Together AI vs Fireworks AI (2026): Speed, Price, and Privacy Compared

Together AI and Fireworks AI are closer than their marketing suggests: both were founded in 2022, both serve the same open-weight models through OpenAI-compatible APIs, and both bill per million tokens. The real decision comes down to 4 axes: speed on your model, price at your volume, retention terms, and fine-tuning needs.

Key takeaways

• Both companies were founded in 2022 and both expose OpenAI-compatible endpoints. Switching between them is one base URL and one API key, so a real A/B test costs you an afternoon, not a sprint.
• Neither has a durable speed advantage. Latency varies by model, quantization, region, and time of day; the only numbers that matter are time-to-first-token and tokens/second measured on your own prompts.
• Both bill per million tokens on serverless tiers and per GPU capacity on dedicated deployments, typically at a fraction of frontier closed-API pricing, but rate cards change monthly, so verify on the providers' pricing pages or DeAI's price tracker before budgeting.
• Privacy at both is a policy commitment, not an architecture. Prompts leave your infrastructure and are processed on their GPUs; retention and training-use terms are self-published statements, not verified facts.
• Fine-tuning is a genuine differentiator: Together AI offers full and LoRA fine-tuning, while Fireworks AI emphasizes LoRA adapters with multi-adapter serving. Check current docs for the exact workflow you need.

Together AI vs Fireworks AI at a glance

The table is deliberately symmetric because the products are. The differences that will actually affect your bill and your p95 latency live one level down.

What each company actually sells

Together AI in one paragraph

Together AI sells hosted inference for open-weight models (the Llama, Qwen, DeepSeek, and Mistral families and a long tail beyond them) through serverless, pay-per-token endpoints, plus dedicated endpoints for teams that need guaranteed capacity. It also sells fine-tuning (both LoRA and full fine-tuning) and raw GPU clusters for teams that want to run their own serving stack. The company markets a research-driven approach to serving optimization and publishes inference research alongside the product.

Fireworks AI in one paragraph

Fireworks AI sells the same core thing: serverless and dedicated serving of open-weight models through an OpenAI-compatible API. The founding team built PyTorch infrastructure at Meta, and the company markets a custom serving stack, rather than off-the-shelf open-source serving, as its latency edge. It offers LoRA-based fine-tuning with multi-adapter serving, structured output, and function-calling support. Like Together, it positions itself as the fast, cheaper alternative to frontier closed APIs.

Which is faster: Together AI or Fireworks AI?

Both providers market speed aggressively, and both publish latency claims. Treat all of those as claims. There is no permanent winner here, for structural reasons:

• Quantization differs. The same model served at FP8 versus BF16 behaves differently on both speed and output quality. Providers don't always disclose which quant a given endpoint runs.
• Load varies. Serverless endpoints are shared infrastructure. A benchmark taken at 10 a.m. on a Tuesday says little about your p95 during a traffic spike.
• Your prompt shape matters. Time-to-first-token scales with input length; throughput matters more for long outputs. A provider can win one and lose the other on the same model.

Third-party trackers such as Artificial Analysis publish cross-provider latency snapshots, which are useful for shortlisting but age quickly as providers retune their stacks. The defensible method is to measure both providers yourself, on the same model, with your production prompts, at your expected concurrency, at three different times of day. Record time-to-first-token, tokens per second, and error rates. Also run a small eval set against both endpoints: silent quantization or serving changes can shift model behavior, not just speed. (DeAI's refusal-index methodology scores exactly this class of provider-side behavior drift against reference model behavior; results are published as they complete.)

How does Together AI pricing compare to Fireworks AI pricing?

Both providers publish per-million-token rate cards tiered by model size, and both bill dedicated deployments by GPU capacity, typically with discounts for committed use. Input and output tokens are usually priced differently, with output tokens costing more. Both sit well below frontier closed-API pricing for comparable open-weight models.

Because rate cards change frequently (providers cut prices and re-tier models regularly), this article deliberately quotes no specific numbers. DeAI had no verified snapshot of either rate card as of 2026-08-20, and any figure printed here would be stale within weeks. Check both pricing pages directly, or the cross-provider tables on DeAI's /prices page, on the day you budget.

What actually determines your bill, beyond the headline rate:

• Output-token share. A chatbot with long answers costs more per request than a classifier with short ones, at the same rate card.
• Context length. Long-context tiers are often priced separately. If your RAG pipeline stuffs 32K-token prompts, price that tier, not the headline one.
• Serverless vs dedicated crossover. At sustained volume, dedicated GPUs can beat per-token pricing; at spiky volume, they can't. Model a month of real traffic both ways.
• Fine-tuning hosting. Training is usually cheap; hosting the resulting adapter or model is the recurring cost. Price the full lifecycle.

Which provider is more private?

Neither is private by architecture. Both are centralized US cloud providers: your prompts leave your machines, cross the internet, and are processed on GPUs they operate. TLS protects traffic in transit; nothing protects prompts from the provider itself except the provider's own policies.

