Together AI vs Groq vs Fireworks: LLM Inference APIs Compared

Most teams spend weeks evaluating which LLM to use and about thirty minutes picking where to run it. That is backwards. The same Llama 3.1 70B model can cost 3x more on one provider versus another, respond 5x faster, and come with completely different rate limits, fine-tuning options, and reliability guarantees. Your inference provider is the layer you interact with every single API call. It deserves real evaluation.

Together AI, Groq, and Fireworks AI have emerged as the three most credible alternatives to running open models through the big cloud providers. Each takes a fundamentally different approach to the problem. Together AI built a full-stack platform around open models with training, fine-tuning, and inference in one place. Groq designed custom LPU hardware from scratch to deliver the lowest latency in the industry. Fireworks built a developer-first platform optimized for compound AI systems where you chain multiple models and tools together.

I have run production workloads on all three. This article covers what actually matters: latency, cost, model availability, fine-tuning, rate limits, and the specific scenarios where each one wins. If you are evaluating inference providers for a product that needs to scale, this is the comparison I wish I had when I started.

Speed is the headline metric for inference providers, and it is where these three diverge the most. I measured time-to-first-byte (TTFB) and output tokens per second across their most popular models. Tests were run from US-East, averaged over 100 requests with a 512-token input prompt and 256-token max output.

Llama 3.1 70B Instruct:

• Groq: 85ms TTFB, 310 tokens/sec output. This is not a typo. Groq's LPU hardware is genuinely that fast.
• Fireworks: 220ms TTFB, 95 tokens/sec output. Solid performance, especially considering the price.
• Together AI: 280ms TTFB, 80 tokens/sec output. Competitive but noticeably slower than Groq.

Llama 3.1 8B Instruct:

• Groq: 45ms TTFB, 750 tokens/sec. Absurdly fast. Users literally cannot read this fast.
• Fireworks: 110ms TTFB, 210 tokens/sec.
• Together AI: 150ms TTFB, 180 tokens/sec.

Mixtral 8x7B:

• Groq: 60ms TTFB, 480 tokens/sec.
• Fireworks: 180ms TTFB, 120 tokens/sec.
• Together AI: 200ms TTFB, 105 tokens/sec.

Groq wins the speed contest decisively, and it is not close. Their custom LPU (Language Processing Unit) silicon is purpose-built for sequential token generation, which eliminates the memory bandwidth bottleneck that limits GPU-based inference. For latency-sensitive applications like real-time chat, voice AI, or coding assistants, Groq's speed advantage is a genuine differentiator.

That said, raw speed is not everything. Groq's model selection is narrower, and their pricing reflects the hardware investment. If your application streams responses to users (which most chat UIs do), the perceived speed difference between 95 tokens/sec and 310 tokens/sec is minimal because both exceed human reading speed. The TTFB difference matters more for user experience, and here Groq's sub-100ms response times are genuinely noticeable.

This is where the providers diverge sharply. The model you need determines which providers are even on the table.

Together AI has the broadest model catalog of the three. They support Llama 3.1 (8B, 70B, 405B), Llama 3.2 vision models, Mistral 7B, Mixtral 8x7B, Mixtral 8x22B, CodeLlama variants, Qwen 2.5 models, DeepSeek Coder, and dozens of community fine-tunes. If a popular open model exists, Together AI probably serves it. They also host embedding models (BGE, E5) and image generation models (Stable Diffusion XL, FLUX), making them a one-stop shop for multi-modal AI workloads.

Groq is more selective. They focus on the highest-demand models: Llama 3.1 (8B, 70B), Llama 3.2 (1B, 3B, 11B Vision, 90B Vision), Mixtral 8x7B, and Gemma 2. No 405B Llama (the LPU memory architecture currently limits max model size). No embedding models. No image generation. Groq's strategy is to serve fewer models extremely well rather than supporting everything.

Fireworks takes a middle path. Strong Llama and Mistral support, including the 405B variant. They also support function calling and JSON mode more reliably than the other two, which matters if you are building agentic workflows. Fireworks has invested heavily in their "compound AI" story, where you can chain model calls, tool use, and structured output in a single API flow. They also offer embedding models and a model playground for testing.

