Fireworks AI vs Modal (2026): $0.15/M Tokens vs $3.95/hr H100, and Why You Probably Need Both

Who Wins Per Workload

These products live at different layers, so the right pick is set by the workload, not by a single winner. Each row is a real decision a team faces.

Quick Comparison

Fireworks sells tokens, Modal sells GPU seconds. Morph hosts open models too, but tuned for output fidelity and codegen: it serves DeepSeek at 16-bit (bf16) activations instead of the fp8 most serverless providers quantize to, and runs codegen-specific speculative decoding plus custom inference kernels.

What Each One Actually Is

The single most important thing to understand: these are not the same kind of product. One hands you a model, the other hands you a GPU.

Fireworks AI: a managed inference API

Fireworks hosts a catalog of open-weight models (Llama, Qwen, DeepSeek, Kimi, and more) and exposes them through an OpenAI-compatible endpoint. You send a model name and a prompt, you get tokens back, you pay per token. You do not pick the GPU, you do not tune batching, and you cannot run a model that is not in the catalog unless you upload a fine-tune or rent a dedicated deployment.

Modal: serverless GPU compute

Modal hands you a GPU behind a Python decorator. You write a function, annotate it with the GPU type and dependencies, and Modal builds the container, schedules it, autoscales it, and scales it to zero when idle. It runs inference, fine-tuning, batch jobs, and sandboxes. There is no model catalog. You bring the code and the weights.

Pricing: Per Token vs Per Second

The verdict: Fireworks wins on low or bursty volume, Modal wins once you keep a GPU busy. They bill on incompatible units, so the comparison is about your duty cycle.

Fireworks serverless per-token (per model)

Cached input gets a 50% discount and batch inference is billed at 50% of serverless pricing. Fireworks dedicated on-demand GPUs are also available on its own stack: H100 80GB and H200 141GB at $7.00/hr, B200 180GB at $10.00/hr, B300 288GB at $12.00/hr. Fine-tuning runs $0.50/M training tokens (LoRA SFT, up to 16B) to $20.00/M (Full SFT, over 300B); DPO is double the SFT rate at each size.

Modal serverless per-second GPU

Modal bills per second of active compute with no idle charge, plus CPU at $0.0000131/physical core/sec and memory at $0.00000222/GiB/sec. The Starter plan is $0 with $30/month in free credits (100 containers, 10-GPU concurrency). The Team plan is $250/month plus compute, $100/month in credits, 1,000 containers, and 50-GPU concurrency. Enterprise is custom.

Cost on a Real Workload

Rate Limits, Free Credits, Compliance

Fireworks gates monthly budget by spending tier: Tier 1 is $50/month with a valid card, Tier 2 is $500/month after $50 added or spent, then $5,000 and $50,000. It is OpenAI-compatible, SOC 2 Type II and HIPAA compliant, and does not log prompts or generations for open models without explicit opt-in. Modal is pay-as-you-go with no per-token markup, SOC 2 from the Starter tier, with HIPAA compatibility and audit logs on Enterprise.

Cold Starts & Autoscaling

The verdict: Fireworks serverless has no cold start you manage, Modal has one but boots fast and gives you knobs to eliminate it.

On Fireworks serverless, catalog models are already loaded on shared infrastructure, so there is no per-request warmup you control. The tradeoff is that you do not choose the GPU and cannot guarantee tail latency for tight P99 SLOs, because you share capacity with other tenants.

On Modal, scale-to-zero means your container can be cold when a request lands. Modal boots containers in about 1 second on its custom container stack, and functions scale to zero after a default 60-second idle window (scaledown_window, configurable from 2 seconds to 20 minutes). For large models, memory snapshotting captures a container's memory at user-controlled points and reuses it across boots to cut the cold-start penalty. To remove cold starts entirely, set min_containers to keep warm capacity, or use buffer_containers to over-provision during spikes.

Both autoscale on request volume and scale to zero. Modal gives you the knobs (GPU type, concurrency, batching, warm containers) and the responsibility. Fireworks hides them and handles it for you on shared hardware.

Fireworks Niche Features

The verdict: Fireworks is a managed, OpenAI-compatible API tuned for open models, with the billing discounts and fine-tuning paths that production teams want.

• Cached input at 50%. Cached input tokens get a 50% discount. On GPT-OSS 120B that drops input from $0.15 to $0.015 per million; on DeepSeek V4 Pro from $1.74 to $0.145.
• Batch inference at 50%. Batch jobs are billed at 50% of serverless pricing, halving cost for throughput-bound work that does not need real-time responses.
• Fine-tuning. LoRA and full-parameter, SFT and DPO, per million training tokens: $0.50 (LoRA SFT, up to 16B) up to $20.00 (Full SFT, over 300B), DPO double the SFT rate at each size. Fine-tunes deploy behind the same serverless API.
• Dedicated GPUs. On-demand H100 80GB and H200 141GB at $7.00/hr, B200 180GB at $10.00/hr, B300 288GB at $12.00/hr for isolated capacity.
• Embeddings. $0.008/M input tokens for models up to 150M params; Qwen3 8B embeddings at $0.10/M.
• Compliance. SOC 2 Type II and HIPAA, zero data retention on open models without opt-in, TLS 1.2+ in transit and AES-256 at rest.

Modal Niche Features

The verdict: Modal is the developer-experience platform for running arbitrary GPU code, with sandboxes for untrusted execution.

