LLM Inference Providers Compared 2026: Groq vs Cerebras vs Together vs Fireworks
Once you choose an open-weight model, you still have to run it somewhere — and the inference provider you pick shapes speed, cost, and reliability as much as the model does. The four leaders specialize: Groq, built on its custom LPU hardware, leads on raw tokens-per-second for ultra-low-latency use; Cerebras, on its wafer-scale chips, also competes hard on extreme speed; Together AI offers the broadest open-model catalog with strong price-performance and fine-tuning; and Fireworks targets production reliability and efficient serving with function calling and structured output. For most teams the decision is a triangle of speed, cost, and model availability, with reliability and developer experience as the tiebreakers.
Key Insight
Once you choose an open-weight model, you still have to run it somewhere — and the inference provider you pick shapes speed, cost, and reliability as much as the model does. The four leaders specialize: Groq, built on its custom LPU hardware, leads on raw tokens-per-second for ultra-low-latency use; Cerebras, on its wafer-scale chips, also competes hard on extreme speed; Together AI offers the broadest open-model catalog with strong price-performance and fine-tuning; and Fireworks targets production reliability and efficient serving with function calling and structured output. For most teams the decision is a triangle of speed, cost, and model availability, with reliability and developer experience as the tiebreakers.
TL;DR
Choosing an open-weight model is only half the decision. You still have to run it somewhere, and the inference provider you choose affects latency, cost, and reliability as much as the model choice itself. This guide compares the four leaders — Groq, Cerebras, Together AI, and Fireworks — on speed, price, model selection, and production-readiness.
Quick orientation: Groq and Cerebras compete on raw speed via custom hardware, Together leads on model breadth and price-performance, and Fireworks targets production reliability.
What an Inference Provider Does
An inference provider hosts open-weight models on its own hardware and serves them through an API. You send a prompt, you get tokens back, you pay per token — and you never touch a GPU. This is the practical middle path between two extremes: a closed frontier API (simplest, but you are locked to that vendor's models) and self-hosting (full control, but you operate the infrastructure).
For the models these providers run, see our open-source LLMs comparison; for when self-hosting beats using a provider, that guide covers the volume threshold.
How We Compared
This is an editorial comparison assembled from each provider's documentation, pricing pages, published throughput figures, and community benchmarks such as the public inference-speed leaderboards — not a single controlled test run. We weighed five dimensions:
- Speed — tokens per second and latency
- Price — cost per million tokens for comparable models
- Model selection — breadth of available open models
- Production features — function calling, structured output, reliability
- Fine-tuning — ability to customize models
Where a figure cannot be sourced to published material, the rating is qualitative, and speed claims depend heavily on the specific model and conditions. Verify current pricing and throughput with each provider.
The Comparison
| Provider | Leads on | Hardware | Model breadth | Best for |
|---|---|---|---|---|
| ---------- | ---------- | ---------- | --------------- | ---------- |
| Groq | Speed | Custom LPU | Curated | Ultra-low latency |
| Cerebras | Speed | Wafer-scale | Curated | Extreme throughput |
| Together AI | Breadth + value | GPU | Broadest | Flexible default |
| Fireworks | Production serving | GPU | Wide | Reliable apps |
1. Groq — Best for Speed
Best for: Real-time applications where latency is the priority
Groq built custom inference hardware — its Language Processing Unit (LPU) — specifically to serve models fast, and speed is its defining advantage. For applications where streaming feel, low latency, or high tokens-per-second decides the user experience — real-time assistants, voice, interactive agents — Groq is consistently among the fastest options available.
- Custom LPU: Hardware purpose-built for inference speed
- Very high throughput: Among the leaders on tokens-per-second
- Low latency: Strong for real-time and streaming use
- Simple API: OpenAI-compatible interface
Limitations: A more curated model selection than the GPU-based providers, since models are optimized for the LPU. Best when speed is the priority rather than maximum model choice.
2. Cerebras — Best for Extreme Throughput
Best for: Workloads that need the highest possible generation speed
Cerebras takes a different hardware path to the same goal: its wafer-scale engine — an unusually large single chip — targets extreme inference speed. Like Groq, it competes at the very high end of tokens-per-second, and it is a strong alternative when raw throughput is what matters most.
- Wafer-scale hardware: Distinctive architecture built for speed
- High throughput: Competes at the top end of generation speed
- Strong for large models: Architecture suits big-model inference
- OpenAI-compatible API: Straightforward to adopt
Limitations: Like Groq, a curated rather than exhaustive model catalog. The specialized hardware focus means model availability follows what the platform optimizes.
3. Together AI — Best for Model Breadth and Value
Best for: Teams that want wide model choice, fine-tuning, and good price-performance
Together AI is the flexible default. It offers one of the broadest catalogs of open models across families and sizes, supports fine-tuning, and competes strongly on price-performance. For teams that want to experiment across many models or customize one to their data without committing to specialized hardware, Together covers the most ground.
- Broadest catalog: Many model families and sizes
- Fine-tuning: Customize models on your own data
- Strong price-performance: Competitive per-token economics
- Production-ready: Scales for real workloads
Limitations: Runs on GPUs, so it does not match the custom-silicon providers on the absolute speed ceiling. Breadth means more choices to evaluate.
