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Inference comparison

Fireworks AI vs Together AI

Managed inference API against managed inference api. 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, 2026Together AI product facts checked October 2, 2026Together AI 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

Managed inference API

Together AI

Serverless per-token; dedicated inference per GPU-minute for ready replicas; GPU clusters and provisioned throughput have separate contracts.

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Fireworks AI vs Together AI GPU inference comparison
DimensionFireworks AITogether AI
CategoryManaged inference APIManaged inference API
BillingServerless: model/tier-specific tokens. Reserved Throughput: prepaid dollar-per-minute reservation plus overage. Dedicated: per-GPU-second.Serverless per-token; dedicated inference per GPU-minute for ready replicas; GPU clusters and provisioned throughput have separate contracts.
GPU choiceManaged serving stack plus on-demand GPU-hour SKUsManaged serverless; dedicated deployment profiles with selected hardware; separate GPU clusters for infrastructure control.
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.Dedicated replicas need provisioning/model loading after restart or zero replicas; ready warm replicas continue billing while idle. No universal startup latency is established.
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.Serverless catalog plus supported dedicated models, fine-tunes and uploaded LoRA adapters. Custom deployment profiles must meet Together's supported-runtime requirements.
Best forProduction chat and agent backends that need snappy open-model inferenceServing open-weight models without operating your own GPU fleet
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.Serverless token APIs, dedicated GPU hours and provisioned throughput have different cost models. Each PTU represents reserved capacity; tokens per minute depend on the model and token type. Together announced $800M in Series C funding on July 1, 2026; this does not guarantee endpoint availability or service levels.
GPU choice (1–10)3/103/10
Time-to-serving (editorial) (1–10)9/109/10
Price clarity (1–10)7/107/10
Production ops (1–10)8/108/10
Open-model breadth (1–10)8/10Winner: 9/10

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 Together AI when serving open-weight models without operating your own gpu fleet. 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 Together AI

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

How are Fireworks AI and Together AI billed?
Both bill as serverless: model/tier-specific tokens. reserved throughput: prepaid dollar-per-minute reservation plus overage. dedicated: per-gpu-second.. Cost still depends on GPU class, region, and whether instances idle — verify live rates on each site before budgeting.
Which gives more control over the GPU, Fireworks AI or Together AI?
Fireworks AI: Managed serving stack plus on-demand GPU-hour SKUs. Together AI: Managed serverless; dedicated deployment profiles with selected hardware; separate GPU clusters for infrastructure control.. Both score 3/10 for GPU flexibility in our editorial ratings. 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 Together AI?
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.. Together AI: Dedicated replicas need provisioning/model loading after restart or zero replicas; ready warm replicas continue billing while idle. No universal startup latency is established.. Both score 9/10 for time to first token in our editorial ratings — validate with your model and traffic pattern. 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 Together AI?
Choose Fireworks AI when production chat and agent backends that need snappy open-model inference. Choose Together AI when serving open-weight models without operating your own gpu fleet. 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. Serverless token APIs, dedicated GPU hours and provisioned throughput have different cost models. Each PTU represents reserved capacity; tokens per minute depend on the model and token type. Together announced $800M in Series C funding on July 1, 2026; this does not guarantee endpoint availability or service levels.