For most teams, Together AI's model breadth wins if you need obscure or specialized models. Groq wins if you only need mainstream models and want the fastest possible inference. Fireworks wins if you need structured output, function calling, or plan to build agent-style applications. Check our LLM API pricing guide for a broader comparison that includes OpenAI and Anthropic pricing alongside these providers.

Pricing is where most teams should start. Small differences per million tokens compound into massive cost gaps at production scale. Here are the current rates (as of mid-2026) for the most commonly used model.

Llama 3.1 70B Instruct pricing (per million tokens):

• Together AI: $0.88 input, $0.88 output.
• Groq: $0.59 input, $0.79 output.
• Fireworks: $0.90 input, $0.90 output.

Llama 3.1 8B Instruct pricing (per million tokens):

• Together AI: $0.18 input, $0.18 output.
• Groq: $0.05 input, $0.08 output.
• Fireworks: $0.20 input, $0.20 output.

Now let us run the numbers for realistic workloads. Assume a 50/50 input/output token split on Llama 3.1 70B.

1 million tokens/month (early startup, MVP):

• Together AI: $0.88. Effectively free at this scale. All three providers are pennies.
• Groq: $0.69.
• Fireworks: $0.90.

10 million tokens/month (growing product, real users):

• Together AI: $8.80/month.
• Groq: $6.90/month.
• Fireworks: $9.00/month.

100 million tokens/month (scaled product):

• Together AI: $88/month.
• Groq: $69/month.
• Fireworks: $90/month.

1 billion tokens/month (high-volume production):

• Together AI: $880/month.
• Groq: $690/month.
• Fireworks: $900/month.

At every scale, Groq is the cheapest for this model. The gap is roughly 20-25%. For the 8B model, Groq's pricing advantage is even larger because they price it extremely aggressively at $0.05 per million input tokens.

However, pricing is only part of the story. Together AI and Fireworks both offer volume discounts and committed-use pricing that can close the gap at high volumes. Together AI's dedicated endpoints start at around $0.50 per GPU-hour, which can be cheaper than pay-per-token pricing once you exceed roughly 500M tokens per month. Fireworks offers similar dedicated capacity pricing. Groq does not currently offer dedicated endpoints, which can be a dealbreaker for teams that need guaranteed throughput.

For cost optimization strategies beyond provider selection, see our guide on LLM cost optimization through model routing.

If you are just calling a base model with prompts, all three providers work fine. The differences show up when you need to customize models or process data at scale.

Fine-tuning:

• Together AI: Full fine-tuning support with a clean workflow. Upload your dataset in JSONL, pick a base model, configure hyperparameters, and train. They support LoRA and full fine-tuning for Llama and Mistral models. Pricing is per GPU-hour during training, and serving your fine-tuned model costs the same as the base model. This is the strongest fine-tuning story of the three.
• Fireworks: Fine-tuning support for Llama and Mistral models via LoRA. The workflow is solid, and they recently added support for fine-tuning with function calling data, which is valuable for agent builders. Pricing is competitive with Together AI.
• Groq: No fine-tuning support. Groq's LPU hardware is optimized for inference only. If you need a custom model, Groq is not an option today. You would need to fine-tune elsewhere and then check if Groq supports serving your fine-tuned model (currently they do not serve custom fine-tunes).

Batch inference:

• Together AI: Offers batch inference at 50% off standard pricing. You submit a batch of prompts and get results within hours. Excellent for data processing, evaluation runs, and offline analysis.
• Fireworks: Batch API available with similar discount pricing. Supports structured output in batch mode, which is useful for data extraction pipelines.
• Groq: No batch API. Every request is real-time. This makes sense given their focus on speed, but it means you pay full price for workloads that do not need low latency.

Dedicated endpoints:

• Together AI: Available. You get a dedicated GPU allocation with guaranteed throughput and the ability to serve custom fine-tuned models. Starts around $0.50/GPU-hour for A100s.
• Fireworks: Available. Similar offering with their own optimized serving stack. They claim 2-4x better throughput per GPU than naive vLLM deployments.
• Groq: Not available for individual customers. Enterprise agreements may include capacity guarantees, but there is no self-serve dedicated endpoint option.

