Fireworks vs Together vs GMI Cloud: LLM Inference Endpoint Provider Comparison

Developers evaluating LLM inference endpoint providers in 2026 tend to focus on price per million tokens. It is a reasonable starting metric, but it misses the variable that most frequently causes teams to switch providers after deploying to production: tail latency. A platform with a $0.10 cheaper per-million rate that triples its TTFT under concurrent load will cost more in user experience damage than the token savings recover.The meaningful comparison between Fireworks AI, Together AI, and official model APIs is not the headline per-token rate but how each platform handles latency stability, custom model deployment, and the specific model families each one actually serves.This piece runs that comparison on real benchmark data.

What "LLM Inference Endpoint" Actually Means Across These Platforms

Three structurally different endpoint types compete in this category:

• Fireworks AI and Together AI: Serverless inference providers for open-source models. They host and optimize popular open-weight models (Llama, DeepSeek, Qwen, Mistral) on their own GPU clusters. You call their endpoints, pay per token, and never provision hardware. Neither platform serves GPT, Claude, or Gemini.
• Official model APIs (OpenAI, Google, Anthropic, DeepSeek): The first-party endpoints from the model developers themselves. These are the only way to access GPT-5.4-nano, Gemini 3.1 Flash-Lite, and DeepSeek-V4-Pro through their official, model-developer-operated infrastructure.
• MaaS aggregators (GMI Cloud): Unified API access to official models from multiple providers under one key and billing structure.

The category overlap is intentional but partial. Fireworks and Together compete directly on open-source model serving. They do not compete with official APIs for the proprietary model market.

The Real Differences Across the Three Platforms

Fireworks AI: latency-first, fine-tuning included

Fireworks was built by former Meta PyTorch engineers and competes primarily on inference speed. The FireAttention custom CUDA kernel is the technical foundation: independent benchmarks show 167-174 tokens per second on DeepSeek V4 Pro, compared to 33-41 tokens per second at comparable providers on the same model.

The P50 TTFT on Llama 3.3 70B is 150ms. P95 is 320ms, giving a P99/P50 ratio of 3.9x. Independent uptime monitoring shows 99.8% availability in Q1 2026, the highest measured among the specialized inference providers. Under 100 concurrent requests, latency degrades only 15% with a 0.1% error rate. These are stability numbers that matter in production.

Pricing is $0.20 per million input tokens for 8B models and $0.90 per million for 70B models. This is 5-15% higher than Together AI on comparable models.

Fine-tuning is a genuine differentiator: Fireworks deploys the trained model to a serverless endpoint at the same per-token rate as the base model.LoRA fine-tuning and full parameter fine-tuning are both supported. The full end-to-end cycle, from dataset submission to production deployment, runs on one platform without a separate hosting step. For teams that need custom model behavior and low latency on that custom model, this is the most complete single-vendor path available.

Dedicated GPU endpoints are available for guaranteed capacity, billed per GPU-second with no minimum commitment, for workloads where shared-infrastructure p99 variance is not acceptable.

Together AI: breadth-first, batch-discounted, fine-tuning mature

Together AI competes on catalog breadth and pricing flexibility. The 200+ model catalog covers more open-source options than Fireworks, and the batch inference API at 50% of standard serverless pricing is the most aggressive batch discount in the category.

The P50 TTFT on Llama 3.3 70B is 220ms. P95 is 450ms. Uptime is 99.7% with a 0.3% error rate. Both metrics trail Fireworks on raw performance. The gap is not large enough to matter for batch processing or async workflows. For synchronous user-facing APIs with sub-300ms p95 requirements, Together's shared infrastructure introduces meaningful variance.

Pricing is 5-15% below Fireworks on most comparable models. Llama 4 Scout runs $0.11 input/$0.34 output per million tokens. Batch API at half those rates makes Together the cheapest option for any workload that can tolerate multi-hour latency on results.

Fine-tuning pricing ranges from $0.48 per million training tokens for LoRA on models up to 16B, to $3.20 per million for full fine-tuning on 70-100B models. Dedicated GPU endpoints cost approximately $2.99/hr for H100 but require 24-48 hours of provisioning time and a minimum 1-hour commitment. For teams iterating on fine-tuned models in development, this provisioning friction matters.

