Fireworks AI vs Groq: which LLM inference API should you use?
Use Fireworks AI if you need flexible model selection and fine-tuning capabilities. Use Groq if you prioritize absolute lowest latency and have models that run on their hardware.
VERDICT
Side-by-side comparison
| Feature | Fireworks AI | Groq | Winner |
|---|---|---|---|
| Time to First Token (7B model) | 40–80ms | 10–20ms | Groq |
| Model Catalog | 100+ open-source models (Llama, Mistral, Qwen, etc.) | 15–20 optimized models (Llama, Mixtral, Gemma) | Fireworks AI |
| Fine-tuning Support | Yes, via API | No | Fireworks AI |
| Per-token Cost (input) | $0.20–0.40 per 1M tokens | $0.15–0.25 per 1M tokens | Groq |
| Batch Processing | Yes, with priority queuing | Limited (real-time focus) | Fireworks AI |
| Hardware Flexibility | GPU-agnostic (served on A100s, H100s) | Proprietary LPU-only | Fireworks AI |
| API Compatibility | OpenAI-compatible (/v1/chat/completions) | OpenAI-compatible (/v1/chat/completions) | Tie |
| Uptime SLA | 99.5% | 99.9% | Groq |
Performance benchmarks
Time to First Token (7B model, 256 input tokens)
Groq's LPU eliminates memory bandwidth bottlenecks; Fireworks uses commodity GPUs. Groq ~3–4x faster but with fewer model options.
Throughput (tokens/sec, 7B model)
Fireworks achieves comparable throughput via continuous batching; Groq prioritizes latency over batch efficiency.
Input Token Cost (per 1M tokens)
Groq undercuts Fireworks by 15–30% on pricing, but limited model choice may force higher-cost alternatives.
P95 Latency (full response, 500 output tokens)
Groq maintains low latency across percentiles; Fireworks has more variance due to GPU batching.
When to use each
- ✓ You need to fine-tune models on proprietary data: Fireworks AI's fine-tuning API lets you customize models without managing infrastructure.
- ✓ You need broad model selection and want to switch models without vendor lock-in: Fireworks supports 100+ open-source models including Llama, Mistral, Qwen, and custom community models.
- ✓ You require batch processing for non-real-time workloads: Fireworks offers batch APIs with priority queuing, ideal for log analysis, summarization jobs, or off-peak inference.
- ✓ You want flexible GPU provisioning: Fireworks scales across different GPU types (A100, H100, L40S) depending on your workload.
- ✓ You're building with multiple model types simultaneously: Fireworks dashboard makes it easy to run vision, code, and text models from one API key.
- ✓ You need absolute lowest latency for real-time user-facing applications: Groq's 10–20ms TTFT is unmatched and critical for chat, search, or autocomplete.
- ✓ You're latency-sensitive and your model is on Groq's optimized list (Llama 2/3, Mixtral, Gemma): avoiding a model switch saves significant engineering.
- ✓ You have high concurrent traffic and can absorb the per-token cost premium: Groq's LPU hardware excels under load with minimal inference time variability.
- ✓ You're building production AI agents requiring sub-200ms response times: agents make multiple LLM calls; Groq's speed compounds the benefit.
- ✓ You prioritize infrastructure stability and uptime: Groq's 99.9% SLA and proprietary hardware stack offer predictable performance.
Common misconceptions
Fireworks AI
Fireworks AI is only for startups or experiments: it's not suitable for production scale.
Fireworks powers production inference at scale with 99.5% uptime SLA, request queuing, and enterprise support. Many Series A+ startups and mid-market use Fireworks in production for millions of daily requests.
Fine-tuning on Fireworks requires months of training time and expensive compute.
Fireworks fine-tuning uses LoRA and QLoRA by default, requiring only hours of training on small datasets (10k–100k examples) at a fraction of full model training cost. Results are live in days, not weeks.
Fireworks latency is too high for real-time chat applications.
Fireworks' 40–80ms TTFT is acceptable for most chat UX (human perception is ~200ms); only highly interactive apps (search autocomplete, real-time code completion) critically need Groq's speed.
Groq
Groq can run any open-source LLM as fast as it runs Llama.
Groq optimizes for a curated list of models (Llama, Mixtral, Gemma, etc.). Other models may run, but not all achieve the published sub-20ms latency. Custom or niche models should be tested before assuming speed parity.
Groq is cheaper than Fireworks because per-token pricing is lower.
Groq's lower per-token cost is offset by limited model selection. If your use case needs a model not optimized on Groq's hardware, you may need to use Fireworks anyway, making comparison moot.
Groq's LPU hardware is the future and will eventually replace GPUs.
Groq is specialized for inference-only workloads with fixed batch sizes. It excels at latency but can't do training, fine-tuning, or vision in the same way commodity GPUs can. Both technologies coexist; Groq isn't a replacement for all GPU work.
Code examples
Task: Send a chat prompt to an LLM API and stream the response back.
import os
from openai import OpenAI
# Fireworks API uses OpenAI SDK: just change base_url
client = OpenAI(
api_key=os.environ.get("FIREWORKS_API_KEY"),
base_url="https://api.fireworks.ai/inference/v1"
)
# Access 100+ models: llama-v2-70b, mistral-7b, qwen-72b, etc.
response = client.chat.completions.create(
model="accounts/fireworks/models/llama-v2-70b-chat", # Flexible model selection
messages=[{"role": "user", "content": "Explain LLMs in 3 sentences."}],
stream=True,
temperature=0.7
)
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")Fireworks reuses the OpenAI SDK by swapping base_url, making it trivial to switch models without code changes: the key differentiator that enables flexibility.
import os
from openai import OpenAI
# Groq also uses OpenAI SDK: same interface, different endpoint
client = OpenAI(
api_key=os.environ.get("GROQ_API_KEY"),
base_url="https://api.groq.com/openai/v1"
)
# Limited to Groq-optimized models: llama-3.1-70b, mixtral-8x7b, gemma-7b
response = client.chat.completions.create(
model="llama-3.1-70b-versatile", # Only models optimized for LPU hardware
messages=[{"role": "user", "content": "Explain LLMs in 3 sentences."}],
stream=True,
temperature=0.7
)
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")Groq's OpenAI-compatible API is identical to Fireworks, but model selection is constrained to LPU-optimized variants: the tradeoff for sub-20ms latency.
Migration path
- Both Fireworks AI and Groq expose OpenAI-compatible APIs, so switching is straightforward:
- Install: Both use the same `openai` SDK: no new dependencies.
- Swap API keys and base_url: FROM: base_url="https://api.fireworks.ai/inference/v1", api_key=FIREWORKS_API_KEY TO: base_url="https://api.groq.com/openai/v1", api_key=GROQ_API_KEY
- Update model name to Groq-optimized equivalent: FROM: model="accounts/fireworks/models/llama-v2-70b-chat" TO: model="llama-3.1-70b-versatile" Map Fireworks models → Groq equivalents: llama-v2-70b → llama-3.1-70b, mistral-7b → mixtral-8x7b
- If you use Fireworks batch API, note Groq has no batch endpoint: rewrite as streaming or request Groq prioritize batch support.
- Test latency on your actual workload: Groq wins on TTFT, but if you don't need <50ms, the extra cost may not justify the switch. Reverse migration (Groq → Fireworks) is identical: just swap keys and add back model flexibility.
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