Together AI vs Fireworks for Qwen 3 235B inference

When you need to serve Qwen 3 235B in production, the two managed endpoints engineers argue about are Together AI and Fireworks. This post puts Together AI vs Fireworks Qwen 3 235B side by side on the dimensions that actually break deployments: throughput, price structure, API ergonomics, and hard limits.

Capabilities and model alignment

Both platforms host the official Qwen 3 235B weights behind an OpenAI-compatible chat completions route. Neither modifies the checkpoint, but they differ in how they expose sampling, context, and tool use.

Context length and batching

Fireworks documents a 128K token context for Qwen 3 235B on its serverless route. Together’s default serverless window is plan-dependent and typically smaller unless you provision dedicated capacity. If your application packs long system prompts or RAG context, that gap forces architectural changes. Both accept max_tokens up to the remaining window; neither supports sliding window attention beyond the stated limit.

Tool calling and structured output

Qwen 3 235B natively supports function calling via the tools field. Together passes this through with minimal validation. Fireworks adds a constrained decoding layer for JSON schema outputs, which reduces malformed payloads when you strictly need structured responses. In the Together AI vs Fireworks Qwen 3 235B matchup, Fireworks leads on reliable structured extraction.

Price and cost model

Neither vendor publishes a single static price for Qwen 3 235B; both use per-token metering with separate input and output rates. Together AI applies a surcharge for dedicated capacity reservations, whereas Fireworks folds throughput guarantees into tiered enterprise contracts.

The hidden cost is context padding. On both you pay for full prompt tokens even if the model early-stops. Fireworks offers prompt caching on repeated prefixes, which cuts repeat-inference cost dramatically for fixed system prompts. The Together AI vs Fireworks Qwen 3 235B cost difference is mostly about cache hits versus raw rate when you send the same long system prompt at scale.

Latency and throughput

Cold starts

Serverless endpoints both cold-start, but Fireworks keeps warm pools for popular models, yielding lower time-to-first-token (TTFT) under load. Together’s shared pool can spike to multi-second TTFT during region congestion. For bursty traffic, expect Fireworks to be steadier.

Streaming behavior

Both stream SSE chunks compliant with OpenAI’s format. Together occasionally batches tokens under load (larger chunks), which hurts perceived interactivity. Fireworks streams per-token with lower jitter. If you build a typing indicator, Fireworks feels better.

Ergonomics

API compatibility

Both implement /v1/chat/completions with identical auth header Authorization: Bearer <key>. The difference is in client config:

from openai import OpenAI

together = OpenAI(
    base_url="https://api.together.xyz/v1",
    api_key="TOGETHER_KEY"
)

fireworks = OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key="FIREWORKS_KEY"
)

resp = together.chat.completions.create(
    model="Qwen/Qwen3-235B",
    messages=[{"role": "user", "content": "Summarize: ..."}]
)

The model identifier string differs: Together uses HuggingFace repo style (Qwen/Qwen3-235B), Fireworks uses its own slug (accounts/fireworks/models/qwen3-235b). This breaks naive config swaps.

Error shapes

Together returns standard OpenAI error JSON with error.type. Fireworks adds error.code integers mirroring HTTP semantics. Retry logic must handle both. A gateway that normalizes these—like n4n.ai, which honors client routing directives and forwards provider cache-control hints—saves you writing adapter code.

Ecosystem and extras

Together AI bundles a playground and fine-tuning UI for Qwen variants; you can launch a LoRA job on Qwen 3 235B with one API call. Fireworks focuses on inference speed and offers quantized variants (e.g., FP8) of the same weights, which trade marginal accuracy for higher throughput. If you need to customize the model, Together is the only one of the two with managed training.

Observability: Fireworks exposes per-request trace IDs and token-level timing in its dashboard. Together provides usage exports but thinner latency breakdowns.

Limits and quotas

Together enforces per-account RPM soft limits that throttle before hard 429s; raises are manual. Fireworks uses concurrent request limits with autoscaling; you pay overage if you exceed provisioned concurrency.

Both cap single request max_tokens at 8K for Qwen 3 235B regardless of context window. Long generations must be chunked with continuation prompts.

Comparison table

Which to choose

High-volume batch extraction

If you run millions of short structured extractions daily, Fireworks wins on latency and JSON reliability. The 128K context lets you batch multiple documents per call. Use prompt caching to slash cost on static instructions.

Long-context RAG with custom training

When you need to fine-tune Qwen 3 235B on proprietary data and serve it with flexible context, Together AI is the only option here. Accept higher TTFT variance and build retry queues.

Multi-provider resilience

For latency-sensitive production where downtime is unacceptable, front both with a router. A gateway that provides one OpenAI-compatible endpoint across 240+ models and automatic fallback when a provider is rate-limited or degraded lets you shift traffic from Together to Fireworks mid-stream. Set routing directives per request and forward cache-control to benefit from Fireworks’ prefix cache without code changes.

Cost-constrained prototyping

Together’s straightforward serverless pricing is easier to reason about for a prototype. Fireworks’ cache discount only pays off at scale.

Pick based on whether you need training (Together) or speed and context (Fireworks). For most stateless app backends, Fireworks Qwen 3 235B is the better default; for model ownership, Together is mandatory.