Fireworks AI vs DeepInfra: Tuned Premium Stack vs the Price Floor (2026)

Who Wins Per Workload

The verdict is rarely "one is better." It splits by what you are serving and how steadily.

Quick Comparison

Same API shape, different priorities. The table below adds Morph as the coding-agent pick, since neither general host is built for the apply-and-search inner loop.

Pricing: DeepInfra Undercuts on Tokens

DeepInfra is the cheaper serverless option across the board. The gap is largest on mid-size models.

On Llama 3.1 70B, DeepInfra at $0.40 per million is 2.25x cheaper than Fireworks at $0.90. On 8B-class models the spread is even wider. Fireworks offsets some of this with a 50% cached-input discount and a 50% batch-inference discount for high-volume offline jobs.

Cost on a Real Workload

Dedicated GPUs: A 4x Price Gap

This is the most lopsided number on the page. DeepInfra's dedicated GPU pricing is roughly a quarter of Fireworks' on-demand rate.

DeepInfra rents an H100 80GB at $1.79/hr versus Fireworks at $7.00/hr, and a B200 at $2.79/hr versus $10.00/hr. For steady self-hosting of your own fine-tuned model, DeepInfra is the obvious cost choice.

Fireworks bills dedicated capacity per second with auto-scale to zero, which suits bursty traffic where you do not want to pay for idle GPUs. The two pricing models answer different questions: DeepInfra for cheap sustained throughput, Fireworks for spiky workloads that need to scale down between bursts.

Neither is built for the coding-agent apply loop; if applying model-generated code edits is the bottleneck, that is a different tool (Morph Fast Apply, ~10,500 tok/s, with published benchmarks).

Inference Speed: Fireworks' Custom Stack

Fireworks wins on tuned latency. Its inference engine is the differentiator, not the model weights.

Every Fireworks model runs on FireAttention, a production engine with handwritten CUDA attention kernels. Fireworks reports up to 12x faster long-context inference with FireAttention V2 and lower latency than self-hosted vLLM. Speculative decoding is on by default for latency-sensitive deployments, and FireOptimizer tunes that speculation to your own traffic.

DeepInfra runs optimized H100 and A100 clusters with continuous batching and autoscaling, but does not market a proprietary kernel stack. In practice it delivers competitive throughput at a much lower price, while Fireworks targets the lowest tail latency. If you are latency-sensitive, benchmark both on your own prompts before committing.

Fine-Tuning: Fireworks Is the Full Platform

Fireworks is the more complete self-serve training platform. DeepInfra is the cheaper place to serve a model you already trained.

Fireworks runs managed supervised fine-tuning, DPO, and reinforcement fine-tuning for open models up to 1T+ parameters, priced per million training tokens. A LoRA SFT on a 16B to 80B model costs $3.00 per million training tokens. You can then serve the adapter on serverless or dedicated infrastructure.

DeepInfra does not run managed training. Its play is letting you deploy your own custom or fine-tuned model on dedicated GPUs at $0.89 to $2.79/hr. If you already have a trained checkpoint and want the cheapest place to serve it, DeepInfra fits. If you want the training and the serving in one platform, Fireworks does both.

Feature Surface: DeepInfra Hosts More Models

DeepInfra is broader on model count and modality. Fireworks is deeper on a curated, performance-tuned set.

DeepInfra hosts 190+ open-source models spanning text, vision, OCR, embeddings, rerankers, image, video, and speech, and is a supported Hugging Face Inference Provider. Default serverless accounts cap at 200 concurrent requests, raisable from the dashboard.

Fireworks runs a smaller, curated catalog on its own engine, with structured outputs, tool calling, a batch inference API at 50% of serverless cost, and availability through AWS Marketplace. The trade is breadth versus a tuned, compliance-heavy stack.

Compliance & Enterprise: Fireworks Is Ahead

Fireworks has the more complete compliance story. This matters most in regulated industries.

Fireworks is SOC 2 Type II and HIPAA compliant, and markets a broader enterprise suite including GDPR and ISO. For healthcare, fintech, or any deployment that needs a signed BAA and audited controls, that is a hard requirement Fireworks meets out of the box.

