Together AI vs Fireworks AI — Inference Providers

If you want to run an open-weight large language model (an LLM whose parameters anyone can download and run, as opposed to closed models like GPT-4 or Claude) without buying and babysitting your own GPUs, two companies consistently top developer shortlists in 2026: Together AI and Fireworks AI. Both rent out OpenAI-compatible inference endpoints for models like Llama, Mistral, Qwen, and DeepSeek, and both have raised enormous rounds this year to build the data-center capacity to keep up with demand. This piece breaks down how each company works, what differentiates their engineering, what they actually cost, and which one fits your project.

What problem are Together AI and Fireworks AI solving?

Training or running a frontier-scale language model requires racks of GPUs, a serving stack tuned for low latency, and a team that understands both. Most companies building AI products do not want to be in that business — they want an API endpoint that returns tokens fast and reliably, the same way most companies don't run their own power plants.

That gap is what "managed inference" providers exist to fill. You send a request to an HTTP endpoint that looks just like OpenAI's API, and the provider handles GPU provisioning, model loading, batching, autoscaling, and uptime behind the scenes. The provider makes money on the spread between what raw GPU compute costs them and what they charge per token.

Together AI and Fireworks AI both emerged from this model, but from different starting points. Together grew out of an academic and open-source research lineage — a bet that owning model breadth and training infrastructure would win developers over time. Fireworks grew out of production infrastructure engineering — a bet that shaving milliseconds off every token would win developers instead. Both bets have paid off financially, and both companies raised historic funding rounds in mid-2026.

Fireworks AI now processes roughly 40 trillion tokens per day across its platform, according to the company's July 2026 funding disclosures, up 5x year over year alongside more than $1 billion in annualized revenue.

Together AI: breadth, research credibility, and distributed compute

Together AI was founded in 2022 by Vipul Ved Prakash (previously founder of Topsy, the social-search company Apple acquired in 2013), Ce Zhang, and a team with deep roots in distributed systems and ML research. The company's original technical bet was that large models could be trained across geographically distributed, loosely connected GPUs rather than requiring a single tightly networked supercomputer cluster — a genuinely hard systems problem that shaped the company's later inference architecture.

On July 1, 2026, Together AI closed an $800 million Series C at an $8.3 billion valuation, a 2.5x step-up from the $3.3 billion valuation it held just seventeen months earlier. Investors in the round included Nvidia, Salesforce Ventures, General Catalyst, Vista Equity Partners, Lux Capital, and several others. The company disclosed that its annual bookings crossed $1.15 billion in the final quarter of 2026, driven by what it described as open-source model usage tripling across the industry in the preceding twelve months.

Today Together hosts more than 200 models spanning chat and reasoning (Llama, Mistral, Qwen, DeepSeek, Mixtral), code generation, image generation (FLUX, Stable Diffusion XL), and embedding models used for search and retrieval. The platform's differentiators fall into a few buckets.

Model breadth and research pedigree. Together has co-released open models and datasets with academic labs at Princeton and Stanford, and it runs the RedPajama project, an effort to reproduce large-scale open training datasets that mirror the composition of closed, proprietary corpora. That research visibility functions as a kind of trust signal for developers who want to know the team understands models at the weights level, not just the API level.

First-party fine-tuning. Together offers both LoRA (Low-Rank Adaptation, a technique that trains a small set of additional parameters instead of the full model, dramatically cutting compute cost) and full fine-tuning across a wide range of base models, plus support for preference-based training methods like DPO (direct preference optimization).

Dedicated endpoints and a compute marketplace. Beyond the shared, pay-per-token serverless tier, Together lets customers rent dedicated GPU capacity with custom autoscaling, and it operates a distributed compute marketplace originally built for training workloads that customers can also tap for inference at scale.

Fireworks AI: an engineering-first bet on speed

Fireworks AI was founded in 2022 by Lin Qiao, who previously led the PyTorch engineering team at Meta, alongside several other former Meta AI infrastructure engineers. Where Together's founding story runs through open research, Fireworks' founding story runs through production ML systems — the unglamorous but high-leverage work of making inference runtimes faster and cheaper at the kernel level.

Fireworks raised a $250 million Series C in late 2025 at a $4 billion valuation, led by Lightspeed Venture Partners, Index Ventures, and Sequoia Capital. That valuation moved fast: by July 2026, Fireworks closed a $1.505 billion Series D at a $17.5 billion post-money valuation, led by Atreides Management, Index Ventures, and TCV, with continued participation from Nvidia. The company said it had crossed $1 billion in annualized revenue, five times what it generated a year earlier, while serving roughly 40 trillion tokens per day.

