There is no evidence-backed universal winner for fastest or cheapest LLM routing in 2026. A useful shortlist is LiteLLM, Portkey (now branded PRISMA AIRS AI Gateway), OpenRouter, Requesty, Kong AI Gateway, Cloudflare AI Gateway, and Helicone—but their routing controls, deployment models, and operational features are not directly interchangeable. Choose by testing your own workload: measure gateway overhead separately from total response time, and compare total cost and task quality under the same conditions.
What “LLM routing” means—and why the distinction matters
LLM routing can refer to two related but different decisions:
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- Deployment or provider routing: send a request to one of several deployments or providers for the same model, perhaps to balance load, reduce latency or cost, or improve resilience.
- Model selection: choose a different model for a request. A learned router, for example, may send simpler requests to a less expensive model and reserve a stronger model for harder ones.
A gateway may offer one capability without offering the other. A provider selector is not automatically a quality-aware model router, and a model-selection policy is not a substitute for provider failover. Confirm which decision a product actually makes before comparing it with another tool.
Seven tools to put on a shortlist
This is a candidate comparison, not a ranked top-seven list. The available June 23, 2026 comparison was published by Requesty, a vendor included here; treat its comparative latency figures as vendor-reported claims, not independent results. Official product descriptions establish broad positioning, but do not provide a common, independently verified latency test across all seven.
| Tool | What the available evidence supports | What to verify in your evaluation |
|---|---|---|
| LiteLLM | Its router documentation describes weighted selection, latency-based and cost-based routing, routing groups, session affinity, and fallbacks. Its pricing page lists open-source self-hosting at $0 and an Enterprise offering priced annually according to capacity, deployment architecture, and support needs. | Check which strategy fits your traffic and whether usage-based routing overhead affects your latency target. Confirm current feature and pricing details directly with LiteLLM. |
| Portkey / PRISMA AIRS AI Gateway | Portkey’s official site says “Portkey is now PRISMA AIRS AI Gateway” and presents a broader offering covering gateway, observability, guardrails, governance, and prompt management. | Confirm current product naming, commercial terms, and which routing controls are available for the deployment you plan to use; current pricing and partner terms were not established in the available material. |
| OpenRouter | Its provider-routing documentation describes controls for selecting providers. It is a managed provider-routing candidate. | Test its provider controls and network path against your requirements rather than assuming they match a self-hosted gateway’s controls or latency. |
| Requesty | Requesty’s official site positions it as an AI gateway and LLM router. It also published the June 2026 comparison that names this shortlist. | Ask how its routing policies map to your use case, and treat any comparative latency figures it publishes as vendor-reported unless independently reproduced. |
| Kong AI Gateway | Kong’s official product page establishes an AI gateway offering and makes it a candidate for teams evaluating an enterprise API platform. | Check current routing behavior and feature details against your requirements; comparable latency evidence was not established. |
| Cloudflare AI Gateway | Cloudflare provides official AI Gateway documentation. It is a candidate for teams already considering Cloudflare’s edge platform. | Measure the actual regions and network path your users will use. The available evidence does not establish it as a generally lowest-latency option. |
| Helicone | Helicone’s official site describes an AI gateway and LLM observability offering. | Determine whether its current routing capabilities meet your needs; the available evidence supports its inclusion for monitoring, but does not establish it as a like-for-like dynamic model router. |
How to compare latency and cost without misleading yourself
Gateway overhead is only one part of the time a user waits. Total end-to-end latency also includes the provider and model response, along with any retries or other work in the request path. A low-overhead gateway can still produce a slow response if the selected provider or model is slow; a routing policy can also trade some gateway time for a different provider, lower cost, or better availability.
Do not turn the Requesty-published June 2026 latency figures into a general ranking: they are vendor-authored comparative claims, and no independent, equivalent 2026 cross-vendor latency benchmark was established in the available evidence. Instead, run a replay or controlled canary with a production-like workload and record the test date.
Keep the test fair
- Use the same request distribution, prompts, model and provider choices, regions, concurrency, and logging, caching, and retry settings when isolating gateway overhead.
- Report p50 and tail latency (p95 and/or p99), not just an average. Record gateway timing separately from provider/model timing.
- For a separate end-to-end comparison, compare complete routing policies and include the policy decision, retries, and cache hits in the record.
- Compare total request cost, not only a model’s advertised token rate. Account for input and output token charges, service fees or markup, retries, and caching.
- Track task success or another consistent quality measure against a fixed-model baseline. Lower cost alone is not a win if answer quality falls.
Capture enough detail to explain the result
For each request, log the provider and model, region, prompt and completion token counts, routing decision, gateway and provider timing, retries, cache hits, cost, and quality score. Record concurrency and policy settings for each run. This makes it possible to tell whether a result came from the gateway, a changed model or provider, or a different request mix.
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RouteLLM illustrates model selection rather than a direct comparison of these seven gateway products. In a 2024 paper, Isaac Ong and coauthors describe preference-data-trained routers that choose between stronger and weaker models. The authors report over 2x cost savings in certain evaluated benchmark cases. That is a bounded result from their evaluated settings—not a production savings guarantee for any gateway, workload, or organization.
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If you test model selection, compare its task quality and total cost with a fixed-model baseline using your own request distribution. Keep this experiment separate from a gateway-overhead test: changing models can alter response time and cost even if the gateway itself adds no measurable difference.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose by operational fit, then verify the policy
Start with the capability you need, rather than a headline latency number. A team that wants control over provider and deployment routing may want to evaluate LiteLLM’s documented strategies. A team comparing managed provider selection can evaluate OpenRouter. Teams considering observability, governance, guardrails, prompt management, or an enterprise API platform should check the relevant product’s current scope and deployment fit—especially Portkey/PRISMA AIRS, Helicone, and Kong. Cloudflare AI Gateway is a natural candidate to test when Cloudflare’s edge platform is already under consideration. These are starting points for evaluation, not endorsements or a performance ranking.
Before committing, confirm data and key custody, available regions, policy controls, fallback and health-check behavior, spend attribution, access controls, and setup and maintenance burden. For any usage-based strategy, measure its effect under your actual concurrency and logging settings. Product features and model prices change, so note the date and configuration of each test and recheck current product documentation and commercial terms before deployment.
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