Production applications hit rate limits. This is the section most comparison articles skip, and it is where teams get burned.

Rate limits (free tier / paid tier):

• Together AI: Free tier is limited to 60 requests/minute. Paid tier scales to 600 requests/minute by default, with higher limits available on request. Token limits are generous at 1M tokens/minute on paid plans.
• Groq: Free tier is 30 requests/minute with 14,400 tokens/minute. Paid tier jumps to 1,000 requests/minute. Groq is more restrictive on free tier but reasonable on paid plans. The key constraint is that during peak demand, Groq may queue requests, adding latency on top of their normally fast inference.
• Fireworks: Free tier is 600 requests/minute (the most generous free tier of the three). Paid tier scales up significantly, and dedicated endpoints remove rate limits entirely.

API compatibility:

All three providers offer OpenAI-compatible APIs. This means you can swap providers by changing a base URL and API key if you are using the OpenAI SDK. This is a huge deal for portability. In practice, there are small differences in how each provider handles function calling, streaming, and edge cases in the chat completions format. Fireworks has the most complete OpenAI compatibility, including tool use and JSON mode. Together AI is close behind. Groq handles the basics well but has occasional quirks with complex function calling schemas.

SDKs:

• Together AI: Official Python and TypeScript SDKs, plus OpenAI SDK compatibility. Their Python SDK has nice helpers for fine-tuning workflows and dataset management.
• Groq: Official Python and TypeScript SDKs (groq-python, groq-js). Clean and simple, focused purely on inference.
• Fireworks: Official Python SDK plus OpenAI SDK compatibility. Their SDK has first-class support for structured output with Pydantic models, which saves a lot of boilerplate for data extraction use cases.

Reliability and uptime:

Together AI and Fireworks both publish status pages and have been stable in my experience, with occasional latency spikes during peak hours. Groq has had more growing pains with capacity. During high-demand periods (new model launches, viral demos), Groq's shared infrastructure can experience noticeable slowdowns or queuing. For production workloads that cannot tolerate variability, Together AI and Fireworks with dedicated endpoints are safer bets than Groq's shared pool.

Here is my opinionated recommendation based on shipping production AI products on all three platforms.

Choose Groq if:

• Latency is your top priority. Real-time chat, voice AI, interactive coding assistants, or any application where sub-100ms TTFB changes the user experience.
• You only need mainstream models (Llama 3.x, Mixtral, Gemma).
• You do not need fine-tuning, batch processing, or dedicated endpoints.
• Your workload is steady enough to work within shared infrastructure rate limits.
• You want the lowest per-token cost for standard models.

Choose Together AI if:

• You need fine-tuning as part of your workflow. Together AI has the most complete training and fine-tuning pipeline of the three.
• You want access to the widest variety of open models, including niche or community fine-tunes.
• You need batch inference for offline data processing at discounted rates.
• You want dedicated endpoints for guaranteed throughput and custom model serving.
• You are building a multi-modal application that needs text, embeddings, and image generation from one provider.

Choose Fireworks if:

• You are building agentic applications with function calling, tool use, and structured output. Fireworks has the best developer experience for compound AI systems.
• You need reliable JSON mode and Pydantic-style structured output without prompt engineering hacks.
• You want the most generous free tier for prototyping and development.
• You need the 405B Llama variant with good performance (Together AI also offers this, Groq does not).
• You value API compatibility with OpenAI's format for easy provider switching.

The multi-provider approach:

Honestly, the smartest strategy for most production applications is to use more than one. Route latency-sensitive requests to Groq, batch workloads to Together AI, and agent workflows to Fireworks. All three support OpenAI-compatible APIs, so building a routing layer is straightforward. You can start with one provider and add others as your workload patterns become clear. The cost of provider lock-in with open models is low because the same model weights run everywhere.

If you are evaluating inference providers for a product build, or if you want help designing a multi-provider architecture that optimizes for cost and latency, book a free strategy call. We have helped teams cut inference costs by 40-60% through smart provider routing and model selection.