Fireworks' technical identity centers on three pieces of proprietary infrastructure.

FireAttention. This is a set of custom CUDA kernels (low-level GPU code that implements the attention mechanism at the heart of transformer models) engineered to extract more throughput per GPU than stock open-source serving frameworks. Fireworks has published benchmarks claiming up to 5x faster throughput on newer large mixture-of-experts models like DeepSeek V4 Pro compared to unoptimized baselines, though as with any vendor-published benchmark, real-world gains vary by model size, batch size, and hardware generation.

FireOptimizer. An automated pipeline that profiles a customer's actual traffic pattern — request sizes, concurrency, latency targets — and applies distillation (training a smaller, faster model to mimic a larger one's outputs) and quantization (reducing the numerical precision of model weights to speed up computation and shrink memory footprint) to fit that specific workload without the customer manually re-engineering their deployment.

FireFunction. A family of models purpose-trained for function calling and tool use — the mechanism by which an LLM decides to invoke an external API or code function rather than just generating text. FireFunction-V2 is widely cited as one of the strongest open function-calling models available through a managed API, which matters for anyone building agents that need to reliably call tools rather than hallucinate plausible-looking JSON.

Think of Together as a research university that also runs a rental car fleet: deep in the fundamentals, broad in what it offers, happy to let you tinker. Fireworks is more like a Formula 1 pit crew: narrower focus, but every millisecond of the stack has been re-engineered for speed under load.

How managed inference actually works under the hood

To understand why FireAttention or Together's distributed training background matter, it helps to know what happens when your API request hits one of these platforms.

A modern LLM generates text one token at a time, and each new token requires the model to attend to every previous token in the conversation — the "attention mechanism" that gives transformer models their name. Naively recomputing that attention for every token is wasteful, so serving frameworks cache the intermediate results (the "KV cache," short for key-value cache) and reuse them. How efficiently a provider manages that cache, batches multiple users' requests together, and schedules GPU memory determines most of the difference in speed and cost between providers running the exact same open-weight model.

This is also why the same underlying model — say, Llama 3.3 70B — can perform meaningfully differently on two providers. The model's weights are identical; the serving engine wrapped around them is not. Groq and Cerebras take this even further by replacing GPUs entirely with custom chips (Groq's Language Processing Unit and Cerebras's wafer-scale engine) purpose-built for this exact computation pattern, which is why they post the highest raw throughput numbers in the market, at the cost of supporting far fewer models.

Why does the same model run at different speeds on different providers?

Because "the model" is only the weights. Actual generation speed depends on the serving engine's batching strategy, KV-cache management, GPU generation, and kernel optimizations layered on top — which is exactly where Together's and Fireworks' engineering investments diverge.

The broader inference landscape in 2026

Together and Fireworks are the two most-cited generalist providers, but they compete in a crowded field, each occupying a distinct niche.

• Groq — Custom LPU (Language Processing Unit) chips deliver the fastest tokens-per-second on a limited library of popular open models (Llama, Mixtral, Gemma). See our Groq explainer for the deep dive on how the chip architecture achieves that speed.
• Cerebras Inference — Wafer-scale chips post the highest raw throughput in the industry, roughly 2,100 to 2,200 tokens per second on Llama 3.1/3.3-class models, according to 2026 pricing benchmarks, but the model catalog is smaller than Groq's and enterprise-scale access is often negotiated rather than self-serve.
• Anyscale — Built on the Ray distributed-computing framework, positioned more broadly for general ML compute than pure LLM serving.
• Replicate — Strong for multimodal models (image, video, audio generation), less specialized for high-throughput text inference.
• Modal — General-purpose serverless compute; teams use it to self-host inference stacks like vLLM rather than consuming a packaged inference API.
• OpenRouter — An aggregator, not an infrastructure operator. It exposes one unified, OpenAI-compatible endpoint that routes requests to Together, Fireworks, and dozens of other providers behind the scenes, handling failover and cost-based routing automatically. In 2026, developers increasingly reach for OpenRouter specifically to avoid getting locked into any single provider's uptime and pricing.

