Llama 4 Maverick API pricing: Groq vs Together AI vs Fireworks

Sorting out Llama 4 Maverick API pricing Groq Together Fireworks requires more than scanning per-token rates. The three providers differ sharply in throughput characteristics, context handling, and the hidden costs of rate limits that determine what you actually pay in production.

Capabilities and model access

All three serve Llama 4 Maverick as a weights-hosted inference endpoint, but the surrounding feature set is not uniform.

Context window and modifiers

Groq exposes Maverick with a fixed context length dictated by its LPU memory layout; you get the full pretrained window but cannot extend it with custom RoPE scaling. Together AI allows selecting variant builds (e.g., base vs. instruct) and supports passing temperature, top_p, and repetition penalties unchanged from the reference spec. Fireworks ships Maverick with function-calling adapters and JSON-mode enforcement that are baked into their serving stack, not dependent on client-side prompting.

Fine-tuning and private weights

If you need to serve a LoRA atop Maverick, Together AI is the only one of the three with a first-class fine-tune job API that returns a servable model ID. Groq does not expose customer fine-tunes on Maverick; Fireworks supports custom model uploads but treats Maverick as a base model you must replicate into their registry.

Price and cost model

The headline Llama 4 Maverick API pricing Groq Together Fireworks conversations always start with token counts, but the metering details diverge.

Groq uses a single per-token meter for input and output with no separate charge for cached prefixes; because their architecture favors short bursts, sustained high-volume contracts are negotiated offline rather than published. Together AI applies per-token billing with a documented batch inference discount (typically asynchronous jobs at lower priority) and meters exactly what the OpenAI-compatible usage object reports. Fireworks bills per token but honors provider cache-control hints: if you send cache_control on a system block, repeated calls with the same prefix are cheaper at the token level.

None of the three charges for failed requests (non-200), but only Together surfaces token counts for partial streaming stops in the final usage block. Fireworks rounds usage to the nearest token; Groq truncates fractional tokens.

{
  "model": "meta-llama/llama-4-maverick",
  "usage": {
    "prompt_tokens": 412,
    "completion_tokens": 128,
    "total_tokens": 540
  }
}

Latency and throughput

This is where the hardware story dominates.

Groq’s LPU delivers consistently low time-to-first-token (TTFT) under 100 ms for Maverick at batch size 1, but throughput degrades if you push concurrent requests past their allocated tenant quota. Together AI runs Maverick on GPU clusters (A100/H100 class); TTFT is higher (several hundred ms) but sustains higher aggregate tokens/sec per dollar for long generations. Fireworks uses a mixed fleet with speculative decoding on Maverick, giving mid-range TTFT and strong inter-token latency for streaming chat.

If your workload is synchronous user-facing chat, Groq wins on perceived snappiness. For bulk summarization of 10k-doc corpora, Together’s batch tier is cheaper despite slower start.

Ergonomics and SDKs

All three implement the OpenAI chat completions schema, so a single client can target them by swapping base_url.

from openai import OpenAI

groq = OpenAI(base_url="https://api.groq.com/openai/v1", api_key=GROQ_KEY)
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 = groq.chat.completions.create(
    model="meta-llama/llama-4-maverick",
    messages=[{"role": "user", "content": "Explain Raft consensus."}]
)

Groq’s SDK quirks: it rejects unknown parameters like seed unless explicitly supported. Together passes through extra body fields via extra_body. Fireworks requires response_format for JSON mode; otherwise it ignores the hint.

If you’d rather not maintain three base URLs and fallback logic, a gateway such as n4n.ai presents one OpenAI-compatible endpoint across 240+ models with automatic fallback when a provider is rate-limited, and forwards cache-control hints to Fireworks or Together as applicable.

Ecosystem and limits

Rate limits

Groq enforces per-minute token and request caps that are generous for prototypes but throttle hard on shared tiers. Together AI publishes tier-based limits and lets you request raises via support; its community tier is rate-limited but not capped on total monthly volume. Fireworks uses a credit-wallet model: you preload credits, and 429s appear when the wallet is empty or per-second token rate exceeds plan.

Observability

Together ships request IDs and per-job logs accessible via API. Groq provides minimal headers (x-ratelimit-*) and no request replay. Fireworks includes a tracing dashboard with token-level cost projection.

Head-to-head comparison

Which to choose

Prototyping a latency-sensitive chatbot

Use Groq. The LPU TTFT makes Maverick feel instantaneous in a demo, and the OpenAI-compatible surface means you can swap to another provider later without code changes. Watch the per-minute caps; implement exponential backoff from day one.

Running nightly document extraction over millions of tokens

Use Together AI. The batch discount and fine-tune API let you adapt Maverick to your schema and process asynchronously at lower cost. Latency does not matter when the job runs at 2 a.m.

Shipping a product with repeated system prompts and streaming UI

Use Fireworks. Prompt caching cuts cost on every request that re-sends your long instructions, and speculative decoding keeps tokens flowing smoothly. If you need JSON guarantees, their response_format enforcement is the most reliable of the three.

Multi-provider resilience

If uptime matters more than squeezing the last cent, abstract the three behind a single client. A gateway that honors routing directives and falls back on provider degradation removes the operational tax of monitoring three dashboards. The Llama 4 Maverick API pricing Groq Together Fireworks spread then becomes a weighting factor in your router config, not a codebase fork.

Engineers evaluating these options should benchmark their own traffic shape before committing. The per-token numbers move; the architectural differences in how Groq, Together, and Fireworks serve Maverick do not.