LiteLLM Alternatives for Enterprise Teams: The Complete Evaluation Guide
TL;DR: LiteLLM’s ceiling is architectural: across regions, it forces a choice between one shared PostgreSQL database or separate workers with separate state. The alternatives that matter separate the management plane from the data plane so policy is set once and enforced everywhere, including on agent tool calls via MCP. Migration changes each app’s base URL and key, teams move one at a time, and a bridge covers teams that cannot change apps yet. Evaluation criteria: cross-region governance, agent tool-call governance, deployment flexibility for data residency, and flat pricing.
Across regions, LiteLLM makes platform teams choose between one shared PostgreSQL database and separate workers that each hold their own state. Every alternative below is evaluated against that constraint. Gartner forecasts AI gateway spending will grow 70.9% in 2027, from $251 million to $429 million. This guide covers why LiteLLM stalls at enterprise scale, which categories of alternatives exist, which criteria matter, and how to migrate one app at a time.
Overcoming operational bottlenecks in LiteLLM
LiteLLM’s single-worker design shows its limits in three places: cross-region coupling, per-worker policy changes, and ungoverned agent tool calls.
Cross-region proxy limits in LiteLLM
LiteLLM runs one shared PostgreSQL database across every region, or separate workers that each keep their own keys, teams, and budgets, so every policy change is made once per worker.
A management plane/data plane architecture removes that coupling: the admin sets rules once, and each data plane runs on its own local copy.
Why LiteLLM stalls at enterprise scale
LiteLLM’s own troubleshooting documentation and community reports describe the Python proxy slowing above roughly 300 requests per second, with containers consuming 12 GB of memory before crashing.
These figures come from vendor documentation and community reports, not independent benchmarks. These reports point to the same constraint: the proxy’s concurrency limits sit in the Python process, not in the gateway layer.
Avoiding manual sync for LiteLLM workers
LiteLLM’s Global Control Plane, which requires a LiteLLM Enterprise license, is a federated UI: one screen that opens separate workers, each with its own keys, budgets, and teams. A change across three regions is made three times. Blocking a model everywhere means changing every gateway one by one.
A team can build self-hosted sync to push configuration across workers, which adds a permanent maintenance obligation.
Securing agent actions and tool calls
LiteLLM governs model calls, including tool calls made through its unified OpenAI-compatible API. LiteLLM’s governance stops at which model or endpoint an application can reach, and it does not extend to which tools an agent called via MCP or what data it touched. Tetrate’s MCP Gateway and guardrails documentation covers the curated tool catalog, per-profile authentication, and step-up authorization that address this gap.
Enterprise criteria for replacing LiteLLM proxies
The enterprise LLM gateway alternatives fall into four categories: open source proxies, self-hosted sync on top of LiteLLM, managed gateways, and enterprise gateways with management plane/data plane separation.
Evaluating open-source proxy options
Open source proxies can give a platform team transparency, less vendor lock-in, and community support. The Agent Router project (formerly Envoy AI Gateway) at the Agentic AI Foundation (AAIF) runs on the Envoy proxy and reached v1.0 in June 2026, co-created with Bloomberg. Bifrost is another open source option.
The open source Agent Router project ships without fleet management, dashboards, cost controls, or support. Agent Router Enterprise adds fleet management, dashboards, cost controls, and support.
Deploying private AI infrastructure
Private AI infrastructure means the data plane runs in the customer’s own cloud, on-premises, or at the edge, so prompts and responses stay inside the customer’s network. For regulated industries (financial services, insurance, healthcare/pharma), data residency and sovereignty mandates can make this a requirement.
AWS offers Bedrock exclusively as a managed cloud service with no private or on-premises deployment option.
Policy enforcement via managed gateways
A managed gateway pairs a vendor-hosted management plane with customer-controlled data planes. Tetrate hosts the management plane for the admin API and UI, and the customer runs the data plane where traffic flows. Each data plane opens one outbound HTTPS connection to the management plane, so the customer opens no inbound firewall rules. If the data plane runs in the customer’s cloud account, prompts and responses stay inside the customer’s network.
