The Inference Layer: Groq, Together, Fireworks, and Cerebras

Inference speed and cost are their own competitive frontier. Meet the providers sitting between the model labs and your app - and why choosing the right one matters.

TL;DR: OpenAI, Anthropic, and Google build the frontier models. But a whole separate industry has emerged to run open-weight models faster and cheaper than the labs themselves can. Groq, Together AI, Fireworks AI, and Cerebras compete on speed, price, and reliability - not research. Understanding this layer lets you cut costs, slash latency, and pick the right tool for each job.

Why the Inference Layer Exists

When a model lab like Meta releases Llama, the weights are public. Anyone can run it. The question is how well you run it - and that is where inference providers come in.

Running a large language model efficiently at scale is a distinct engineering problem from training one. You need to keep GPUs saturated, manage memory bandwidth, serve thousands of concurrent requests with low latency, and do all of it at a price that makes business sense. The inference providers have spent years optimizing exactly this problem - and they have gotten very good at it.

The result is a market where the same Llama 3.3 70B model might cost $0.59 per million tokens on one provider and arrive in 200ms, or $2.00 per million tokens on a generic cloud GPU and take 800ms. Same weights, different infrastructure, very different outcomes for your app.

This is not a niche corner of the AI industry. It is a genuine competitive frontier, and the providers in it are building custom silicon, proprietary kernels, and new chip architectures - all to win on two numbers: tokens per second and dollars per million tokens.

Groq: Purpose-Built Silicon for Speed

Groq is the most hardware-differentiated player in this space. Instead of running models on GPUs, it built its own chip: the Language Processing Unit, or LPU. The LPU was designed from first principles to do one thing - run autoregressive text generation as fast as physically possible.

The key insight behind the LPU is that LLM inference is memory-bandwidth bound, not compute bound. A GPU is a flexible, general-purpose chip that handles many tasks. An LPU eliminates that flexibility in exchange for deterministic scheduling and extremely high memory bandwidth to the chip's on-chip SRAM. The Groq LPU v1 features 230 MB of on-chip SRAM with 80 TB/s internal bandwidth - so model weights move to compute units at full speed with no waiting.

In practice this translates to speeds that feel genuinely different to a user. Llama 3.3 70B runs at over 250 tokens per second on Groq's current hardware - compared to 50-150 tokens per second on a typical GPU endpoint. Smaller models like GPT-OSS 20B hit 1,000 tokens per second. That is the difference between a response that streams in over several seconds and one that appears nearly instantly.

In early 2026, Groq (now under Nvidia following an acquisition) announced the Groq 3 LPU, targeting 1,500 tokens per second for agentic workloads. The architecture pairs with Nvidia's Vera Rubin GPU for the compute-heavy prefill phase, then hands off to the LPU for the memory-bandwidth-heavy decode phase.

Groq's API is OpenAI-compatible, so switching is a one-line change. Pricing is per token - Llama 3.3 70B runs at $0.59 input / $0.79 output per million tokens as of mid-2026. There is a free tier with no credit card required (rate-limited but real), and batch processing cuts costs by 50%. Prompt caching for repeated prefixes saves another 50%.

from groq import Groq

client = Groq()
response = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "Explain inference latency in one paragraph."}]
)
print(response.choices[0].message.content)

Groq is the right call when latency is the primary constraint - real-time voice interfaces, live coding assistants, interactive chat where users notice delay, or agentic loops where each LLM call feeds the next one.

Together AI: The Open-Model Catalog

Together AI's pitch is breadth. Where Groq runs a focused set of models at maximum speed, Together hosts 200-plus models across text, image, code, and audio - all through a unified API with pay-per-token serverless pricing.

The catalog includes every major open-weight family: Llama, Qwen, DeepSeek, Mistral, and a rotating set of newer arrivals. In mid-2026 the lineup includes DeepSeek V3.1 ($0.60 input / $1.70 output per million tokens), Qwen 3.6 Plus ($0.50 / $3.00), and reasoning-focused models like DeepSeek R1 at $3.00 / $7.00 per million. The cheapest serverless endpoints run as low as $0.05 per million tokens for smaller models.