DeepInfra positions itself as a low-cost, no-frills serverless host. It does not market the same depth of certifications, so for strict regulated workloads you will want to confirm its current attestations directly with their team before committing.

When to Use Fireworks AI

• Latency-sensitive production. FireAttention kernels plus FireOptimizer-tuned speculative decoding target the lowest tail latency, with up to 12x faster long-context inference reported.
• You need managed fine-tuning. LoRA SFT, DPO, and reinforcement fine-tuning for models up to 1T+ params, priced per million training tokens, then served on the same platform.
• Regulated industries. SOC 2 Type II, HIPAA, GDPR, and ISO coverage make Fireworks the safer pick for healthcare and finance.
• Bursty traffic. Per-second dedicated billing with scale-to-zero means you do not pay for idle GPUs between spikes.
• Offline batch jobs. The batch inference API runs at 50% of serverless pricing for large non-interactive workloads.

When to Use DeepInfra

• Cost is the constraint. Llama 70B at $0.40/M and a dedicated H100 at $1.79/hr undercut Fireworks by 2x to 4x.
• Serving your own fine-tuned model. Deploy a custom checkpoint on dedicated A100/H100/H200/B200 at $0.89 to $2.79/hr.
• You need many models or modalities. 190+ open models across text, vision, embeddings, rerank, image, video, and speech on one API.
• Hugging Face workflows. DeepInfra is a supported Hugging Face Inference Provider, so it slots into existing HF pipelines.
• High-volume embeddings. Embedding models at $0.005 to $0.01 per million tokens for large-scale indexing jobs.

Frequently Asked Questions

Is Fireworks AI or DeepInfra cheaper?

DeepInfra is cheaper on both serverless tokens and dedicated GPUs. It serves Llama 3.1 70B at $0.40 per million tokens versus Fireworks at $0.90, and rents a dedicated H100 80GB at $1.79/hr versus Fireworks at $7.00/hr on-demand. Fireworks costs more because it bundles tuned latency, FireOptimizer, and a broader compliance suite.

What is FireAttention and does it make Fireworks faster?

FireAttention is Fireworks' custom CUDA inference engine with handwritten attention kernels and adaptive speculative decoding. Fireworks reports up to 12x faster long-context inference versus prior approaches and lower latency than self-hosted vLLM. DeepInfra runs optimized H100 and A100 clusters but does not market a custom kernel stack, so Fireworks generally wins on tuned tail latency while DeepInfra wins on price.

Can I run fine-tuned models on Fireworks AI and DeepInfra?

Yes. Fireworks offers managed fine-tuning (LoRA SFT, DPO, and reinforcement fine-tuning) for models up to 1T+ parameters, priced per million training tokens, and serves the result on serverless or dedicated infrastructure. DeepInfra supports deploying your own fine-tuned and custom models on dedicated GPUs billed per hour. Fireworks is the more complete self-serve training platform; DeepInfra is the cheaper place to serve a model you already have.

When does a dedicated GPU beat serverless on Fireworks or DeepInfra?

It depends on sustained volume. On DeepInfra, a dedicated H100 at $1.79/hr costs about $1,289/mo. At $0.40 per million output tokens, that equals roughly 3.2 billion tokens/mo, or about 1,240 sustained output tokens/sec, before dedicated is cheaper than serverless. On Fireworks the $7.00/hr H100 raises the break-even to roughly 2,160 sustained tokens/sec at $0.90/M. Below those rates, serverless wins because you only pay for tokens you generate; above them, a saturated dedicated GPU is cheaper.

Are Fireworks AI and DeepInfra OpenAI-compatible?

Yes. Both expose OpenAI-compatible chat completions, embeddings, streaming, structured outputs, and tool calling, so most clients work by swapping the base URL and API key. DeepInfra hosts 190+ open-source models across text, vision, embeddings, rerank, image, video, and speech, and is a Hugging Face Inference Provider. Fireworks hosts a curated set on its own performance engine and is also available through AWS Marketplace.