Both companies publish terms covering data retention and whether customer data is used for training, and enterprise tiers at both may include contractual zero-retention commitments. Read the current versions of those documents before you sign anything, and understand what they are: policy statements made by the provider, not independently verified facts and not technical guarantees. If a provider says it doesn't retain prompts, you are trusting the statement, not verifying it.

For workloads where that trust model is unacceptable, such as legal, medical, sensitive code, or regulated data, the honest options are self-hosting open weights on your own hardware or in your own cloud tenancy (vLLM and SGLang are the standard open-source serving stacks), or evaluating providers whose architecture, not just their policy, minimizes data exposure. Neither Together AI nor Fireworks AI is architecturally zero-knowledge, and neither claims to be.

Fine-tuning, dedicated deployments, and the catalog long tail

This is where the two genuinely diverge on paper:

• Fine-tuning depth. Together AI offers both LoRA and full fine-tuning, which matters if you're adapting a model's behavior substantially rather than adding a narrow skill. Fireworks AI focuses on LoRA adapters and markets the ability to serve many adapters efficiently from shared infrastructure, which is attractive if you run per-customer or per-task adapters.
• Dedicated capacity. Both offer dedicated deployments for consistent latency and isolation. Together also rents raw GPU clusters, which suits teams running their own serving stack. Fireworks offers on-demand and reserved dedicated tiers.
• Catalog coverage. Both serve the major open-weight families (Llama, Qwen, DeepSeek, Mistral), but the long tail differs week to week: specific model variants, quantizations, and context-length tiers. If your production workload depends on a specific variant, confirm it's served, on both, before benchmarking anything else.

How to benchmark both in one afternoon

Because both APIs are OpenAI-compatible, the test harness is one client with two configurations:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["PROVIDER_API_KEY"],
    base_url=os.environ["PROVIDER_BASE_URL"],
    # Together AI:  https://api.together.xyz/v1
    # Fireworks AI: https://api.fireworks.ai/inference/v1
)

response = client.chat.completions.create(
    model=os.environ["MODEL_NAME"],  # use the identical model on both
    messages=[{"role": "user", "content": "Summarize the attached contract clause."}],
    stream=True,  # stream so you can measure time-to-first-token
)

curl -X POST "$PROVIDER_BASE_URL/chat/completions" \
  -H "Authorization: Bearer $PROVIDER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "'"$MODEL_NAME"'",
    "messages": [{"role": "user", "content": "Summarize the attached contract clause."}],
    "stream": true
  }'

Run 50–200 real production prompts against each endpoint, at your expected concurrency, at three different times of day. Log TTFT, tokens/second, total latency, and failures. Then multiply the token counts from your logs by each provider's current rate card. That exercise (not any published benchmark, including ours) is the comparison that predicts your production experience.

So which should you choose?

Choose on the axis that's actually binding for you. If you're latency-sensitive on one flagship model, benchmark both and weight TTFT. The winner will be workload-specific and may change next quarter. If you need full fine-tuning or raw GPU clusters, Together AI matches on paper. If your architecture is many LoRA adapters on shared serving, Fireworks AI matches on paper. If privacy is the top axis, neither is the right tool. Self-host.

The meta-answer, though, is that this is the wrong question to agonize over. Both APIs are OpenAI-compatible, both are month-to-month at the serverless tier, and switching costs are near zero. Build behind a thin provider-abstraction layer, run the afternoon benchmark, and keep the loser as your failover. For the wider field beyond these two, including providers with different privacy and decentralization trade-offs, see our guide to the best open-source LLM APIs.

FAQ

How does Together AI pricing work?

Together AI charges per million tokens on serverless endpoints, with rates tiered by model size, and bills dedicated endpoints by GPU capacity. Rate cards change frequently, so confirm current numbers on Together's pricing page before budgeting.

Is Fireworks AI good? A quick Fireworks AI review

Fireworks AI is an inference provider founded in 2022 by former Meta PyTorch engineers. It markets a custom serving stack focused on low latency, offers serverless and dedicated deployments plus LoRA fine-tuning, and exposes an OpenAI-compatible API.

Which is faster, Together AI or Fireworks AI?

There is no durable public answer. Latency depends on the model, quantization, region, and live load, and both providers market speed heavily. Run identical prompts against both endpoints and compare time-to-first-token and tokens per second yourself.

Which is more private, Together AI or Fireworks AI?

Both are centralized US cloud providers where prompts leave your infrastructure. Retention and training-use terms are self-published policy statements, not independently verified facts. For hard confidentiality requirements, self-host open weights.

Can I switch between Together AI and Fireworks AI easily?

Yes. Both expose OpenAI-compatible chat-completions endpoints, so switching is typically a base-URL and API-key change. That makes an afternoon A/B benchmark cheap. Test both before committing to either.