Together's infrastructure is GPU-based - H100s and equivalents - so raw token throughput is not in Groq or Cerebras territory. What Together offers instead is flexibility. You can prototype on a tiny model, move to a large one when accuracy matters, run fine-tunes on custom checkpoints, and batch-process millions of tokens at 50% off. Batch jobs can handle up to 30 billion tokens asynchronously - useful for dataset processing, document analysis, or offline enrichment pipelines where latency does not matter but cost does.

Dedicated endpoints are available if you need consistent performance: dedicated H100 capacity at around $6.49/hr. Fine-tuning with LoRA and full-parameter training is also offered, with hosting afterward.

The Together API is fully OpenAI-compatible. There is no free trial - the minimum is a $5 credit purchase - but the serverless model means you pay only for what you use, with no minimum commitment.

Together is the right call when you need to evaluate many models quickly, run large batch workloads cheaply, or access a specific open-weight model the other providers do not host.

Fireworks AI: Reliability and Structured Output

Fireworks competes on a dimension the other providers underweight: production reliability and structured output accuracy. This matters more than it sounds in 2026, as more applications depend on models returning valid JSON, calling tools correctly, or following precise schemas.

Fireworks built FireAttention, a proprietary inference kernel that delivers roughly 4x higher throughput and 50% lower latency than open-source serving alternatives. The platform is SOC 2 Type II, GDPR, and HIPAA compliant, and offers private deployments with audit logs and role-based access control - things that matter for regulated industries.

For function calling and structured output, Fireworks invests in models and evaluation that competitors do not. Their FireFunction models achieve over 92% accuracy on complex multi-tool benchmarks. This compounds in agentic workflows: if each tool call in a five-step agent has 85% accuracy, the probability of a clean run end-to-end is 44%. At 95% per call, that jumps to 77%. Accuracy at the individual call level has outsized impact on overall system reliability.

Pricing is per token with cache discounts built in. Cached input tokens are priced at 50% of the standard input rate by default. Batch inference is billed at 50% of serverless pricing. GPU deployments range from $2.90/hr for A100 80GB to $9.00/hr for Blackwell B200. New accounts get $1 in free credits.

import openai

client = openai.OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key="YOUR_FIREWORKS_KEY"
)

response = client.chat.completions.create(
    model="accounts/fireworks/models/firefunction-v2",
    messages=[{"role": "user", "content": "Extract the product name and price from this receipt text."}],
    response_format={"type": "json_object"}
)

Fireworks is the right call for production systems that need structured output, function calling reliability, compliance requirements, or fine-tuned models on custom data.

Cerebras: Wafer-Scale Speed

Cerebras takes the hardware differentiation idea further than anyone. The Wafer-Scale Engine 3 (WSE-3) is literally the largest chip ever built - 46,225 mm² of silicon, 4 trillion transistors, 900,000 AI-optimized cores. A single Cerebras CS-3 system delivers 125 petaflops of AI compute.

The result is inference speeds that sit at the top of any benchmark table. Cerebras targets 3,000-plus tokens per second on the API, with reported speeds of 2,700-plus tokens per second on large models. The WSE-3's memory bandwidth is approximately 7,000x higher than an H100 - the chip solves the memory-bandwidth bottleneck by making it enormous.

The Cerebras Inference API is fully OpenAI-compatible. Models include Llama 3.3 70B, Qwen 3, DeepSeek variants, and large models like Qwen3 Coder 480B. Pricing runs from roughly $0.50 to $3.90 per million tokens depending on model size. The free tier is notably generous: 1 million tokens per day, resetting daily, with no credit card required. Developer tier starts at $10. Reserved capacity for high-volume batch workloads drops to $0.25-$0.60 per million tokens.

In early 2026, AWS announced it will bring Cerebras CS-3 systems into its data centers and offer Cerebras-powered inference through Amazon Bedrock - a signal that wafer-scale inference is graduating from novelty to infrastructure.

Cerebras is the right call for workloads where raw speed is everything - real-time reasoning with large models, streaming inference for live applications, or any scenario where you are willing to pay more per token to get results in milliseconds rather than seconds.

How to Choose

These providers are not interchangeable. Each one optimizes for a different constraint. Here is a practical framework:

All four use OpenAI-compatible endpoints. Switching between them is a two-line change in most SDKs: update the base URL and the model name. This makes it practical to use more than one - Groq for real-time paths, Together for batch, Fireworks for tool-calling pipelines.