Selecting the right enterprise LLM gateway
Feature checklists hide differences that appear after rollout, such as who changes a policy in each region, where prompts are stored, and whether tool calls are logged.
Cross-cloud and multi-region governance
The core question: is a policy change made once, or once per region? A management plane/data plane architecture sets rules once and distributes them to every data plane, each enforcing them independently on a local copy.
LiteLLM’s federated model requires per-worker changes. Cloud provider gateways like AWS Bedrock and Azure API Management are built for single-cloud architectures and do not provide a policy layer across clouds, on-premises, or private models. Tetrate Agent Router Enterprise sets rules once in the management plane, and each data plane enforces them independently.
Data residency and sovereignty controls
Agent Router Enterprise lets each customer choose what data the gateway captures: the full request, only counts and cost, or nothing at all, changeable at any time. Customers can run the gateway inside their own cloud account so prompts stay inside their network.
Securing tool calls via MCP gateways
MCP (Model Context Protocol) connects agents to tools. A gateway that governs MCP traffic can audit which tools an agent called, in addition to which model it used. Agent Router Enterprise provides an MCP Gateway with a curated tool catalog, per-profile authentication and filtering, and step-up authorization built with Ory. Routine agent actions proceed automatically, while higher-risk actions trigger human approval through the customer’s own identity system.
LiteLLM governs model calls. The Tetrate AI risk governance page covers the policy surface for agent actions.
Evaluating gateway latency at scale
The independent Broadcom/VMware test of the Agent Router project (then named Envoy AI Gateway) measured about 2 ms of gateway overhead per request, about 0.01% of end-to-end latency, under real GPU inference on four NVIDIA H100 GPUs. The test used 124 GB of data with over 20,000 unique sessions and a three-hour endurance run at 190 concurrent users, reaching saturation at 224 concurrent users where the GPUs hit their limit first. Broadcom published the full results as “Gateway to Enterprise AI” in June 2026.
Flat rate licensing for AI infrastructure
OpenRouter takes a share of inference spend, tying vendor revenue to your usage.
Tetrate licenses the control layer with flat pricing by feature set, deployment footprint, and support tier, with no per-token fee. Detailed quotes require an NDA and are available through a demo request.
Fitting AI gateways into existing stacks
Agent Router Enterprise provides SSO login, scoped access profiles, and one API key covering every approved model through a single endpoint. Applications can use short-lived tokens from the customer’s identity provider (such as Okta or Microsoft Entra ID), so the customer has no Tetrate keys to manage for those applications.
Comparison table: LiteLLM vs. enterprise gateway architecture
| Criterion | LiteLLM | Agent Router Enterprise |
|---|---|---|
| Cross-region governance | Shared PostgreSQL or separate workers per region | Rules set once, each data plane runs on a local copy |
| Management plane/data plane separation | No: federated workers, each with own keys and budgets | Yes: one management plane, distributed data planes |
| Agent tool-call governance | Model calls and tool calls through its unified API | MCP Gateway with curated tool catalog and step-up authorization |
| Deployment flexibility | Self-hosted | Managed, self-hosted cloud, on-premises, edge, per region |
| Pricing model | Enterprise license for Global Control Plane | Flat by feature set, footprint, and support tier |
| Migration path | N/A (incumbent) | Base URL and key swap per app, no rewrites |
Choosing an AI gateway: hybrid vs managed models
The deployment model determines where prompts and data physically live, who operates the management plane, and what happens during an outage.
Architecture for multi-region AI traffic
Agent Router Enterprise separates the management plane from the data plane. The management plane hosts the admin API and UI. Each data plane carries live AI traffic on a local copy of the rules, and the management plane is not in the data path.
During a management plane outage, AI traffic keeps running because each data plane enforces its local copy. Rule changes pause until the management plane is back, and each data plane picks up changes at its next successful check-in. A CNCF session on the Agent Router project covers the underlying architecture.