• Python-decorator deploys. The core abstraction is a decorated Python function. No Dockerfiles or Kubernetes to hand-write. Modal builds the image, schedules the GPU, and exposes a web endpoint with one more decorator.
• ~1s container boot. Containers boot in about 1 second on Modal's custom container stack, and functions scale to zero when idle (default 60-second window, configurable from 2 seconds to 20 minutes).
• Memory snapshotting. Captures a container's memory at user-controlled points and reuses it across boots to cut the cold-start penalty for large models.
• Warm capacity controls. min_containers keeps warm replicas to eliminate cold starts; buffer_containers over-provisions during traffic spikes.
• Sandboxes. Programmable, isolated Linux execution environments for running agent-generated or user-submitted code safely, with configurable resource limits and optional GPU attachment.
• Any workload. Inference, fine-tuning, batch jobs, image, audio, and video generation in one code-first model. CPU at $0.0000131/core/sec and memory at $0.00000222/GiB/sec billed per second. SOC 2 from Starter, HIPAA on Enterprise.

When to Use Fireworks AI

• Catalog open model, zero ops. If GPT-OSS, Qwen, DeepSeek, Kimi, or GLM is in the catalog and you just want an endpoint, Fireworks is a one-line OpenAI-compatible swap.
• Bursty or low traffic. Per-token billing means you pay nothing when idle. No GPU to keep warm, no cold start to manage.
• Cache-heavy or batch workloads. Cached input at 50% and batch at 50% of serverless cut cost on repeated prompts and throughput-bound jobs.
• Fine-tune then serve. Train a LoRA or full fine-tune (from $0.50/M training tokens) and deploy it behind the same serverless API without managing infrastructure.
• Regulated workloads. SOC 2 Type II and HIPAA, zero data retention on open models, with dedicated GPUs when you need data isolation.

When to Use Modal

• A model nobody hosts. Custom architecture, a private checkpoint, or a niche open model that is not in any catalog. Modal runs whatever you can put in a container.
• Not just LLMs. Image, audio, and video generation, embeddings, batch ETL, training pipelines. Modal is general GPU compute, not a model API.
• High, steady volume. When you keep a GPU busy, per-second billing beats per-token markup.
• Untrusted code execution. Isolated Linux sandboxes for running agent-generated or user-submitted code safely.
• Full control. You choose the GPU, the batching, and the concurrency, which matters for tight P99 latency targets.

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).

One more split worth knowing if your workload is DeepSeek or codegen specifically. Most serverless providers quantize activations to fp8 to cut cost, which degrades output quality versus the reference weights. Morph serves DeepSeek with full 16-bit (bf16) activations, so output matches the reference, which makes it the best place to run DeepSeek when fidelity matters. For coding agents, Morph runs codegen-specific speculative decoding plus custom low-level inference kernels, so it is the fastest and highest-quality option for code generation. morph-dsv4flash (DeepSeek V4 Flash) is $0.139 per 1M input tokens and $0.278 per 1M output. See Morph Open Source Models and pricing.

Frequently Asked Questions

What is the difference between Fireworks AI and Modal?

Fireworks AI is a per-token model API. You call a catalog model by name and pay per token: GPT-OSS 120B at $0.15 in / $0.60 out per million, DeepSeek V4 Pro at $1.74 / $3.48, Kimi K2.6 at $0.95 / $4.00. Modal is per-second serverless GPU compute. You write a Python function, wrap it in a decorator, and pay for the GPU: H100 at $0.001097/sec (about $3.95/hr), B200 at $0.001736/sec (about $6.25/hr). Fireworks is faster to start if your model is in its catalog. Modal runs any model, any code, and any fine-tuned checkpoint.

Is Fireworks AI or Modal cheaper?

It depends on duty cycle. For bursty or low traffic on a catalog model, Fireworks per-token pricing wins because you pay nothing when idle and keep no GPU warm. For sustained volume that keeps a GPU busy most of the day, Modal per-second billing wins because you saturate the hardware. Worked example: DeepSeek V4 Pro at 50M output tokens/day on Fireworks ($3.48/M output) is about $5,220/month, while one Modal H100 kept busy 24/7 is about $2,846/month. On an expensive model at steady volume, Modal undercuts the per-token bill; on a cheap model or bursty traffic, Fireworks wins.

How fast are cold starts on Modal?

Modal boots containers in about 1 second on its custom container stack. Functions scale to zero when idle, with a default 60-second window (scaledown_window, configurable from 2 seconds to 20 minutes). For large models, memory snapshotting captures a container's memory at user-controlled points and reuses it across boots; min_containers keeps warm capacity and buffer_containers over-provisions during spikes. Fireworks serverless has no cold start you manage, since catalog models stay warm on shared infrastructure.

What are Fireworks AI rate limits and free credits?

Without a payment method, Fireworks allows 10 requests per minute. Adding a card raises the ceiling to 6,000 RPM. Monthly spend is gated by tier: $50 with a valid card, $500 after $50 added or spent, then $5,000 and $50,000. New accounts get $1 in free credits. Cached input tokens get a 50% discount and batch inference is billed at 50% of serverless pricing.

Can I use Fireworks AI and Modal together?

Yes, and many teams do. They solve different problems, so they compose cleanly. Use Fireworks as the per-token endpoint for catalog open models like Kimi, DeepSeek, GLM, or GPT-OSS where you just want fast tokens with zero ops. Use Modal for the workloads Fireworks cannot host: a private fine-tuned checkpoint served on your own vLLM or SGLang engine, non-LLM jobs like image or audio generation, batch ETL, training pipelines, and sandboxes for untrusted code. A common pattern is Modal for custom or off-catalog work and Fireworks for everything that is a plain stock LLM call.