# Switch provider by changing two lines
client = openai.OpenAI(
    base_url="https://api.groq.com/openai/v1",   # swap this
    api_key=os.environ["GROQ_API_KEY"]
)
model = "llama-3.3-70b-versatile"  # and this

The Bigger Picture

The inference layer is moving fast. Custom silicon that was in preview last year is in production this year. Prices dropped significantly over the past 12 months and the trend continues. The Nvidia-Groq partnership and the Cerebras-AWS deal both signal that the major cloud players see inference speed as the next infrastructure race - not just a startup niche.

For builders, the practical takeaway is to stop treating inference as a single commodity. The right provider for your real-time voice feature is not the right provider for your nightly document processing job. Treating them as interchangeable leaves money and performance on the table.

The model labs win on capability and research. The inference providers win on the gap between a model existing and a model being usable in production at scale. Both matter, and they are not the same business.

Key Takeaways

Try this next: Now that you know who runs the models, learn what the models actually are - Open-Weight Models: Llama, Mistral, and the Open Frontier walks through the model families these providers serve and how to pick between them for a given task.

LearnlandscapeThe Inference Layer: Groq, Together, Fireworks, and Cerebras
Guidelandscapecore8 min read

The Inference Layer: Groq, Together, Fireworks, and Cerebras

Inference speed and cost are their own competitive frontier. Meet the providers sitting between the model labs and your app - and why choosing the right one matters.

CREATORS OF TODAY.
@creatorsoftoday
319

TL;DR: OpenAI, Anthropic, and Google build the frontier models. But a whole separate industry has emerged to run open-weight models faster and cheaper than the labs themselves can. Groq, Together AI, Fireworks AI, and Cerebras compete on speed, price, and reliability - not research. Understanding this layer lets you cut costs, slash latency, and pick the right tool for each job.

Why the Inference Layer Exists

When a model lab like Meta releases Llama, the weights are public. Anyone can run it. The question is how well you run it - and that is where inference providers come in.

Running a large language model efficiently at scale is a distinct engineering problem from training one. You need to keep GPUs saturated, manage memory bandwidth, serve thousands of concurrent requests with low latency, and do all of it at a price that makes business sense. The inference providers have spent years optimizing exactly this problem - and they have gotten very good at it.

The result is a market where the same Llama 3.3 70B model might cost $0.59 per million tokens on one provider and arrive in 200ms, or $2.00 per million tokens on a generic cloud GPU and take 800ms. Same weights, different infrastructure, very different outcomes for your app.

This is not a niche corner of the AI industry. It is a genuine competitive frontier, and the providers in it are building custom silicon, proprietary kernels, and new chip architectures - all to win on two numbers: tokens per second and dollars per million tokens.

Groq: Purpose-Built Silicon for Speed

Groq is the most hardware-differentiated player in this space. Instead of running models on GPUs, it built its own chip: the Language Processing Unit, or LPU. The LPU was designed from first principles to do one thing - run autoregressive text generation as fast as physically possible.

The key insight behind the LPU is that LLM inference is memory-bandwidth bound, not compute bound. A GPU is a flexible, general-purpose chip that handles many tasks. An LPU eliminates that flexibility in exchange for deterministic scheduling and extremely high memory bandwidth to the chip's on-chip SRAM. The Groq LPU v1 features 230 MB of on-chip SRAM with 80 TB/s internal bandwidth - so model weights move to compute units at full speed with no waiting.

In practice this translates to speeds that feel genuinely different to a user. Llama 3.3 70B runs at over 250 tokens per second on Groq's current hardware - compared to 50-150 tokens per second on a typical GPU endpoint. Smaller models like GPT-OSS 20B hit 1,000 tokens per second. That is the difference between a response that streams in over several seconds and one that appears nearly instantly.

In early 2026, Groq (now under Nvidia following an acquisition) announced the Groq 3 LPU, targeting 1,500 tokens per second for agentic workloads. The architecture pairs with Nvidia's Vera Rubin GPU for the compute-heavy prefill phase, then hands off to the LPU for the memory-bandwidth-heavy decode phase.

