Groq vs Together AI vs Fireworks AI: Fastest LLM API in 2026

If you're building anything with open-source LLMs — chatbots, coding agents, RAG pipelines, batch processing — you need an inference API. OpenAI and Anthropic own the proprietary model space, but for running Llama, Qwen, Mistral, and DeepSeek at scale, three providers dominate: Groq, Together AI, and Fireworks AI.

Each takes a fundamentally different approach. Groq built custom silicon (LPUs) for raw speed. Together AI built the broadest model marketplace with aggressive pricing. Fireworks AI optimized their software stack for low-latency production workloads.

Here's how they actually compare in 2026 — pricing, speed, model selection, and which one you should pick for your use case.

Quick Comparison Table

*Prices as of March 2026. Blended = average of input + output pricing at 3:1 ratio.*

Groq: When Speed Is Everything

Groq is the speed freak. While everyone else runs inference on NVIDIA GPUs, Groq built their own chip — the Language Processing Unit (LPU) — from the ground up for transformer inference.

The results are hard to argue with: Llama 3.1 8B at 657 tokens per second. Their latest gpt-oss-20B model hits 879 tok/s. That's 3-4x faster than GPU-based providers for the same model. Time to first token? Under 0.5 seconds for most models.

Pricing

Groq's pricing is aggressive at the small end:

• Llama 3.1 8B: $0.06/M tokens (blended) — effectively free at small scale
• Llama 3.3 70B: ~$0.59/M blended
• Llama 4 Maverick: ~$0.27/M input, $0.85/M output
• Llama 4 Scout: $0.17/M blended
• Qwen3 32B: Available but priced higher than Together
• DeepSeek-R1: Not available (Groq focuses on non-reasoning open models)

The free tier gives you meaningful usage with rate limits — enough to prototype and test. For production, their pay-as-you-go pricing kicks in without minimums.

Model Selection

This is Groq's weakness. With ~12 models, the catalog is focused: Llama family, Qwen3, and a few others. No fine-tuning. No image generation. No dedicated endpoints. If you need a specific model or want to deploy a custom fine-tune, Groq isn't the platform.

When to Choose Groq

• Real-time chatbots where latency directly impacts user experience
• Agent loops where each LLM call is on the critical path and you're making dozens of calls per task — check our guide on building AI coding agents where inference speed directly impacts iteration cycles
• Interactive coding assistants that need sub-second suggestions
• Prototyping on the free tier before committing to a provider

When to Avoid Groq

• You need DeepSeek, Mixtral 8x22B, or niche models
• You want fine-tuning or dedicated deployments
• You need guaranteed SLAs (Groq is newer and less battle-tested at enterprise scale)
• Batch processing where speed doesn't matter but cost per token does

Together AI: The Model Supermarket

Together AI takes the opposite approach from Groq. Instead of building custom chips, they built the broadest inference platform in the open-source ecosystem. Over 200 models available — LLMs, image generators (FLUX, Stable Diffusion), video models, embedding models, rerankers, and safety classifiers. It's a one-stop shop.

Pricing

Together's pricing is competitive and transparent:

LLMs (per 1M tokens):

• Gemma 3n E4B: $0.02 input / $0.04 output — cheapest inference anywhere
• Llama 3 8B Lite: $0.10 / $0.10 — dead simple flat rate
• gpt-oss-120B: $0.15 / $0.60 — incredible value for a 120B model
• Llama 4 Maverick: $0.27 / $0.85
• DeepSeek-V3.1: $0.60 / $1.70
• Llama 3.3 70B: $0.88 / $0.88
• DeepSeek-R1-0528: $3.00 / $7.00
• Qwen3 235B (FP8): $0.20 / $0.60 — huge MoE model at bargain pricing

Dedicated endpoints: Starting at $3.99/hr for a single H100 80GB. If you need guaranteed throughput or want to deploy a custom fine-tuned model, this is how you do it. Reserved pricing drops to $2.25/hr for 4-6 month commitments.

Fine-tuning: LoRA fine-tuning from $0.48/M tokens for models up to 16B. Full fine-tuning available too. This is a major differentiator — neither Groq nor Fireworks offers comparable training capabilities.

Model Selection

This is Together's crown jewel. The model catalog includes:

• LLMs: Llama 4 (Maverick, Scout), DeepSeek (V3.1, R1), Qwen3 (8B through 235B), Mistral, GLM-5, Kimi K2, gpt-oss-120B, MiniMax M2.5
• Image: FLUX 2.0 (all variants), Stable Diffusion, Ideogram, Google Imagen 4.0
• Video: MiniMax Hailuo, Google Veo 3.0, Sora 2, Kling 2.1
• Audio: Whisper Large v3, Cartesia Sonic
• Embeddings: Multilingual e5
• Safety: Llama Guard 4, VirtueGuard

For teams building products that need LLMs + image generation + embeddings + moderation, Together eliminates the need to manage multiple providers.

