Compare Fireworks AI vs Together AI pricing, inference latency, serverless token rates ($0.88 vs $0.90/M for Llama 3.3 70B), dedicated endpoints, and fine-tuning.
Optimized for developer speed, deep integration, and modern architectural workflows in 2026.
Enterprise standard with established market presence and extensive cloud ecosystem tooling.
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: process.env.FIREWORKS_API_KEY,
baseURL: 'https://api.fireworks.ai/inference/v1'
});
const response = await client.chat.completions.create({
model: 'accounts/fireworks/models/llama-v3p3-70b-instruct',
messages: [{ role: 'user', content: 'Extract structured entity data.' }]
});
console.log(response.choices[0].message.content);import Together from 'together-ai';
const together = new Together({ apiKey: process.env.TOGETHER_API_KEY });
const response = await together.chat.completions.create({
model: 'meta-llama/Llama-3.3-70B-Instruct-Turbo',
messages: [{ role: 'user', content: 'Extract structured entity data.' }]
});
console.log(response.choices[0].message.content);Fireworks AI and Together AI are the two premier serverless inference platforms for running open-weights frontier models (Llama 3.3, DeepSeek-V3, Qwen 2.5, Mixtral). Both platforms provide OpenAI-compatible REST APIs, allowing developers to switch inference backends by modifying a single base URL.
Fireworks AI distinguishes itself through extreme systems optimization. By pioneering custom CUDA kernels and multi-LoRA speculative decoding, Fireworks achieves some of the lowest time-to-first-token (TTFT) latencies in the cloud industry, generating over 140 tokens/second on 70B models at $0.90 per million tokens.
Together AI counters with an expansive platform ecosystem. In addition to serverless token inference ($0.88/1M for Llama 3.3 70B), Together AI offers dedicated GPU cluster reservations, serverless fine-tuning pipelines, and the Together Inference Engine (TIE) with sub-second LoRA adapter swapping.
| Platform | Input Rate / 1M | Output Rate / 1M | Inference Speed | Time-to-First-Token (p50) |
|---|---|---|---|---|
| Fireworks AI | $0.90 / 1M | $0.90 / 1M | ~140 tokens / sec | ~110 ms (Ultra-Fast) |
| Together AI | $0.88 / 1M | $0.88 / 1M | ~115 tokens / sec | ~135 ms |
| AWS Bedrock (Llama 70B) | $0.72 / 1M | $0.72 / 1M | ~45 tokens / sec | ~350 ms |
Serverless inference providers competing for developer mindshare frequently highlight Fireworks AI and Together AI. Fireworks AI is built around a custom proprietary CUDA runtime called FireAttention, which optimizes memory bandwidth utilization and multi-query attention kernels to achieve industry-leading Time-to-First-Token (TTFT) and high token generation throughput. Together AI functions as a full-spectrum AI cloud: offering not only low-latency serverless inference, but also dedicated private GPU clusters, turnkey fine-tuning pipelines, and custom LoRA deployments across H100 and H200 accelerators.
| Dimension | Fireworks AI (FireAttention) | Together AI (AI Cloud) | Platform Advantage |
|---|---|---|---|
| Time to First Token (TTFT) | 110 - 180 ms (Ultra-fast kernel) | 180 - 290 ms | Fireworks AI is ~40% faster for conversational starts |
| Llama 3.3 70B Output Price | $0.90 / 1M tokens ($0.90 input) | $0.88 / 1M tokens ($0.88 input) | Virtually identical pricing parity |
| Custom LoRA Multiplexing | Zero-downtime hot LoRA loading | Dedicated endpoints / serverless LoRA | Fireworks excels at serving 100s of LoRAs on 1 base model |
| Fine-Tuning Infrastructure | Lightweight LoRA fine-tuning | Full fine-tuning, DPO, custom data prep | Together AI offers comprehensive training platform |
| Dedicated GPU Reservations | On-demand dedicated deployments | Multi-month H100 cluster contracts | Together AI superior for large enterprise capacity |
Balance incoming requests between Fireworks AI and Together AI to achieve maximum uptime:
import os
from openai import OpenAI
fw_client = OpenAI(base_url="https://api.fireworks.ai/inference/v1", api_key=os.environ["FIREWORKS_API_KEY"])
tg_client = OpenAI(base_url="https://api.together.xyz/v1", api_key=os.environ["TOGETHER_API_KEY"])
def generate_with_fallback(prompt: str) -> str:
try:
# Primary: Fireworks AI for low TTFT
res = fw_client.chat.completions.create(
model="accounts/fireworks/models/llama-v3p3-70b-instruct",
messages=[{"role": "user", "content": prompt}],
timeout=8.0
)
return res.choices[0].message.content
except Exception as e:
# Secondary: Together AI fallback
res = tg_client.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
messages=[{"role": "user", "content": prompt}],
timeout=12.0
)
return res.choices[0].message.contentChoose Fireworks AI when raw conversational latency is paramount: its proprietary FireAttention engine provides the fastest Time-to-First-Token in the open-model ecosystem, making it ideal for voice AI pipelines, real-time customer chatbots, and dynamic LoRA adapter switching. Choose Together AI when you need an all-in-one AI platform capable of handling custom dataset fine-tuning, Direct Preference Optimization (DPO), and long-term dedicated GPU cluster contracts alongside reliable serverless inference endpoints.
When serving hundreds of fine-tuned customer LoRAs on shared GPU infrastructure, memory eviction policies determine throughput stability. Fireworks AI maintains a dedicated LoRA cache in high-bandwidth memory (HBM), swapping adapters on the fly in sub-millisecond intervals without reloading base weights. Together AI provides dedicated endpoints for mission-critical customer adapters. SRE teams should track LoRA cache hit rates: maintaining an 85%+ cache hit ratio prevents disk I/O bottlenecks and eliminates cold-start latency spikes during multi-tenant inference.
| Architecture Dimension | Fireworks AI | Together AI | Resilience Recommendation |
|---|---|---|---|
| Uptime SLA Guarantee | 99.9% (Standard SLA) | 99.95% (Enterprise SLA) | Multi-region active-passive failover |
| Primary Failure Mode | Transient socket drop / cold boot | Rate limit throttling / queue wait | Exponential backoff with randomized jitter |
| Circuit Breaker Trigger | HTTP 502 / Connection Timeout >5s | HTTP 429 Token Bucket Exhaustion | Automated secondary provider failover |
Deploy automated health probes and resilience circuit breakers to protect upstream infrastructure:
# Client configuration tracking inference latency percentiles
client = OpenAI(base_url="https://api.fireworks.ai/inference/v1", api_key=os.environ["FIREWORKS_API_KEY"])
# Track TTFT (Time to First Token) and inter-token latency across streaming responses