Groq's API is OpenAI-compatible, so switching is a one-line change. Pricing is per token - Llama 3.3 70B runs at $0.59 input / $0.79 output per million tokens as of mid-2026. There is a free tier with no credit card required (rate-limited but real), and batch processing cuts costs by 50%. Prompt caching for repeated prefixes saves another 50%.

from groq import Groq

client = Groq()
response = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "Explain inference latency in one paragraph."}]
)
print(response.choices[0].message.content)

Groq is the right call when latency is the primary constraint - real-time voice interfaces, live coding assistants, interactive chat where users notice delay, or agentic loops where each LLM call feeds the next one.

Together AI: The Open-Model Catalog

Together AI's pitch is breadth. Where Groq runs a focused set of models at maximum speed, Together hosts 200-plus models across text, image, code, and audio - all through a unified API with pay-per-token serverless pricing.

The catalog includes every major open-weight family: Llama, Qwen, DeepSeek, Mistral, and a rotating set of newer arrivals. In mid-2026 the lineup includes DeepSeek V3.1 ($0.60 input / $1.70 output per million tokens), Qwen 3.6 Plus ($0.50 / $3.00), and reasoning-focused models like DeepSeek R1 at $3.00 / $7.00 per million. The cheapest serverless endpoints run as low as $0.05 per million tokens for smaller models.

Together's infrastructure is GPU-based - H100s and equivalents - so raw token throughput is not in Groq or Cerebras territory. What Together offers instead is flexibility. You can prototype on a tiny model, move to a large one when accuracy matters, run fine-tunes on custom checkpoints, and batch-process millions of tokens at 50% off. Batch jobs can handle up to 30 billion tokens asynchronously - useful for dataset processing, document analysis, or offline enrichment pipelines where latency does not matter but cost does.

Dedicated endpoints are available if you need consistent performance: dedicated H100 capacity at around $6.49/hr. Fine-tuning with LoRA and full-parameter training is also offered, with hosting afterward.

The Together API is fully OpenAI-compatible. There is no free trial - the minimum is a $5 credit purchase - but the serverless model means you pay only for what you use, with no minimum commitment.

Together is the right call when you need to evaluate many models quickly, run large batch workloads cheaply, or access a specific open-weight model the other providers do not host.

Fireworks AI: Reliability and Structured Output

Fireworks competes on a dimension the other providers underweight: production reliability and structured output accuracy. This matters more than it sounds in 2026, as more applications depend on models returning valid JSON, calling tools correctly, or following precise schemas.

Fireworks built FireAttention, a proprietary inference kernel that delivers roughly 4x higher throughput and 50% lower latency than open-source serving alternatives. The platform is SOC 2 Type II, GDPR, and HIPAA compliant, and offers private deployments with audit logs and role-based access control - things that matter for regulated industries.

For function calling and structured output, Fireworks invests in models and evaluation that competitors do not. Their FireFunction models achieve over 92% accuracy on complex multi-tool benchmarks. This compounds in agentic workflows: if each tool call in a five-step agent has 85% accuracy, the probability of a clean run end-to-end is 44%. At 95% per call, that jumps to 77%. Accuracy at the individual call level has outsized impact on overall system reliability.

Pricing is per token with cache discounts built in. Cached input tokens are priced at 50% of the standard input rate by default. Batch inference is billed at 50% of serverless pricing. GPU deployments range from $2.90/hr for A100 80GB to $9.00/hr for Blackwell B200. New accounts get $1 in free credits.

import openai

client = openai.OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key="YOUR_FIREWORKS_KEY"
)

response = client.chat.completions.create(
    model="accounts/fireworks/models/firefunction-v2",
    messages=[{"role": "user", "content": "Extract the product name and price from this receipt text."}],
    response_format={"type": "json_object"}
)

Fireworks is the right call for production systems that need structured output, function calling reliability, compliance requirements, or fine-tuned models on custom data.

Cerebras: Wafer-Scale Speed

Cerebras takes the hardware differentiation idea further than anyone. The Wafer-Scale Engine 3 (WSE-3) is literally the largest chip ever built - 46,225 mm² of silicon, 4 trillion transistors, 900,000 AI-optimized cores. A single Cerebras CS-3 system delivers 125 petaflops of AI compute.

