Fireworks AI vs Together AI speed is the question every team asks when they need open-model inference without OpenAI’s lock-in. Both platforms expose OpenAI-compatible endpoints, but their serving stacks produce different latency profiles and throughput ceilings that directly affect product feel and compute spend.
Capabilities
Fireworks AI built its reputation on low-latency serving of open weights (Llama, Mixtral, Qwen) with custom CUDA kernels and speculative decoding. It supports function calling, JSON mode, and LoRA adapters on hosted models. Together AI ships a broader platform: inference, fine-tuning, and training on a large GPU cluster, with the Together Inference API optimized for high batch throughput.
Both cover text generation, embeddings, and vision models. Fireworks leans into real-time responsiveness; Together leans into scale and model variety.
Price and cost model
Neither publishes a single flat rate. Both charge per output token and per input token, with model-specific pricing tiers. Fireworks typically prices small models (7B–13B) cheaply and scales up for 70B+ and MoE. Together uses a similar token-meter but offers asynchronous batch inference at a discount for non-interactive jobs.
If you need predictable cost at scale, compute it from your own traffic mix. A 70B model served at 100 req/s will cost dramatically more than a 7B on either platform.
Latency and throughput
When evaluating Fireworks AI vs Together AI speed, separate two metrics: time-to-first-token (TTFT) and tokens-per-second (TPS) on the generation stream.
Fireworks optimizes TTFT. Its kernel fusion and speculative decoding cut the gap before the first byte arrives, which matters for chat UX. On warm requests for sub-20B models, users routinely see sub-second starts. Throughput per single request is solid but not its headline.
Together AI optimizes for saturated throughput. If you send 1,000 concurrent requests with long prompts, Together’s clustered fleet sustains higher aggregate TPS because it schedules batches across more GPUs efficiently. TTFT can be higher under load, but total job completion time for bulk workloads is competitive.
A practical pattern: use Fireworks for interactive paths, Together for backfill. If you front these providers with a gateway like n4n.ai, you get automatic fallback when a provider is rate-limited or degraded, plus per-token metering across both.
from openai import OpenAI
# Fireworks
fw = OpenAI(base_url="https://api.fireworks.ai/inference/v1", api_key="FW_KEY")
# Together
tg = OpenAI(base_url="https://api.together.xyz/v1", api_key="TG_KEY")
stream = fw.chat.completions.create(
model="accounts/fireworks/models/llama-v3p1-8b-instruct",
messages=[{"role": "user", "content": "ping"}],
stream=True,
)Ergonomics
Both are drop-in with the OpenAI Python/TS SDK. You swap base_url and api_key. Fireworks adds a account/model path convention; Together uses plain model IDs like meta-llama/Llama-3.3-70B-Instruct.
Streaming works identically. Tool calling is supported on both, though Fireworks’ schema enforcement is stricter on smaller models. Together’s dashboard exposes job history and fine-tune tracks; Fireworks’ console focuses on latency graphs and adapter management.
curl https://api.together.xyz/v1/chat/completions \
-H "Authorization: Bearer $TG_KEY" \
-d '{"model":"meta-llama/Llama-3.3-70B-Instruct","messages":[{"role":"user","content":"hi"}]}'Ecosystem
Fireworks has deep integration with LangChain, LlamaIndex, and Vercel AI SDK. Its model zoo is curated; you won’t find every obscure weight, but the hosted ones are tuned.
Together hosts one of the largest open-model catalogs, including many community fine-tunes, and provides the together CLI for training. For teams that also want to fine-tune on the same vendor, Together reduces context switching.
Limits
Fireworks enforces per-key RPM and TPM; large bursts may hit 429s unless you request quota. Context windows follow the underlying model (up to 128K on supported Llama builds). Together imposes similar rate limits but allows higher concurrency on enterprise tiers.
Both suffer occasional GPU contention. Neither guarantees zero cold starts on rarely used adapters.
Head-to-head summary
| Dimension | Fireworks AI | Together AI |
|---|---|---|
| Primary strength | Low TTFT, interactive latency | High batch throughput, model breadth |
| Cost model | Per-token, model-tiered | Per-token, batch discount |
| OpenAI compat | Yes | Yes |
| Max context | Model-dependent (to 128K) | Model-dependent (to 128K) |
| Tool calling | Yes (strict on small models) | Yes |
| Model catalog | Curated, tuned | Very large, includes community |
| Fine-tuning | LoRA adapters | Full FT + training cluster |
| Best for | Real-time chat, latency-sensitive UX | Bulk jobs, large concurrent batches |
Which to choose
Latency-sensitive product (chat, voice, agents): Fireworks AI wins. The Fireworks AI vs Together AI speed gap in TTFT is the difference between a snappy feel and a sluggish one. Use Fireworks for any path where a human is waiting.
Bulk embedding, dataset generation, eval runs: Together AI. Its batch API and throughput orientation cut wall-clock time for thousands of calls. Cost per million tokens drops with async submission.
Mixed workload with one codebase: Put both behind an OpenAI-compatible router. Send interactive traffic to Fireworks, background tasks to Together. If a provider degrades, fail over automatically.
Fine-tune + serve in same place: Together if you need full training; Fireworks if LoRA on a hosted base is enough.
Maximum model choice: Together. If you need a specific community quant or research release, it’s likely there.
Engineers optimizing for Fireworks AI vs Together AI speed should benchmark their own prompts, not trust marketing. Spin up both endpoints, replay production traffic, and measure p50/p99 TTFT and total TPS. The right answer is usually a split routing policy, not a single winner.