 to $0.27 per million tokens. A free tier is available, allowing developers to start experimenting without initial costs.
To explore Groq’s offerings, visit their official website and check out their GitHub repository for the Python client SDK.
2. Perplexity Labs
perplexity-ai
Perplexity Labs, once known primarily for its AI-driven search functionalities, has evolved into a full-fledged inference platform that actively integrates some of the most advanced open-source LLMs. The company has recently broadened its horizons by supporting not only established model families like Llama 2 but also the latest wave of next-generation models. This includes cutting-edge variants of Llama 3.1 and entirely new entrants such as Liquid LFM 40B from LiquidAI, as well as specialized versions of Llama integrated with the Perplexity “Sonar” system.
Some New Model Offerings:
- Llama 3.1 Instruct Models: Offering improved reasoning, multilingual capabilities, and extended context lengths up to 128K tokens, allowing the handling of longer documents and more complex instructions.
- Llama-3.1-sonar-large-128K-online: A tailored variant combining Llama 3.1 with real-time web search (Sonar). This hybrid approach delivers not only generative text capabilities but also up-to-date references and citations, bridging the gap between a closed-box model and a true retrieval-augmented system.
Key Features
- Wide Model Support: The pplx-api supports models such as Mistral 7B, Llama 13B, Code Llama 34B, and Llama 70B.
- Cost-Effective: Designed to be economical for both deployment and inference, Perplexity Labs reports significant cost savings.
- Developer-Friendly: Compatible with the OpenAI client interface, making it easy for developers familiar with OpenAI’s ecosystem to integrate seamlessly.
- Advanced Features: Models like llama-3-sonar-small-32k-online and llama-3-sonar-large-32k-online can return citations, enhancing the reliability of responses.
Pricing
Perplexity Labs offers a pay-as-you-go pricing model that charges based on API requests and the number of tokens processed. For instance, llama-3.1-sonar-small-128k-online costs $5 per 1000 requests and $0.20 per million tokens. The pricing scales up with larger models, such as llama-3.1-sonar-large-128k-online at $1 per million tokens and llama-3.1-sonar-huge-128k-online at $5 per million tokens, all with a flat $5 fee per 1000 requests.
In addition to pay-as-you-go, Perplexity Labs offers a Pro plan at $20 per month or $200 per year. This plan includes $5 worth of API usage credits monthly, along with perks like unlimited file uploads and dedicated support, making it ideal for consistent, heavier usage.
For detailed information, visit Perplexity Labs.
3. SambaNova Cloud
SambaNova Cloud
SambaNova Cloud delivers impressive performance with its custom-built Reconfigurable Dataflow Units (RDUs), achieving 200 tokens per second on the Llama 3.1 405B model. This performance surpasses traditional GPU-based solutions by 10x, addressing critical AI infrastructure challenges.
Key Features
- High Throughput: Capable of processing complex models without bottlenecks, ensuring smooth performance for large-scale applications.
- Energy Efficiency: Reduced energy consumption compared to conventional GPU infrastructures.
- Scalability: Easily scale AI workloads without sacrificing performance or incurring significant costs.
Why Choose SambaNova Cloud?
SambaNova Cloud is ideal for deploying models that require high-throughput and low-latency processing, making it suitable for demanding inference and training tasks. Their secret lies in its custom hardware. The SN40L chip and the company’s dataflow architecture allow it to handle extremely large parameter counts without the latency and throughput penalties common on GPUs
See more about SambaNova Cloud’s offerings on their official website.
4. Cerebrium
Cerebrium
Cerebrium simplifies the deployment of serverless LLMs, offering a scalable and cost-effective solution for developers. With support for various hardware options, Cerebrium ensures that your models run efficiently based on your specific workload requirements.
A key recent example is their guide on using the TensorRT-LLM framework to serve the Llama 3 8B model, highlighting Cerebrium’s flexibility and willingness to integrate the latest optimization techniques.
Key Features
- Batching: Enhances GPU utilization and reduces costs through continuous and dynamic request batching, improving throughput without increasing latency.
- Real-Time Streaming: Enables streaming of LLM outputs, minimizing perceived latency and enhancing user experience.
- Hardware Flexibility: Offers a range of options from CPUs to NVIDIA’s latest GPUs like the H100, ensuring optimal performance for different tasks.
- Quick Deployment: Deploy models in as little as five minutes using pre-configured starter templates, making it easy to go from development to production.
Use Cases
Cerebrium supports various applications, including:
- Translation: Translating documents, audio, and video across multiple languages.
- Content Generation & Summarization: Creating and condensing content into clear, concise summaries.
- Retrieval-Augmented Generation: Combining language understanding with precise data retrieval for accurate and relevant outputs.
To deploy your LLM with Cerebrium, visit their use cases page and explore their starter templates.
5. PrivateGPT and GPT4All
https://github.com/nomic-ai/gpt4all
For those prioritizing data privacy, deploying private LLMs is an attractive option. GPT4All stands out as a popular open-source LLM that allows you to create private chatbots without relying on third-party services.
While they do not always incorporate the very latest massive models (like Llama 3.1 405B) as quickly as high-performance cloud platforms, these local-deployment frameworks have steadily expanded their supported model lineups.
At the core, both PrivateGPT and GPT4All focus on enabling models to run locally—on-premise servers or even personal computers. This ensures that all inputs, outputs, and intermediate computations remain in your control.
Initially, GPT4All gained popularity by supporting a range of smaller, more efficient open-source models like LLaMA-based derivatives. Over time, it expanded to include MPT and Falcon variants, as well as new entrants like Mistral 7B. PrivateGPT, while more a template and technique than a standalone platform, shows how to integrate local models with retrieval-augmented generation using embeddings and vector databases—all running locally. This flexibility lets you choose the best model for your domain and fine-tune it without relying on external inference providers.
Historically, running large models locally could be challenging: driver installations, GPU dependencies, quantization steps, and more could trip up newcomers. GPT4All simplifies much of this by providing installers and guides for CPU-only deployments, lowering the barrier for developers who do not have GPU clusters at their disposal. PrivateGPT’s open-source repositories offer example integrations, making it simpler to understand how to combine local models with indexing solutions like Chroma or FAISS for context retrieval. While there is still a learning curve, the documentation and community support have improved significantly in 2024, making local deployment increasingly accessible.
Key Features
- Local Deployment: Run GPT4All on local machines without requiring GPUs, making it accessible for a wide range of developers.
- Commercial Use: Fully licensed for commercial use, allowing integration into products without licensing concerns.
- Instruction Tuning: Fine-tuned with Q&A-style prompts to enhance conversational abilities, providing more accurate and helpful responses compared to base models like GPT-J.
Example Integration with LangChain and Cerebrium
Deploying GPT4All to the cloud with Cerebrium and integrating it with LangChain allows for scalable and efficient interactions. By separating the model deployment from the application, you can optimize resources and scale independently based on demand.
To set up GPT4All with Cerebrium and LangChain, follow detailed tutorials available on Cerebrium’s use cases and explore repositories like PrivateGPT for local deployments.
Conclusion
Choosing the right Inference API for your open LLM can significantly impact the performance, scalability, and cost-effectiveness of your AI applications. Whether you prioritize speed with Groq, cost-efficiency with Perplexity Labs, high throughput with SambaNova Cloud, or privacy with GPT4All and Cerebrium, there are robust options available to meet your specific needs.
By leveraging these APIs, developers can focus on building innovative AI-driven features without getting bogged down by the complexities of infrastructure management. Explore these options, experiment with their offerings, and select the one that best aligns with your project requirements.