Table: Managed inference providers, 2026 snapshot

Feature	Together AI	Fireworks AI	Groq	Cerebras	Replicate
Model library	200+	100+	~15-20	~10	Multimodal-heavy
Llama 3.1/3.3 70B speed (approx.)	~150 tok/s	~200 tok/s	~750 tok/s	~1,800-2,100 tok/s	Variable
Function calling	Yes	FireFunction (custom)	Limited	Limited	Per model
Fine-tuning	LoRA + full + DPO	LoRA + distillation	No	No	Per model
Dedicated endpoints	Yes (from ~$6.49/hr H100)	Yes (from ~$7/hr H100)	Limited	Yes, often negotiated	Yes
OpenAI-compatible API	Yes	Yes	Yes	Yes	Partial
Image models	FLUX, SDXL	Yes	No	No	Many
2026 valuation	$8.3B (Series C, Jul 2026)	$17.5B (Series D, Jul 2026)	Private, undisclosed	Private, IPO filed	Private
Free tier / credits	Yes	Yes	Yes	Yes, rate-limited	Free for hobby use

Table: Comparative snapshot of major managed LLM inference providers as of mid-2026. Speed figures are approximate, vary by hardware generation and load, and should not be treated as guaranteed SLAs.

Pricing: what you actually pay

Pricing on both platforms is metered per million tokens, split into input tokens (what you send) and output tokens (what the model generates), and it scales with model size.

Together AI structures pricing across four axes: serverless per-token pricing (roughly $0.03 to $4.50 per million tokens depending on the model), dedicated GPU-hour pricing (around $6.49 per hour for an H100 GPU), rented GPU clusters ($5.49/hr on-demand down to about $3.99/hr on reserved terms), and fine-tuning priced per million training tokens processed rather than per GPU-hour. LoRA fine-tuning starts around $0.48 per million tokens for models up to 16 billion parameters, while larger models in the Llama, Mistral, and Qwen families run roughly $8 to $12 per million training tokens. LoRA training also runs about 10% cheaper than full fine-tuning and completes faster, which is why most production teams default to it.

Fireworks AI prices its serverless tier across Standard, Priority, and Fast service tiers, with input costs commonly ranging from about $0.18 to $0.90 per million tokens depending on model size and tier, and dedicated GPU rental starting around $7 per hour for H100/H200 chips, up to roughly $10 to $12 per hour for the newest B200/B300 generation. Fireworks' most distinctive pricing lever is a 50% discount on cached input tokens — if repeated requests share a long system prompt or conversation history, that shared portion is billed at half rate — stacked with a separate 50% batch-inference discount for non-real-time workloads.

For a concrete sense of scale: running Llama 3.1 8B costs roughly $0.10 to $0.20 per million tokens on either platform, a 70B-class model runs somewhere in the $0.60 to $0.90 per million token range, and 405B-class frontier open models land in the $3 to $5 per million token range. The headline rate-card difference between Together and Fireworks for equivalent models rarely exceeds 10%. Real total-cost differences show up more in fine-tuning strategy, cached-token discounts, and dedicated-endpoint utilization than in the sticker price of a single serverless call.

For pure cost-per-token on the models it supports, Groq typically undercuts both — public pricing for its supported model set has ranged as low as $0.05 per million tokens on smaller models, though its narrower catalog limits which projects can actually take advantage of that rate.

Speed: when milliseconds actually matter

For a standard chat interface, both Together and Fireworks are fast enough that the difference is invisible to users. Human reading speed tops out around 200 to 300 words per minute, which translates to roughly 4 to 5 tokens per second; anything above about 100 tokens per second reads as instantaneous streaming regardless of which provider generated it. Fireworks tends to edge out Together on smaller and medium models thanks to FireAttention, but the gap is a few dozen tokens per second at most in that range — not something an end user notices.

Speed becomes decisive in two specific scenarios. The first is agent loops, where a single user action can trigger a chain of five, ten, or more sequential model calls (plan, call a tool, interpret the result, plan again). Latency compounds multiplicatively across that chain, so a 200ms-per-call difference becomes seconds of perceived lag by the end of the loop. The second is real-time voice, where the model has to keep pace with natural conversational turn-taking, typically requiring time-to-first-token under 300ms and sustained generation well above 100 tokens per second to avoid audible gaps.

For either of those use cases, it's worth evaluating Groq or Cerebras specifically for the models they support before defaulting to a generalist provider — see the Groq explainer for how its custom chip architecture achieves those numbers, and why that speed comes with a much narrower model selection than Together or Fireworks offer.

How fast is fast enough for a chatbot?