When to Choose Together AI

• Production platforms that need access to many models without managing multiple API integrations
• Teams that fine-tune — Together's training infrastructure is mature and competitively priced
• Multi-modal applications — one API key covers text, image, video, audio, and embeddings
• Batch processing where the Batch API pricing offers additional savings
• Cost optimization — the breadth of models means you can route simple queries to cheap models ($0.02/M) and complex ones to powerful models

When to Avoid Together AI

• Pure speed is your priority (Groq is 2-3x faster for the same model)
• You only need one model and want the simplest possible setup
• Latency-critical applications where every 100ms matters

Fireworks AI: The Production Workhorse

Fireworks positions itself between Groq's speed and Together's breadth. Their core thesis: optimize the inference software stack so hard that you get near-custom-silicon speed on standard NVIDIA hardware. It's working — their gpt-oss-120B runs at 696 tok/s, competitive with Groq's non-LPU speeds.

Pricing

Fireworks keeps pricing simple:

• Qwen3 8B: $0.20/M blended — their entry-level model
• gpt-oss-120B: $0.26/M blended — exceptionally cheap for a 120B reasoning model
• Qwen3 30B: $0.26/M blended
• DeepSeek V3.2: Available at competitive rates
• GLM-5: Higher pricing tier for their top intelligence model
• Kimi K2.5: $0.50/M input, $2.80/M output

Free tier available with rate limits. Dedicated endpoints for production workloads.

Model Selection

Fireworks curates rather than aggregates. With ~17 core models, they focus on the models that matter most for production use:

• Top-tier reasoning: GLM-5, Kimi K2.5, MiniMax-M2.5, DeepSeek V3.2
• Efficient workhorses: gpt-oss-120B, Qwen3 family (8B, 30B, VL 30B)
• Established favorites: Llama family

Every model on Fireworks supports function calling and JSON mode — this isn't always true on other platforms. For agent architectures that rely on structured tool use, this consistency matters.

Performance

Fireworks' software optimization shows in the numbers:

• gpt-oss-120B: 696 tok/s — fastest 120B model deployment anywhere outside Groq
• Kimi K2.5: 354-362 tok/s — competitive with Groq for this model class
• DeepSeek V3.1: 337 tok/s
• Time to first token: gpt-oss-120B at 0.37s — actually faster than Groq for this specific model

Their optimization work extends beyond raw speed to consistency. Fireworks emphasizes P99 latency, not just average — meaning your worst-case response time is predictable, which matters more than peak speed for production applications.

When to Choose Fireworks

• Production workloads that need consistent, predictable latency
• Agent/tool-use applications where reliable function calling is critical
• Teams using DeepSeek, GLM, or Kimi models — Fireworks often has the best optimized deployments
• Enterprise requirements with SLAs and dedicated infrastructure

When to Avoid Fireworks

• You need the absolute fastest inference (Groq's LPU wins on raw speed for supported models)
• You need a huge model catalog or fine-tuning (Together wins)
• Budget is the only concern (Together's cheapest models undercut Fireworks)

Pricing Deep Dive: What Does Inference Actually Cost?

Let's make this concrete. Say you're building a chatbot that handles 10,000 conversations per day, averaging 500 input tokens and 300 output tokens per conversation.

Monthly usage: 10,000 × 30 = 300,000 conversations

• Input: 300,000 × 500 = 150M tokens
• Output: 300,000 × 300 = 90M tokens

Cost with Llama 3.1 8B (cheapest option per provider):

Cost with a 70B+ model:

The takeaway: for small models, Groq is cheapest. For large models, Fireworks and Together's gpt-oss-120B pricing is hard to beat — you get 120B-class intelligence at 70B prices.

Free Tiers Compared

All three offer free access, but the limits differ:

• Groq: Free tier with generous rate limits. Best for prototyping and personal projects. The rate limits tighten during peak hours.
• Together AI: $1 in free credits on signup. Once that's gone, you pay. Best for evaluating models before committing.
• Fireworks AI: Free tier with rate limits similar to Groq. Good for testing.

For developers building side projects, Groq's free tier is the most generous. It's enough to run a personal chatbot or a low-traffic agent indefinitely.

Self-Hosted: When Cloud APIs Stop Making Sense

There's a fourth option nobody in this space wants you to think about: running inference yourself.

If you're spending more than $200/month on API calls and your latency requirements allow for local hardware, the math starts to favor self-hosting. A high-VRAM GPU like the RTX 4090 with 24 GB VRAM can run Llama 3.1 70B at Q4 quantization (with layer offloading) or comfortably serve any 7-13B model at full speed. The upfront cost pays for itself in 4-6 months compared to API spending at scale.

