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Inference comparison

Fireworks AI vs Modal

Managed inference API against serverless gpu. Editorial fit scores help shortlist; live pricing stays on the provider sites.

Fireworks AI product facts checked October 2, 2026Fireworks AI editorial scores reviewed September 3, 2026Modal product facts checked October 2, 2026Modal editorial scores reviewed September 3, 2026

Managed inference API

Fireworks AI

Serverless: model/tier-specific tokens. Reserved Throughput: prepaid dollar-per-minute reservation plus overage. Dedicated: per-GPU-second.

vs

Serverless GPU

Modal

Functions: per-second CPU, memory and GPU resources; Shared Endpoints: per-token. Storage and network egress have separate allowances and rates.

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Fireworks AI vs Modal GPU inference comparison
DimensionFireworks AIModal
CategoryManaged inference APIServerless GPU
BillingServerless: model/tier-specific tokens. Reserved Throughput: prepaid dollar-per-minute reservation plus overage. Dedicated: per-GPU-second.Functions: per-second CPU, memory and GPU resources; Shared Endpoints: per-token. Storage and network egress have separate allowances and rates.
GPU choiceManaged serving stack plus on-demand GPU-hour SKUsChoose supported GPU types through the function GPU setting, including T4, L4, A10, L40S, A100, RTX PRO 6000, H100, H200, B200 and B300; allocation and multi-GPU limits vary.
Cold startShared Serverless is documented as warm. A dedicated deployment at zero replicas initiates scale-up on a request but immediately returns 503 DEPLOYMENT_SCALING_UP; requests are not queued. Retry with backoff or keep a minimum replica running.Can be noticeable on cold containers; benchmark the real image and model.
Model accessShared serverless catalog of hosted base models; dedicated deployments support custom model uploads and trained LoRA, with a broader selection. Model availability depends on deployment mode.Package models in images or pull at runtime
Best forProduction chat and agent backends that need snappy open-model inferenceDevelopers who want GPU code as functions with autoscaling
Watch outStandard shared Serverless has no uptime/latency SLA. Reserved Throughput provides a throughput SLA within the purchased dollar-per-minute reservation; unused reservation does not roll over and overage has no SLA. On-demand dedicated rates checked October 2: H100/H200 $8, B200 $13, B300 $15 and GB300 $20 per GPU-hour, billed per second; regional restriction premium 1.5x.You can choose a specific supported GPU type, but containers and scaling remain managed. Warm idle containers consume billable resources. Starter compute credits do not cover Shared Endpoints tokens.
GPU choice (1–10)3/10Winner: 6/10
Time-to-serving (editorial) (1–10)Winner: 9/107/10
Price clarity (1–10)7/107/10
Production ops (1–10)8/108/10
Open-model breadth (1–10)Winner: 8/107/10

Chart

Fit scores on the decision axes

Editorial 1–10 ratings for this pair only — not live pricing or latency benchmarks.

GPU choice

Pick exact GPUs

Time-to-serving (editorial)

Warm-path fit

Price clarity

Easy to forecast

Production ops

Less DIY ops

Open-model breadth

Catalog depth

Scores are editorial planning ratings (1–10) for product shape — not published $/hour quotes or vendor SLAs. Verify current pricing on each provider site.

How to decide

Prefer Fireworks AI when production chat and agent backends that need snappy open-model inference. Prefer Modal when developers who want gpu code as functions with autoscaling. If those statements both feel true, rent a GPU for control and keep a managed API for peak traffic — do not force one product to do both jobs.

Common questions

Fireworks AI vs Modal

Answered from the verified figures on this page rather than general guidance.

How are Fireworks AI and Modal billed?
Fireworks AI uses serverless: model/tier-specific tokens. reserved throughput: prepaid dollar-per-minute reservation plus overage. dedicated: per-gpu-second.; Modal uses functions: per-second cpu, memory and gpu resources; shared endpoints: per-token. storage and network egress have separate allowances and rates.. Marketplace hourly spend tracks GPU uptime; serverless and managed APIs bill for what you invoke — different failure modes if you forget to shut things down.
Which gives more control over the GPU, Fireworks AI or Modal?
Fireworks AI: Managed serving stack plus on-demand GPU-hour SKUs. Modal: Choose supported GPU types through the function GPU setting, including T4, L4, A10, L40S, A100, RTX PRO 6000, H100, H200, B200 and B300; allocation and multi-GPU limits vary.. Modal scores higher for GPU choice (6/10 vs 3/10). Pick a marketplace when you need a specific SKU; pick serverless or managed APIs when you want the platform to handle hardware.
Which has faster cold starts, Fireworks AI or Modal?
Fireworks AI: Shared Serverless is documented as warm. A dedicated deployment at zero replicas initiates scale-up on a request but immediately returns 503 DEPLOYMENT_SCALING_UP; requests are not queued. Retry with backoff or keep a minimum replica running.. Modal: Can be noticeable on cold containers; benchmark the real image and model.. Our editorial ratings favour Fireworks AI for warm API speed (9/10 vs 7/10), but real latency depends on model size, region, and whether endpoints are kept warm. Those 1–10 scores are editorial rankings, not measured milliseconds — record your own TTFT/p95 on a warm endpoint in the target region before you buy on latency.
Should I use Fireworks AI or Modal?
Choose Fireworks AI when production chat and agent backends that need snappy open-model inference. Choose Modal when developers who want gpu code as functions with autoscaling. Watch out: Standard shared Serverless has no uptime/latency SLA. Reserved Throughput provides a throughput SLA within the purchased dollar-per-minute reservation; unused reservation does not roll over and overage has no SLA. On-demand dedicated rates checked October 2: H100/H200 $8, B200 $13, B300 $15 and GB300 $20 per GPU-hour, billed per second; regional restriction premium 1.5x. You can choose a specific supported GPU type, but containers and scaling remain managed. Warm idle containers consume billable resources. Starter compute credits do not cover Shared Endpoints tokens.