The result is inference speeds that sit at the top of any benchmark table. Cerebras targets 3,000-plus tokens per second on the API, with reported speeds of 2,700-plus tokens per second on large models. The WSE-3's memory bandwidth is approximately 7,000x higher than an H100 - the chip solves the memory-bandwidth bottleneck by making it enormous.

The Cerebras Inference API is fully OpenAI-compatible. Models include Llama 3.3 70B, Qwen 3, DeepSeek variants, and large models like Qwen3 Coder 480B. Pricing runs from roughly $0.50 to $3.90 per million tokens depending on model size. The free tier is notably generous: 1 million tokens per day, resetting daily, with no credit card required. Developer tier starts at $10. Reserved capacity for high-volume batch workloads drops to $0.25-$0.60 per million tokens.

In early 2026, AWS announced it will bring Cerebras CS-3 systems into its data centers and offer Cerebras-powered inference through Amazon Bedrock - a signal that wafer-scale inference is graduating from novelty to infrastructure.

Cerebras is the right call for workloads where raw speed is everything - real-time reasoning with large models, streaming inference for live applications, or any scenario where you are willing to pay more per token to get results in milliseconds rather than seconds.

How to Choose

These providers are not interchangeable. Each one optimizes for a different constraint. Here is a practical framework:

  • Latency is your bottleneck (voice, real-time chat, agentic loops): Start with Groq. If you need the absolute ceiling on speed for large models, test Cerebras.
  • Cost is your bottleneck (batch processing, offline pipelines, high volume): Together AI's batch endpoint or Fireworks batch pricing at 50% off serverless.
  • Model selection is your bottleneck (you need a specific checkpoint): Together AI's 200-plus catalog is the most likely to have it. Fireworks is a strong second.
  • Reliability and compliance are your bottleneck (production, regulated industries, agents that call tools): Fireworks. SOC 2, HIPAA, function calling accuracy, private deployments.
  • You are prototyping: Groq's free tier (no credit card) or Cerebras's daily free million tokens are the fastest way to get a real API response in under five minutes.

All four use OpenAI-compatible endpoints. Switching between them is a two-line change in most SDKs: update the base URL and the model name. This makes it practical to use more than one - Groq for real-time paths, Together for batch, Fireworks for tool-calling pipelines.

# Switch provider by changing two lines
client = openai.OpenAI(
    base_url="https://api.groq.com/openai/v1",   # swap this
    api_key=os.environ["GROQ_API_KEY"]
)
model = "llama-3.3-70b-versatile"  # and this

The Bigger Picture

The inference layer is moving fast. Custom silicon that was in preview last year is in production this year. Prices dropped significantly over the past 12 months and the trend continues. The Nvidia-Groq partnership and the Cerebras-AWS deal both signal that the major cloud players see inference speed as the next infrastructure race - not just a startup niche.

For builders, the practical takeaway is to stop treating inference as a single commodity. The right provider for your real-time voice feature is not the right provider for your nightly document processing job. Treating them as interchangeable leaves money and performance on the table.

The model labs win on capability and research. The inference providers win on the gap between a model existing and a model being usable in production at scale. Both matter, and they are not the same business.

Key Takeaways

  • Groq's LPU is purpose-built for text generation speed: 250-1,000 tokens per second depending on model size, versus 50-150 on GPU endpoints.
  • Together AI offers 200-plus open-weight models with batch processing at 50% off - best for variety and high-volume offline workloads.
  • Fireworks invests in structured output and function calling reliability (92%+ multi-tool accuracy), plus SOC 2 / HIPAA compliance for production use.
  • Cerebras runs the largest chip ever built and targets 3,000-plus tokens per second - the highest raw speed available through an API today.
  • All four are OpenAI-compatible: switching is two lines of code, so testing multiple providers is low-friction.
  • Price and speed are no longer the same axis. Custom silicon can be both faster and cheaper for the right workload - or more expensive for the wrong one. Match provider to use case.

Try this next: Now that you know who runs the models, learn what the models actually are - Open-Weight Models: Llama, Mistral, and the Open Frontier walks through the model families these providers serve and how to pick between them for a given task.

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