Self-hosting makes sense when:

• You have predictable, consistent workloads (not spiky)
• Data privacy requirements prohibit sending prompts to third parties
• You need full control over the model (custom quantization, batching, caching)
• You're already running local inference with Ollama

It doesn't make sense when you need elastic scaling, zero maintenance, or access to models too large for consumer hardware. For those cases, check our cloud GPU comparison — renting an A100 or H100 by the hour can be a middle ground between APIs and buying hardware.

If you're on a Mac, Apple Silicon's unified memory architecture is surprisingly competitive for local inference — our Mac LLM guide covers what models run well on which hardware tier.

Use Case Recommendations

Building a Chatbot

Pick Groq. Users feel the difference between 200 tok/s and 650 tok/s. Streaming responses that appear nearly instantly create a qualitatively different experience. Groq's Llama 3.1 8B on the free tier is enough for an MVP. Scale to Llama 4 Maverick when you need more intelligence.

Building an AI Agent

Pick Together AI or Fireworks. Agent loops involve many sequential LLM calls with function calling. You need reliable tool use (both providers excel here), a model with strong instruction following, and pricing that doesn't explode when your agent takes 20 turns to complete a task. Together's gpt-oss-120B at $0.15/M input is a strong choice. Fireworks if you need the lowest-latency function calling.

For agent architecture patterns, see our context engineering guide — efficient context management reduces token usage more than switching providers.

Batch Processing Documents

Pick Together AI. Their Batch API pricing adds additional savings on top of already-low serverless rates. Route to the cheapest model that meets your quality bar (Gemma 3n at $0.02/M for simple extraction, gpt-oss-120B at $0.15/M for complex reasoning). The breadth of models lets you optimize cost per task type.

Multi-Modal Applications

Pick Together AI. No contest. One API for text, images (FLUX 2.0, Imagen 4.0), video (Veo 3.0, Sora 2), audio (Whisper, Cartesia Sonic), embeddings, and moderation. Managing one provider instead of five reduces integration complexity significantly.

Enterprise with SLA Requirements

Pick Fireworks or Together with dedicated endpoints. Both offer single-tenant GPU instances with guaranteed capacity. Together's reserved pricing starts at $2.25/hr for H100s on 4-6 month commitments. Fireworks emphasizes P99 latency guarantees that matter for customer-facing products.

The Verdict

There's no single "best" provider. The right choice depends on your priorities:

• Groq if you're optimizing for speed and real-time user experience. The LPU advantage is real and measurable. The trade-off is a limited model catalog and no fine-tuning.

• Together AI if you want the broadest platform with fine-tuning, multi-modal capabilities, and aggressive pricing across 200+ models. The "AWS of open-source AI" analogy fits — everything's there, and the pricing rewards scale.

• Fireworks AI if you're building production systems where consistent latency, reliable function calling, and curated model quality matter more than breadth or raw peak speed.

Most serious teams will use more than one. Route speed-sensitive user-facing calls through Groq, batch processing through Together, and critical agent workflows through Fireworks. The OpenAI-compatible API format they all support makes multi-provider routing straightforward.

The inference API market is moving fast. Prices dropped 60-80% in the last 12 months, and speeds doubled. Whatever you pick today will be cheaper and faster next quarter. Start with the free tier of all three, benchmark on your actual workload, and commit when you have data — not opinions.

*Running models locally instead of through APIs? Check our Ollama production config guide and Mac Apple Silicon LLM guide. Building agents that call these APIs? Our context engineering deep dive covers how to minimize token usage across multi-turn agent loops.*

*Disclosure: Links above are affiliate links. ToolHalla may earn a commission at no extra cost to you. We only recommend hardware we'd actually use.*

FAQ

What is the difference between Groq, Together AI, and Fireworks AI?

Groq: fastest inference (LPU hardware), best for latency-sensitive applications. Together AI: widest model selection with fine-tuning support. Fireworks AI: competitive speeds with function calling and JSON mode support. All offer free tiers.

Which cloud inference provider is fastest?

Groq is the fastest for supported models — achieving 500-800 tok/s on Llama 3.1 70B, versus 50-150 tok/s on GPU-based providers. Groq's LPU (Language Processing Unit) hardware is purpose-built for inference throughput.

How does Groq pricing compare to OpenAI?

Groq is significantly cheaper: ~$0.59/M input tokens for Llama 3.1 70B. OpenAI GPT-4o is $2.50/M input tokens. For equivalent quality models, Groq costs 4-10× less. Free tier includes generous usage limits.

Does Together AI support fine-tuning?

Yes — Together AI has the most comprehensive fine-tuning support among cloud inference providers. Supports LoRA and full fine-tuning on most major model families with dedicated fine-tuning endpoints.

Which provider is best for production inference?

Depends on requirements. Groq: lowest latency, best for real-time apps. Fireworks: best function calling and structured output. Together: best model variety and fine-tuning. All have 99.9%+ uptime SLAs and enterprise options.