Roughly 100 tokens per second is the practical threshold above which streaming text feels instantaneous to a human reader, since people read at only 4 to 5 tokens per second. Both Together and Fireworks clear that bar comfortably on mid-sized models; the speed race between them only matters for agentic or voice workloads where latency compounds across many chained calls.

Fine-tuning and customization in depth

Fine-tuning is where the two platforms' philosophical differences show up most clearly. Together treats fine-tuning as a first-class, general-purpose capability: LoRA and full fine-tuning across a wide model roster, support for supervised fine-tuning and preference optimization (DPO), and pricing structured explicitly around training-token volume so costs are predictable before you start a run. This fits Together's broader "give developers the full toolkit" positioning.

Fireworks approaches customization from a different angle: distillation as a product. Instead of framing fine-tuning purely as "adapt this model to my data," FireOptimizer treats it as "shrink this expensive, capable model into a cheap, fast one that matches its outputs closely enough for my specific traffic." That is a narrower but often more directly cost-effective goal for teams that already have a large model performing well and just want to cut serving costs by 5x to 10x without losing quality on their specific request distribution.

In practice, a single fine-tuning experiment on a 7B-class model with a modest dataset might cost $10 to $30 in raw training compute on either platform. Production fine-tuning rarely stops at one run, though — teams typically iterate through five to fifteen experimental runs while tuning hyperparameters, adjusting data mixes, and evaluating outputs against a held-out test set, which is the real cost center more than any single training job.

Production features both platforms share

By 2026, the baseline feature set for a credible managed inference provider has converged. Both Together and Fireworks offer:

• An OpenAI-compatible REST API (/v1/chat/completions), so switching providers is often a one-line base-URL change in most SDKs
• Server-sent event (SSE) streaming for token-by-token responses
• JSON mode and structured output enforcement
• Function and tool calling
• Log probabilities (logprobs) for evaluation and confidence scoring
• Observability dashboards for usage, latency, and error tracking
• SOC 2 compliance for enterprise procurement requirements
• Bring-your-own-cloud (BYOC) deployment options for customers who need data to stay inside their own cloud account

Given that baseline parity, the meaningful differentiators really do come down to what's described above: Together's model breadth and fine-tuning depth versus Fireworks' latency engineering and function-calling specialization.

What people get wrong about choosing an inference provider

"The cheapest provider is always the best choice." Sticker price per million tokens is only part of total cost. A slower provider can force you to run more retries, larger batch sizes, or extra caching infrastructure to hit the same user experience, which erases a nominal price advantage. Cached-token discounts, batch discounts, and dedicated-endpoint utilization rates often matter more than the headline rate card.

"Benchmarks from one provider apply to all their models." Throughput varies enormously by model size and architecture. A provider that's fastest on an 8B dense model is not necessarily fastest on a 70B mixture-of-experts model — always check benchmarks for the specific model and size class you plan to deploy, ideally from an independent source rather than only vendor-published numbers.

"You have to pick one provider forever." The OpenAI-compatible API convention that both Together and Fireworks follow makes multi-provider setups genuinely practical. Many production teams route through an abstraction layer — LiteLLM (an open-source library) or OpenRouter (a hosted aggregator) — specifically so they can failover between providers without rewriting application code if one has an outage or a pricing change.

"Fine-tuning always beats prompt engineering." Fine-tuning has real setup and iteration costs, as covered above. For many use cases, a well-engineered prompt with retrieval-augmented context achieves comparable quality at a fraction of the cost and complexity of a fine-tuning pipeline. Reach for fine-tuning when you have a large, high-quality dataset and a narrow, repeatable task — not as a first resort.

Who should pick which provider

Pick Together AI if you're experimenting across many different open models, want a first-party fine-tuning and DPO pipeline with predictable per-token training costs, need distributed training compute in addition to inference, or want to work with a platform that has deep, visible research investment (RedPajama, academic co-releases) behind it.

Pick Fireworks AI if you've already settled on a production architecture and need maximum sustained throughput, if function calling and tool use on open models is core to what you're building, or if your traffic pattern is stable enough that FireOptimizer's automatic distillation can meaningfully cut your serving costs.

Pick Groq if your bottleneck is raw speed on a small, well-supported model list (Llama, Mixtral, Gemma-class models) and you don't need model breadth.

Pick Cerebras if you need the highest available raw token throughput and can work within enterprise or negotiated access rather than pure self-serve.

