A New Frontier

Cortega is the fastest AI Gateway available in the market.
And it also governs every enterprise AI request.

We ran LiteLLM's own benchmarks and BiFrost's benchmarks. We found that Cortega added less latency per request than LiteLLM, Bifrost, and Portkey. It does this while it enforces policy, protects data, and governs MCP tool calls on the same request.

Most AI Gateways give developers one API across providers. Cortega adds what an enterprise needs around it: policy enforcement, an AI firewall, audit evidence, MCP governance, and one control plane for a fleet of gateways.

See the numbers

Benchmark

Cortega is faster than LiteLLM, Bifrost, and Portkey.

We measured it on AIGatewayBench (the open benchmark LiteLLM uses). We measured on BiFrost benchmark. We ran Cortega through it the same way as other gateways and put our result next to the numbers published for LiteLLM, Portkey, and Bifrost.

Note: LiteLLM has two versions (Rust - in development) and Python (released). We do not include any numbers from the Python version as LiteLLM self acknowledges very high latency and low concurrency on that system. The numbers below are self reported by LiteLLM on their website for their Rust version as its not released yet.

0.33 msLatency Cortega adds per request (p99), on LiteLLM's own benchmark

HalfAbout half the added latency of LiteLLM's Rust gateway

1:1Streaming stays token by token. Cortega adds no buffering.

Gateway	Latency added per request (p99)
Cortega	~0.33 ms
LiteLLM (Rust)	0.66 ms
Portkey	2.29 ms
Bifrost	4.54 ms

Cortega was set up as a fully configured AI Governance Gateway with heavy usage of reports while the test was conducted. Cortega checks policy, protects data and governs tool calls on the same request, and still adds the least latency. The LiteLLM, Portkey, and Bifrost numbers are LiteLLM's own published figures. Cortega's is our run on the same benchmark. This latency test runs on one machine, so the number is the gateway's own cost with no network in the way.

Throughput

One gateway carries more than 50,000 requests per second.

This test runs over a real network. A separate machine sends traffic to the gateway, the way a real client would. We held the upstream model at a fixed delay and raised the request rate until the gateway ran out of room.

Requests per second	Success	Median latency	Added by the gateway
40,000	100%	62 ms	~2 ms
48,000	100%	63 ms	~3 ms
52,000	100%	65 ms	~5 ms
53,000	99.97%	69 ms	out of headroom

One gateway holds more than 50,000 requests per second at a 100% success rate, adding about five milliseconds. Most of the latency you see is the upstream model, held fixed so the gateway's own cost is clear. Cortega runs as a fleet, so capacity grows as you add gateways. The multi-gateway numbers come next.

Read the performance details

Feature check

What you get from LiteLLM Enterprise, and what Cortega adds.

LiteLLM is a strong developer gateway. Some of what an enterprise needs sits behind its paid Enterprise tier. Some of it LiteLLM does not do at all. Here is where each line lands.

Capability	LiteLLM	Cortega
Model access and routing	Open source. One API across providers, keys, budgets, routing, fallbacks.	Included, with policy applied on every route.
SSO and RBAC	Paid. SSO is free up to 5 users, then needs an Enterprise license.	Included. Tied to policy, audit, model access, and tool actions.
Audit logs	Paid. Listed as an Enterprise feature.	Included. Evidence links identity, policy result, data, model, and outcome.
Guardrails	Open source. Set per request, per key, or per team.	A full AI firewall. Block, redact, route, approve, or record before the call proceeds.
MCP tool governance	Partial. MCP support, with credential work on the roadmap.	Full. Servers, tools, permissions, calls, evidence, and policy in one model.
Employee AI and shadow AI	Not offered. LiteLLM governs application and developer API traffic.	Cortega Edge covers ChatGPT, Claude, browsers, and internal assistants.
Where your AI traffic data sits	In its database. Requests and spend are logged to Postgres.	Kept out of the operational database. Governance does not read or store your prompts and responses.
Usage intelligence	Limited. Spend and usage tracking.	What teams use AI for, where quality drifts, and where strategy and real use differ.

Operating cost

Scaling LiteLLM means running a whole stack.

The gateway is the easy part. To run it in production you also run a database, a cache, proxy replicas, and the monitoring around them. Cortega manages the gateways, the policy, and the reporting as one product, so your team assembles less.

Part of the stack	What LiteLLM needs	What Cortega does instead
Proxy replicas	Run and tune multiple instances, load balance, watch latency, handle deploys.	Distributed gateways managed from one control plane.
Postgres	Store keys, users, and logs. Plan HA, backups, upgrades, and growth.	Light Postgres use. Traffic data is not stored there, so reporting does not grow the database.
Redis	Add Redis for cache, rate limits, and budgets across workers.	Policy and gateway state are part of the platform, not parts you wire up.
Reporting	Use the dashboard and APIs, then decide how to export, retain, and explain usage.	A separate reporting layer that can also read AI traffic already on your network.
People	Your team owns uptime, upgrades, alerts, scaling, and security reviews.	One product surface for routing, policy, firewall, evidence, analytics, and gateway management.

Cost of ownership

Most of the cost is the database around it.

There is no single "typical Postgres cost" for LiteLLM. It depends on traffic, log retention, HA, backups, cloud provider, and reporting needs. A real production plan usually adds managed Postgres, Redis, replicas, monitoring, backup testing, and the people to run them. Cortega keeps enforcement, control, and reporting separate, so adding gateways does not mean scaling one shared reporting database in the request path.

Questions for the self-managed path

How long will spend logs and request logs be retained?
Will reporting queries run against the production database?
Do you need read replicas, partitioning, or a separate analytics store?
Who owns database migrations during upgrades?
What is the recovery plan if the database or Redis fails?
How many engineers are on call for the AI gateway?

What Cortega changes

Central management for many gateways.
Policy and AI firewall controls in the request path.
Audit evidence captured with governed traffic.
Usage intelligence for security, finance, compliance, and leadership.
Core for production agents and Edge for employee AI use.
Local, private, and commercial models under one policy model.

Production quality

See how your AI behaves in production.

AI behaves differently in production than in development. Cortega measures how providers, models, agents, prompts, and guardrails perform on real traffic. The same governed traffic that enforces policy also shows quality, latency, cost, provider fit, agent behavior, and drift.

Provider and model evaluation

Compare model behavior, cost, latency, and fit across providers, including local and private models.

Low-latency guardrails

Cortega runs guardrails in the request path with very low added latency, as the benchmark above shows.

Agent production signals

See how agents behave after deployment: tool use, policy hits, cost, quality signals, and unusual activity.

One control plane

Core and Edge share observability, policy, reporting, and enterprise controls, so a new AI surface does not need a separate tool.

References

Sources used for this comparison.

AIGatewayBench is LiteLLM's open benchmark for gateway overhead. Its published figures put LiteLLM Rust at 0.66 ms, Portkey at 2.29 ms, and Bifrost at 4.54 ms of added latency per request (p99). Cortega measured about 0.33 ms on the same benchmark, one connection at a time, with the load generator on the gateway node, so the number reflects the gateway's own processing.

Cortega's throughput figure comes from a separate test over a network using the BiFrost benchmark: a single gateway held more than 50,000 requests per second at a 100% success rate against a fixed-delay upstream. More detail is on the Cortega performance page linked above.

Evaluating LiteLLM for enterprise AI?

Show us the setup you are considering. We will compare the gateway, database, reporting, security, performance, and operating work side by side.

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Cortega is the fastest AI Gateway available in the market.And it also governs every enterprise AI request.