Which inference platforms offer predictable latency?

As of November 5, 2026: Fireworks Reserved Throughput (dollars per minute reserved on serverless or on-demand models, with a throughput SLA and 99.9% uptime terms) is the most explicit guarantee for open models. Together AI Provisioned Throughput (PTUs billed per minute) and Together Dedicated Inference (H100 $5.49, B200 $8.99 per GPU-hour, single-tenant) give you capacity no one else shares. For closed models, OpenAI Fast mode (service_tier fast, about 2x the standard rate) gives more consistent latency, but OpenAI says Fast mode for GPT-6 Astra has no latency SLA. Anthropic's Priority Tier is no longer sold to new customers.

Does OpenAI guarantee latency in Fast mode?

Only in some cases. OpenAI's Fast mode docs say Fast mode for GPT-6 Astra does not include a latency SLA; for GPT-5.6 and earlier models, Fast mode and Scale Tier get the same SLA treatment and eligible Enterprise agreements may give service credits when latency targets are missed. Fast mode can also be downgraded to standard speed and price if traffic ramps too fast: above 1 million input tokens per minute, OpenAI advises growing by no more than 50% every 15 minutes.

Is there a priority tier for Claude models?

Not for new customers. Anthropic's service-tiers documentation marks service tiers as legacy and says Priority Tier capacity commitments are no longer available for purchase; existing commitments run until their contract end date, and teams that need guaranteed capacity are told to contact sales. Claude Opus 5.5 does offer a fast mode at $8/$40 per million tokens, which raises speed but is not a capacity reservation.

Why is my LLM latency unpredictable on serverless APIs?

Shared serverless capacity is best-effort: at peak, requests queue behind other customers' traffic, so a fast median hides a slow p95. Reasoning models add a second source of variance, since hidden thinking tokens vary per prompt. The fixes are a priority mode for burst traffic, reserved throughput or dedicated GPUs for steady traffic, and capped reasoning effort for interactive calls.

Quick Answer

Inference Platforms With Predictable Latency (2026 Guide)

Published: November 5, 2026

The short answer

Predictable latency comes from reserved capacity, not from a faster model. On shared serverless APIs your request waits behind everyone else’s at peak. There are three ways to stop that, in rising order of cost and commitment. As of November 5, 2026:

Priority modes — pay a per-token premium to jump the shared queue. OpenAI Fast mode, Fireworks Priority.
Reserved throughput — reserve capacity by the minute on a provider’s serving stack. Fireworks Reserved Throughput, Together Provisioned Throughput (PTUs).
Dedicated GPUs — single-tenant hardware running only your traffic. Together Dedicated Inference, Fireworks on-demand deployments.

This is the predictability spoke of our low-latency LLM inference providers guide, which ranks providers on raw speed.

The comparison

Option	What you buy	Price (Nov 5, 2026)	Written guarantee	Best for
OpenAI Fast mode	Priority processing on shared capacity, `service_tier: "fast"`	~2x Standard (GPT-5.6 Sol $8/$40 short context)	No latency SLA for GPT-6 Astra; GPT-5.6 and earlier: SLA as Scale Tier on eligible Enterprise contracts	User-facing apps on OpenAI models
OpenAI Scale Tier	Purchased TPM bundles	By contract	Contractual	Steady high-volume OpenAI traffic
Fireworks Priority	Per-request priority at peak	Kimi K3 $3.75/$18.75 vs $3/$15 Standard	None stated	Burst traffic on open models
Fireworks Reserved Throughput	Dollars per minute reserved, serverless rates	Serverless rates, committed	Throughput SLA, 99.9% uptime terms	Steady production traffic, open models
Together Provisioned Throughput	PTUs, each a fixed capacity	Per PTU per minute	Fixed capacity per PTU	Predictable tokens-per-minute
Together Dedicated Inference	Single-tenant GPUs	H100 $5.49 · B200 $8.99 per GPU-hour	”Guaranteed performance (no sharing)“	Custom or fine-tuned models
Anthropic Priority Tier	Capacity commitment	Not sold to new customers	Existing contracts only	—

Prices and terms from each vendor’s pricing page or docs on November 5, 2026. Model prices: see current API prices.

Priority modes: cheapest, weakest guarantee

OpenAI Fast mode is what OpenAI called Priority processing until July 30, 2026. Set service_tier to fast (or priority) per request, or make it the project default in Settings. OpenAI says it is up to 2.5x faster for gpt-5.6-sol and gives more consistent latency. Three things limit how predictable it is:

No SLA on the newest flagship. OpenAI’s docs say Fast mode for GPT-6 Astra has no latency SLA.
Ramp-rate downgrades. Ramp too fast and some requests run at standard speed and price; the response then says service_tier: "default". Above 1 million input tokens per minute, grow by at most 50% every 15 minutes, and do not run ETL or batch jobs in Fast mode.
Shared rate limits. Fast mode and Standard share one rate limit per model, and Fast mode does not draw on Scale Tier bundles.

For more speed rather than more consistency, OpenAI’s Ultrafast mode runs GPT-6 Astra and GPT-6.1 Sol at about 6x the standard rate.

Fireworks Priority is a per-request mode on the same endpoint as Standard, Fast (100+ tokens a second), Batch (50% off) and Spot (30% off, throughput can drop to zero). Priority costs about 25% more on Kimi K3. It is the cheapest way to protect a latency-sensitive slice of traffic.

Reserved throughput: the sweet spot

Fireworks Reserved Throughput reserves dollars per minute at serverless rates and comes with a throughput SLA and 99.9% uptime terms. It adds to your adaptive rate limits, and traffic above the reservation bills at standard serverless rates, “never a penalty rate”. That makes it the clearest written latency-and-capacity guarantee for open models.

Together Provisioned Throughput sells PTUs. Each PTU is a fixed capacity; how many tokens per minute it delivers depends on the model and on input, cached and output tokens. Together’s calculator assumes continuous 24/7 provisioning (about 43,800 minutes a month), so price it against your steady load, not your peak.

Dedicated GPUs: full isolation

Together Dedicated Inference runs single-tenant instances at $5.49 per H100-hour and $8.99 per B200-hour on demand, with custom models and autoscaling. Nothing else shares the GPUs, so latency depends only on your own load and batch size. This is the step when you serve a fine-tuned model or need p99 control you can tune yourself. Compare providers in best dedicated inference endpoint providers.

Claude: no priority tier for new customers

Anthropic’s service-tiers documentation now marks tiers as legacy: Priority Tier capacity commitments are no longer available for purchase, and existing commitments run to contract end. For guaranteed capacity Anthropic says to contact sales. Claude Opus 5.5’s fast mode ($8/$40 per million tokens, Claude API only) adds speed, not reserved capacity.

How to choose

Bursty, user-facing traffic on closed models: OpenAI Fast mode, with a ramp plan.
Steady traffic on open models: Fireworks Reserved Throughput or Together PTUs.
Custom model or strict p99: dedicated GPUs.
Whatever you pick: measure p95 time to first token at your peak hour, and cap reasoning effort on interactive calls; see how to reduce LLM latency.

Last verified: November 5, 2026.