Pick an aggregator like OpenRouter if you want to avoid single-provider lock-in entirely and are comfortable trading a small routing markup for automatic failover across dozens of providers.

For teams that want to run models entirely on their own hardware rather than through any hosted API — for privacy, cost control at very high volume, or offline use — see our explainer on Ollama, which covers local LLM deployment on personal machines, and our guide to MLX, Apple's machine learning framework for running models efficiently on Apple Silicon.

FAQ

Q: Are Together AI and Fireworks AI OpenAI-compatible? A: Yes. Both expose /v1/chat/completions endpoints following the standard OpenAI request and response schema, so most existing OpenAI SDK integrations work with just a base URL and API key change.

Q: Which is cheaper, Together AI or Fireworks AI? A: Headline per-token pricing is close, typically within 10% for equivalent models. The larger cost differences show up in fine-tuning approach, cached-token discounts (Fireworks offers a 50% discount on cached input), and how efficiently you utilize dedicated GPU capacity rather than in the base serverless rate card.

Q: Do Together AI and Fireworks AI support fine-tuning? A: Both do, but with different emphasis. Together supports LoRA, full fine-tuning, and preference-based training (DPO) across a wide range of base models. Fireworks emphasizes distillation through its FireOptimizer pipeline — training a smaller model to match a larger model's outputs for a specific traffic pattern.

Q: Which provider has better function calling for open-source models? A: Fireworks AI. Its FireFunction-V2 model is purpose-trained specifically for tool use and function calling, and it's widely regarded as one of the strongest options available through a managed API in this category.

Q: Can I run Together AI or Fireworks AI models on my own infrastructure instead of their cloud? A: Both offer dedicated and bring-your-own-cloud (BYOC) deployment options where the model runs on infrastructure you control. For fully self-hosted deployment without a managed provider at all, open-source serving engines and tools like Ollama are the alternative path.

Q: How much did Together AI and Fireworks AI raise in 2026? A: Together AI closed an $800 million Series C in July 2026 at an $8.3 billion valuation. Fireworks AI closed a $1.505 billion Series D that same month at a $17.5 billion post-money valuation, following a $250 million Series C at $4 billion just months earlier.

Q: Why is the same open-source model faster on one provider than another? A: Because the model's weights are identical across providers, but the serving engine wrapped around those weights is not. Differences in attention-kernel optimization (like Fireworks' FireAttention), KV-cache management, batching strategy, and GPU hardware generation all affect real-world throughput even when the underlying model is the same.

Q: Is Groq or Cerebras better than Together AI or Fireworks AI? A: "Better" depends on the tradeoff you're making. Groq and Cerebras post significantly higher raw tokens-per-second on the specific models they support, using custom chips instead of GPUs. But their model catalogs are much smaller than Together's or Fireworks', so they're a better fit when speed on a narrow model list matters more than breadth of model selection.

Q: What is OpenRouter, and is it a substitute for Together AI or Fireworks AI? A: OpenRouter is an aggregator, not an infrastructure operator — it doesn't run its own GPUs. It routes your request to providers like Together, Fireworks, and dozens of others behind a single unified API, handling failover and provider selection automatically. It's a complement to direct provider access more than a substitute, useful specifically to avoid single-provider lock-in.

Q: Do I need to fine-tune a model to get good results, or is prompting enough? A: For most applications, a well-constructed prompt combined with retrieval-augmented context gets you most of the way there at much lower cost and complexity than fine-tuning. Fine-tuning tends to pay off specifically when you have a large, high-quality, task-specific dataset and a narrow, repeatable job the base model doesn't handle well out of the box.

The takeaway

Together AI and Fireworks AI both solve the same core problem — running open-weight models without owning GPUs — but they solved it by hiring for different strengths. Together built breadth and a research-grade fine-tuning pipeline on top of a distributed-compute background; Fireworks built a narrower but deeply optimized serving stack on top of production ML infrastructure experience from Meta's PyTorch team. Both raised multibillion-dollar rounds in mid-2026 because demand for managed open-model inference is growing faster than either company alone can absorb, which is also why so many production teams now route through an abstraction layer rather than betting everything on one vendor. The right choice is rarely "which company is better" in the abstract — it's whether your workload needs breadth and fine-tuning depth, or whether it needs every possible millisecond shaved off the token stream.

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Read next:

• What Is Groq? Ultra-Fast LLM Inference
• What Is Ollama? Run LLMs Locally Explained
• What Is MLX? Apple's ML Framework