Together AI vs Fireworks: open-weight inference (2026)

Side-by-side

Together AI vs Fireworks — at a glance

Dimension	Together AI	Fireworks
Catalogue	200+ open models (Llama, Mistral, Qwen, DeepSeek, etc.)	100+ open models, curated
Llama 3.3 70B price (per 1M tok)	~$0.60-1	~$0.60-1
Function calling	Standard tool-call schema	FireFunction-v2 — purpose-built
Fine-tuning	Yes (LoRA + full)	Yes (LoRA)
Latency (typical)	Competitive on Llama / Mistral	Often fastest on flagship open models
Dedicated endpoints	Yes	Yes
Best for	Broadest open-weight catalogue, fine-tuning, large-batch inference	Function-calling agents, lowest-latency open inference

When to choose which

Pick Together AI or Fireworks?

When to choose Together AI

Choose Together AI when catalogue breadth, fine-tuning, or batch inference matter more than raw latency. Together serves 200+ open models — including most Llama, Mistral, Qwen, and DeepSeek variants — and has invested heavily in LoRA and full fine-tuning workflows. For teams that want to fine-tune Llama 3.3 70B on internal data and serve the result behind an OpenAI-compatible endpoint, Together is the most mature option.

200+ open-weight models (broadest catalogue in 2026)
Mature LoRA and full fine-tuning workflows
Llama 3.3 70B at ~$0.60-1 per 1M tokens
Strong batch inference for large jobs
Dedicated endpoints for predictable latency

When to choose Fireworks

Choose Fireworks when function-calling reliability or latency on hot open models is the priority. Fireworks ships FireFunction-v2, a function-calling model purpose-built around the OpenAI tool-call schema, and has historically led on time-to-first-token for Llama and Mistral flagships. For agent products built on open-weight inference, Fireworks reduces the gap with closed-API tool calling.

FireFunction-v2 — purpose-built for function calling
Often fastest TTFT on Llama 3.3 70B and Mistral
OpenAI-compatible API with native tool-call schema
LoRA fine-tuning with quick deployment
Strong support for DeepSeek and Qwen models

Why not both?

Run Together AI and Fireworks side-by-side

VerticalAPI lets you switch between Together AI and Fireworks per-request through a single OpenAI-compatible endpoint. Same SDK, same API key, zero markup on tokens — you pay Together AI and Fireworks directly with your own keys (BYOK).

from openai import OpenAI
client = OpenAI(base_url="https://api.verticalapi.com/v1", api_key="vapi_...")

# Together AI — broad catalogue, fine-tuning
resp_x = client.chat.completions.create(
    model="together/llama-3.3-70b",
    messages=[{"role": "user", "content": "Classify these support tickets at scale"}],
    extra_headers={"X-Provider-Key": "tg-..."},
)

# Fireworks — FireFunction-v2 for function calling
resp_y = client.chat.completions.create(
    model="fireworks/llama-3.3-70b",
    messages=[{"role": "user", "content": "Call the get_weather tool then summarise"}],
    extra_headers={"X-Provider-Key": "fw-..."},
)

Try VerticalAPI free →

VerticalAPI verdict

Use Together AI when catalogue breadth, fine-tuning, or batch jobs matter most. Use Fireworks when function calling or lowest-latency open-weight inference drive the decision. Through VerticalAPI you can route between both with a single OpenAI-compatible endpoint and BYOK — no SDK migration. In practice, many teams use both: Fireworks for live agents, Together for batch and fine-tuned variants.

Get started — BYOK both providers →

FAQ

Frequently asked questions

Is Together AI cheaper than Fireworks for Llama 3.3 70B?

Roughly the same. Both providers price Llama 3.3 70B in the ~$0.60-1 per 1M tokens range in 2026, with the exact number depending on input/output ratio and current promotions. Differences of 10-20% appear and disappear quarter to quarter. For high-volume workloads, total cost is usually decided by latency, throughput, and fine-tuning fit rather than headline per-token price.

Which is better for function-calling agents?

Fireworks. FireFunction-v2 is a purpose-built function-calling model on top of open weights, with measurable improvements in JSON-schema adherence and parallel tool calling versus generic Llama or Mistral. Together AI supports the standard tool-call schema across its catalogue but does not ship a dedicated function-calling-tuned model in 2026.

Which has better fine-tuning?

Together AI. Together has invested longer in both LoRA and full fine-tuning, supports a wider range of base models, and offers more flexible deployment options for fine-tuned variants. Fireworks supports LoRA fine-tuning with fast deployment but a narrower base-model catalogue. For teams whose differentiation is a fine-tuned Llama, Together is the safer default.

Which is faster on Llama 3.3 70B?

Fireworks has historically led on time-to-first-token for flagship open models, including Llama 3.3 70B and Mistral, by 50-150ms versus Together AI. The gap depends on region and current load. For batch and large-output workloads, throughput matters more than TTFT, and the two providers are typically within 10-20% of each other.

Can I call both Together and Fireworks through one endpoint?

Yes. VerticalAPI exposes a single OpenAI-compatible endpoint at https://api.verticalapi.com/v1. You send the same request shape and change the model parameter (for example, together/llama-3.3-70b or fireworks/llama-3.3-70b) and the matching X-Provider-Key header. There is no markup on tokens; you pay Together AI and Fireworks directly using your own keys (BYOK).

Caveats

Limitations of this comparison

Open-weight inference pricing changes monthly; the ~$0.60-1 figure for Llama 3.3 70B is a mid-2026 mid-market range.
Latency numbers depend on region, time of day, and current load — published benchmarks rarely match real production traffic.
Function-calling quality is hard to compare cleanly: FireFunction-v2 wins JSON-schema adherence on synthetic tests but real-world workloads vary.
Fine-tuning costs (training and serving) sit on top of inference and are not included in headline per-token numbers.
This page covers the two leading providers. Replicate, DeepInfra, Lepton, and Lambdalabs all serve overlapping catalogues with different trade-offs.

Outlook

What may change in 12-24 months

Open-inference pricing for Llama-class models is on a clear downtrend — $0.50 / 1M tokens is plausible by late 2026.
Together is expected to ship a function-calling-tuned variant to close the gap with FireFunction-v2.
Fireworks is expected to expand fine-tuning options to a broader catalogue, including DeepSeek and Qwen.
Provider lock-in will weaken further as OpenAI-compatible gateways (including VerticalAPI) make swapping open-inference providers a one-line change.

Keep reading