When you’re picking an inference provider for open-weight models, the line between Together AI vs Fireworks AI pricing often decides the build. Both expose OpenAI-compatible endpoints, meter by the token, and compete on the same Llama and Mixtral derivatives, but their cost models and performance envelopes diverge in ways that matter at scale.
Capabilities
Model catalog
Together AI ships a broad catalog: 100+ open-weight checkpoints, including Llama 3, Mixtral, Qwen, and many community fine-tunes. You can spin up a dedicated endpoint for a custom weight or use the shared fleet.
Fireworks AI takes a curated approach. It optimizes a smaller set of models (Llama 3, Mixtral, its own Firefunction series) with a serving stack built for low latency. It also supports custom model deployments, but the default experience pushes you toward its tuned roster.
Inference features
Both support streaming, JSON mode, and function calling on capable models. Fireworks leans hard on speculative decoding to cut time-to-first-token. Together exposes detailed usage objects and supports longer context windows on selected models (up to 32k+ tokens). Neither locks you into a proprietary schema—both mimic the OpenAI request shape.
from openai import OpenAI
together = OpenAI(base_url="https://api.together.xyz/v1", api_key="TOGETHER_KEY")
fw = OpenAI(base_url="https://api.fireworks.ai/inference/v1", api_key="FW_KEY")
# identical payload shape
payload = {
"model": "meta-llama/Llama-3-8b-instruct",
"messages": [{"role": "user", "content": "ping"}],
"stream": False,
}
t_resp = together.chat.completions.create(**payload)
fw_resp = fw.chat.completions.create(**payload)Price/cost model
Per-token structure
The Together AI vs Fireworks AI pricing debate starts with the meter. Both charge per token, separating input (prompt) from output (completion). Output tokens typically cost more because they consume compute sequentially. Neither has minimum commitments on the pay-as-you-go tier.
Fireworks publishes cache-aware discounts: if you reuse a long system prompt across calls, cached input tokens bill at a lower rate. Together applies similar logic on dedicated endpoints but is less aggressive on the shared tier.
Hidden costs
Watch the line items beyond raw tokens:
- Batch APIs: Together offers async batch at a discount; Fireworks has batch via its queue.
- Dedicated capacity: Both charge hourly for reserved GPUs, which can undercut per-token rates at high volume.
- Egress: Standard cloud egress applies if you self-host adjacent services.
A simple cost projection script helps:
def estimate_cost(input_tokens, output_tokens, in_rate, out_rate):
return input_tokens/1e6 * in_rate + output_tokens/1e6 * out_rate
# example rates are illustrative, check live pricing pages
together_cost = estimate_cost(10_000, 2_000, 0.10, 0.20)
fireworks_cost = estimate_cost(10_000, 2_000, 0.08, 0.18)Latency/throughput
Cold starts and autoscaling
On shared tiers, both scale horizontally. Together’s fleet is large; under burst you may see queueing. Fireworks markets sub-100ms p50 time-to-first-token on 8B-class models via speculative decoding, but real numbers depend on region and concurrency.
Throughput optimizations
Fireworks’ serving stack (based on optimized kernels and draft models) sustains higher tokens/sec for small models. Together counters with its own kernel work and supports tensor parallelism for 70B+ models on shared infra. If you need 100+ concurrent streams on a 70B model, Together’s dedicated endpoints are the safer bet.
Ergonomics
API compatibility
Both are drop-in for the OpenAI Python/TS SDK. You swap base_url and api_key. Fireworks adds a response_format shortcut for JSON; Together returns standard usage with prompt_tokens and completion_tokens.
Observability
Together’s dashboard shows per-request token counts and latency histograms. Fireworks provides trace IDs and latency percentiles. Both integrate with LangSmith or Helicone via proxy headers.
Ecosystem
Together AI has deeper roots in the open-source training community—many fine-tunes originate there. Fireworks targets production app builders with enterprise SLAs and a polished playground. If you route through a gateway such as n4n.ai, you get one OpenAI-compatible endpoint that fronts both, with per-token metering and automatic fallback when a provider is rate-limited.
Limits
Rate limits
Shared tiers start at modest RPM (requests per minute) and TPM (tokens per minute) caps, raised on request. Fireworks enforces separate limits per model family; Together pools limits across its catalog.
Context windows
Fireworks supports 128k context on selected Llama 3 variants. Together offers 32k+ on many models, with some 8k defaults. Exceeding the window returns a 400, not a silent truncate.
Head-to-head summary
| Dimension | Together AI | Fireworks AI |
|---|---|---|
| Model catalog | 100+ open weights, custom fine-tunes | Curated optimized set, Firefunction, spec-decode |
| Pricing unit | Per token, input/output split, dedicated discounts | Per token, input/output split, cache-aware input discount |
| Latency | Variable, good at scale | Low p50 via speculative decoding on small models |
| Throughput | High via shared + dedicated GPU | High, kernel-optimized serving |
| Ergonomics | OpenAI-compatible, detailed usage | OpenAI-compatible, JSON/func shortcuts |
| Ecosystem | OSS training community, notebooks | Enterprise SLAs, playground |
| Limits | Pooled RPM/TPM, 32k+ context common | Per-family limits, up to 128k context |
Which to choose
High-volume batch processing
If you run nightly summarization over millions of documents, Together AI’s batch API and broad model choice win. The per-token rate on its shared tier is competitive, and dedicated endpoints drop unit cost below retail when you saturate a GPU.
Low-latency interactive apps
For a chat UX where p50 latency drives retention, Fireworks AI is the stronger default. Speculative decoding on 8B/70B models keeps first token fast, and cache discounts reward stable system prompts.
Multi-model routing
When you need Llama 3 for one task and Mixtral for another without vendor lock, put a gateway in front. A single endpoint that honors routing directives and forwards cache-control hints removes the per-provider glue code. The Together AI vs Fireworks AI pricing gap then becomes a weighted average you can shift by sending traffic to the cheaper provider for each call type.
Custom weights
If you trained a LoRA or full fine-tune and need it served, Together’s upload-and-deploy path is more mature. Fireworks supports custom deploys but expects you to use its optimization pipeline.
Pick based on the dominant constraint: cost-at-scale favors Together; latency-per-interaction favors Fireworks; flexibility favors a routing layer.