Fireworks vs Together AI for DeepSeek R1 inference

Fireworks vs Together AI DeepSeek R1 is the comparison every engineer makes when they need open-weight reasoning inference without running 671B parameters themselves. Both host the official DeepSeek-R1 checkpoint with 128K context and OpenAI-compatible endpoints, but their serving stacks, cost structures, and operational limits differ in ways that directly affect production readiness.

Capabilities

Both platforms serve the full-size DeepSeek R1 (671B MoE, 37B active per token), not the distilled variants. Fireworks exposes it as deepseek-r1 on its serverless tier; Together uses the Hugging Face coordinate deepseek-ai/DeepSeek-R1. Each supports chat completions, streaming, and logprobs.

Fireworks adds structured output via response_format: { type: "json_object" } and respects cache_control markers for prompt caching on supported models. Together offers equivalent JSON mode and maintains a separate cached token discount on its tier. Neither wires native function calling into R1—the model is a reasoning specialist, not a tool caller.

from openai import OpenAI

fw = OpenAI(base_url="https://api.fireworks.ai/inference/v1", api_key="FW_KEY")
resp = fw.chat.completions.create(
    model="deepseek-r1",
    messages=[{"role": "user", "content": "Prove sqrt(2) is irrational."}],
    stream=True,
)

tg = OpenAI(base_url="https://api.together.xyz/v1", api_key="TG_KEY")
resp2 = tg.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1",
    messages=[{"role": "user", "content": "Prove sqrt(2) is irrational."}],
    stream=True,
)

Price and Cost Model

Both providers meter by token with no minimum commitment on serverless. Fireworks publishes a per-million-token rate that scales with instance type; Together does the same but often prices spot/serverless slightly lower for the same model class. If you need predictability, Fireworks sells dedicated deployments billed hourly; Together offers dedicated endpoints with monthly reservations.

Neither charges for prompt caching writes; both discount cached reads (exact percentages shift, so check the dashboard). For bursty workloads, serverless per-token is fine. For sustained >10 req/s, dedicated avoids noisy-neighbor variance.

{
  "fireworks": {"model": "deepseek-r1", "billing": "per_token", "dedicated": "hourly"},
  "together": {"model": "deepseek-ai/DeepSeek-R1", "billing": "per_token", "dedicated": "monthly"}
}

Latency and Throughput

Fireworks runs a custom CUDA kernel stack (derived from TensorRT-LLM patterns) tuned for low time-to-first-token (TTFT) on small batches. Together serves via a vLLM-based fleet with continuous batching, which wins on aggregate tokens/sec when you pack many concurrent requests.

In practice: interactive apps with one user per request see sub-second TTFT more consistently on Fireworks. Bulk evaluation jobs over 10k prompts finish cheaper and faster on Together because its scheduler saturates GPUs better. Both stream tokens; neither blocks on full generation.

Ergonomics

Both are drop-in with the OpenAI SDK (see code above). Fireworks ships a first-party fireworks Python client with typed helpers; Together ships together with similar. Fireworks’ dashboard shows per-request trace timelines; Together shows token accounting and rank.

Fireworks allows passing top_logprobs and echo directly. Together exposes repetition_penalty and temperature with slightly different clamping ranges. Both support max_tokens, but R1’s reasoning traces eat context fast—set max_tokens conservatively.

curl https://api.fireworks.ai/inference/v1/chat/completions \
  -H "Authorization: Bearer $FW_KEY" \
  -d '{"model":"deepseek-r1","messages":[{"role":"user","content":"hi"}],"stream":true}'

Ecosystem and Tooling

Fireworks bundles fine-tuning, model versioning, and a function-calling proxy for non-R1 models. Together provides a dataset hub, fine-tuning jobs, and a research-grade model zoo. If you front either with a gateway such as n4n.ai, you can send one OpenAI-compatible payload and rely on automatic fallback when a provider is rate-limited or degraded, while still honoring each backend’s cache-control hints.

For CI, Together’s CLI uploads eval sets; Fireworks’ CLI pushes prompts to a registry. Both support webhooks for async batch completion.

Limits and Quotas

Default rate limits are account-tiered. Fireworks starts new accounts at modest RPM/TPM and raises after usage history. Together uses a credit-based tier that expands with spend. Both enforce a 128K context window; max completion tokens for R1 is typically 8K–32K depending on the request shape.

Fireworks rejects requests exceeding its max batch size per call; Together returns 429 with retry-after. Neither permits parallel tool calls on R1.

Comparison Table

Which to Choose

Interactive reasoning apps (chat, copilots): Fireworks. Its kernel optimizations keep TTFT low for single-user streams, and the hourly dedicated tier locks that in.

Bulk evaluation or offline distillation: Together AI. The vLLM scheduler squeezes more tokens per dollar when you fire thousands of concurrent requests.

Cost-sensitive prototyping: Either serverless; compare current per-token rates on the pricing page. Together is often a hair cheaper, but Fireworks’ free tier credits may offset.

Multi-provider resilience: Put both behind a routing layer. Using a gateway that honors client routing directives means you can shift traffic without rewriting app code, and automatic fallback covers provider degradation.

Fine-tuning or data pipelines: Together if you want integrated dataset hosting; Fireworks if you already use its registry. R1 itself isn’t fine-tunable on either without custom enterprise agreement—use the base DeepSeek distill for that.

Pick based on traffic shape, not brand. The model weights are identical; the metal around them is not.