4. Fireworks AI — Best for Production Serving
Best for: Teams shipping reliable applications with structured outputs
Fireworks focuses on production-grade serving: reliability, efficient inference, and the features real applications need — function calling, structured output, and dependable performance under load. For teams that have moved past experimentation and need an inference layer they can ship on, Fireworks is built for that stage.
- Production focus: Reliability and efficient serving
- Function calling + structured output: Features production apps need
- Wide model selection: Broad catalog tuned for serving
- Good performance: Strong GPU-based throughput
Limitations: Like Together, GPU-based rather than custom-silicon, so not aimed at the absolute speed records. Differentiates on production features more than raw tokens-per-second.
Which Should You Choose?
For real-time, latency-critical apps
Recommended: Groq (or Cerebras)
When streaming speed defines the experience, the custom-silicon providers lead. Groq and Cerebras are the two to evaluate head-to-head on your specific model.
For model breadth and fine-tuning
Recommended: Together AI
The widest catalog plus fine-tuning makes it the most flexible starting point for teams exploring open models.
For shipping reliable production apps
Recommended: Fireworks
When function calling, structured output, and dependable serving matter more than record throughput, Fireworks is built for that.
For the absolute lowest cost
Recommended: compare per-model pricing across all four
Cost varies enough by model and provider that the right answer depends on exactly which model you run — check current per-model rates rather than assuming one provider is always cheapest.
Provider vs Self-Hosting
Using a provider is the middle path. The decision against self-hosting comes down to sustained volume: below the point where dedicated hardware beats per-token pricing, a provider is simpler and cheaper, and it gives you open-model economics and choice without operating infrastructure. Above that volume — or when data must never leave your environment — self-hosting wins. Our open-source LLMs guide covers that threshold, and our vector database showdown covers the retrieval layer many inference workloads pair with.
Conclusion
The inference provider you choose is a real architectural decision, not an afterthought. For 2026: Groq and Cerebras lead on speed via custom hardware, Together AI leads on model breadth and value, and Fireworks leads on production serving. The choice is a triangle of speed, price, and model availability, with reliability and developer experience as tiebreakers — and for most teams a provider beats self-hosting until sustained volume grows large.
For the surrounding stack, see our guides on open-source LLMs, AI reasoning models, and AI agent frameworks.
This comparison is an editorial synthesis of vendor documentation, published throughput figures, and community benchmarks; see our [methodology](/methodology). Speed and pricing vary by model — verify current details with each provider.
Key Takeaways
- Inference providers run open-weight models for you via an API, so you get open-model economics without operating GPUs yourself
- Groq's custom LPU hardware targets the lowest latency and highest tokens-per-second, making it the choice for real-time and speed-critical applications
- Cerebras uses wafer-scale chips to compete at the extreme-speed end, another option when raw throughput is the priority
- Together AI offers the broadest catalog of open models plus fine-tuning and strong price-performance, making it a flexible default for many teams
- Fireworks focuses on production-grade serving — reliability, function calling, structured output — for teams shipping real applications
- The core decision is a triangle of speed, price-per-token, and model availability; reliability and developer experience break the tie
- Using a provider is the middle path between a closed frontier API and self-hosting — open-model cost and choice without running infrastructure
Frequently Asked Questions
What is an LLM inference provider?
An LLM inference provider runs open-weight models on its own hardware and exposes them through an API, so you can use models like Llama, Qwen, or DeepSeek without buying GPUs or operating infrastructure. You pay per token, get open-model economics and model choice, and skip the complexity of self-hosting — a middle path between closed frontier APIs and running models yourself.
Which inference provider is the fastest?
Groq and Cerebras lead on raw speed, both using custom hardware purpose-built for inference rather than general-purpose GPUs. Groq's LPU and Cerebras's wafer-scale chips target very high tokens-per-second and low latency. For applications where streaming speed or real-time response is the priority, these two are the usual front-runners.
Should I use an inference provider or self-host?
Use a provider when you want open-model economics and choice without operating GPUs — which is most teams below very high sustained volume. Self-host when sustained usage is large enough that owning or renting dedicated hardware beats per-token pricing, or when data must never leave your infrastructure. Our open-source LLM guide covers the self-hosting threshold in more detail.
Which provider has the most models?
Together AI is known for one of the broadest open-model catalogs, spanning many model families and sizes plus fine-tuning support. Fireworks also offers a wide selection tuned for production. Groq and Cerebras tend to focus on a curated set optimized for their hardware. If model variety and fine-tuning matter most, Together is often the starting point.
How much does LLM inference cost in 2026?
Pricing is per million input and output tokens and varies by model size and provider, generally far below frontier closed-model rates for comparable open models. Faster specialized hardware sometimes carries a premium for the speed. Always check each provider's current per-model pricing, since rates change often and vary significantly by the specific model you run.
About the Author
Fatima Al-Hassan
Security & Privacy Editorial Desk
Security & Privacy Editorial Desk · Web3AIBlog
Fatima Al-Hassan is a pen name for our security and privacy editorial desk. Posts under this byline are written and reviewed by contributors with backgrounds in application security, smart contract auditing, threat modeling, and privacy-preserving cryptography. The desk specializes in attacker-perspective explainers — how exploits actually work, what real recoveries look like, and which defenses survive contact with sophisticated adversaries. We coordinate disclosures responsibly and publish nothing that helps active attackers.