For production batch processing, model catalog breadth, and cost minimization where p95 latency flexibility exists, Together AI is typically the lower-cost path.

The latency comparison that matters

Independent benchmark data from April 2026 on Llama 3.3 70B:

Metric	Together AI	Fireworks AI	Groq
TTFT P50	220ms	150ms	65ms
TTFT P95	450ms	320ms	130ms
Output tokens/sec	95	145	420
End-to-end (500 tokens)	5.8s	3.9s	1.4s
Uptime Q1 2026	99.7%	99.8%	99.4%

Groq leads on speed through custom LPU silicon but is limited to open-source models in its catalog. The speed advantage is real but the model constraint is significant. For teams that need the specific models Groq supports, it is the fastest option. For teams that need the full Together or Fireworks catalog, Groq is not a substitute.

When Official Model APIs Are the Correct Answer

Fireworks AI and Together AI do not serve GPT-5.4-nano, Gemini 3.1 Flash-Lite, or DeepSeek-V4-Pro through official developer endpoints. These models run on the infrastructure of their respective developers, with the quality guarantees, update schedules, and commercial terms that come from the model provider itself.

For teams building on these models, the comparison shifts from Fireworks versus Together to which access path for official models best matches their production requirements:

• GPT-5.4-nano($0.20/$1.25 per million tokens): OpenAI's smallest reasoning model, released March 17, 2026. Designed for high-volume classification, coding subagent workflows, and tasks where reasoning depth outweighs generation speed.
• Gemini 3.1 Flash-Lite($0.10/$0.40 per million tokens): Google's cheapest major API offering as of March 2026. Flat pricing across the full 1M token context window. The lowest input rate from any major provider for a capable model.
• DeepSeek-V4-Pro($1.39 per million input tokens): MIT-licensed open-weight model serving at approximately 55-60 tokens per second on its first-party API. Competes on benchmark performance with frontier closed models at a fraction of their cost.

GMI Cloud provides unified API access to all three under a single key and per-request billing, without regional restrictions or separate provider accounts.For teams building on a mix of official proprietary models and open-source models, this consolidation reduces integration overhead. A single endpoint handles GPT-5.4-nano for reasoning tasks, Gemini Flash-Lite for high-volume classification, and DeepSeek-V4-Pro for complex generation, without three separate billing relationships.

Model documentation is atdocs.gmicloud.aiand the model library is atconsole.gmicloud.ai.

Matching the Platform to the Production Requirement

Production requirement	Recommended platform	Key reason
Low p95 TTFT on open-source model	Fireworks AI	320ms P95 vs 450ms Together; custom kernel optimization
Lowest per-token cost on open-source model	Together AI	5-15% cheaper than Fireworks; 50% batch discount
Broadest open-source catalog	Together AI	200+ models vs Fireworks 20+
Fine-tuned open-source model at scale	Fireworks AI	Same-rate deployment; no dedicated endpoint required
High concurrent open-source inference	Fireworks AI	Best stability under load (0.1% error rate at 100 concurrent)
Official GPT/Gemini/DeepSeek access	GMI Cloud MaaS	Only path to official model endpoints
Multi-model routing across open + official	GMI Cloud MaaS	Single API covers both categories
Maximum raw speed (open-source only)	Groq	LPU hardware; sub-100ms TTFT on supported models
Batch processing at volume	Together AI	Batch API at 50% of serverless rate

The Vendor Is Not the Bottleneck, the Requirement Is

Fireworks AI and Together AI are both credible production platforms. They differ in ways that create real advantages for specific workloads: Fireworks on latency and fine-tuned model serving, Together on catalog breadth, cost, and batch pricing. Neither serves proprietary models.

The production deployment decision becomes straightforward when the model requirement is clear. Open-source models with low p95 TTFT requirements go to Fireworks. Open-source models with batch volume or catalog breadth requirements go to Together. Official models go to their developer's endpoint or an aggregator with clean access.

The mistake is choosing a platform before specifying which model the production system actually needs, because the model availability constraint often resolves the choice before the latency and pricing comparison becomes relevant.