Single cloud gateway deployment limits
AWS Bedrock and Azure API Management can fit an organization that has committed to one cloud for its AI architecture. Both are scoped to that cloud, so cross-cloud failover, on-premises data residency, and governance for private models running outside that cloud require other tooling.
Hybrid models for data residency
Hybrid deployment means Tetrate runs the management plane and the customer runs the data plane in their own cloud, on-premises, or at the edge. Each data plane opens one outbound HTTPS connection to the management plane. Nothing connects in, so the customer opens no inbound firewall rules.
Configuration flows down to each data plane, telemetry and usage numbers flow up, and the customer chooses whether prompts and responses also go up, for example in request logs. A Tetrate product session walks through the hybrid deployment model.
Decision matrix for platform leaders
| Organizational need | Open source proxy | Self-hosted sync | Managed or enterprise gateway |
|---|---|---|---|
| Multi-cloud governance | Build it yourself | Sync code across workers | One management plane, distributed data planes |
| Data residency | Self-host everywhere | Self-host everywhere | Data plane in customer’s network |
| Agent tool-call governance | MCP server aggregation with tool filtering | Model calls and tool calls through its unified API, per worker | MCP Gateway with step-up authorization |
| Flat pricing | Free (support costs extra) | Free (engineering time) | Flat license, no per-token fee |
| Zero-code migration | Varies | N/A (staying on LiteLLM) | Base URL and key swap |
Migration planning: from LiteLLM to enterprise gateway
Migration from LiteLLM changes two settings in each app: the base URL (keeping the /v1 path) and the key. Teams move one at a time while LiteLLM keeps running.
Zero code change migration strategy
Agent Router Enterprise exposes an OpenAI-compatible API. Existing code connects by changing one base URL, and the typical time to first request is under five minutes. Calls for chat, responses, embeddings, images, speech, and Anthropic messages work unchanged.
Any app can roll back by switching its base URL back. Migration is reversible per app, avoiding a big-bang cutover. The Tetrate migration documentation outlines the process.
Step-by-step replacement of LiteLLM
- Point a team’s apps at the new gateway base URL. Each app changes its base URL to the Agent Router Enterprise endpoint, keeping the /v1 path.
- Issue new keys or short-lived tokens. Each app gets a new Agent Router Enterprise key or a short-lived token from the customer’s identity provider.
- Verify traffic flows. Confirm requests route through the new gateway and telemetry appears in the customer’s observability tools.
- Move the next team. Repeat the process per team while LiteLLM keeps running for teams that have not moved.
Switching proxies without downtime
A team that cannot change its apps yet can use a bridge. The team’s LiteLLM gateway points at Agent Router Enterprise, which holds one key in place of the provider keys. Provider keys move under central control, and spend by team becomes visible in one dashboard, before any app changes. Per-person cost reports and per-app guardrail choices wait until the apps themselves move.
Running both gateways in parallel during migration
LiteLLM and the new gateway run in parallel during migration. Each app moves when its team is ready, and any app can roll back by switching its base URL back. Because both gateways run at the same time, each app avoids a hard cutover.
A large fintech company with thousands of developers consolidated fragmented, home-grown gateways into a single governed path. Model access widened from two frontier providers to Azure AI Foundry, Amazon Bedrock, Google Vertex AI, Mistral, and private open-weight models, with no application rewrites.
Comparing managed vs self-hosted AI control planes
The choice between scaling LiteLLM with self-hosted sync and moving to a managed gateway is a choice about where the distributed systems burden lives.
Scaling LiteLLM with self-hosted sync
Teams can scale LiteLLM by adding more workers and building self-hosted sync. This is a legitimate option. The ongoing cost is real: config delivery, rollback, drift checks, and recovery become the platform team’s permanent responsibility. Running LiteLLM in production typically means adding Redis for caching and rate limiting, PostgreSQL for data persistence, and separate tooling for log aggregation. Each dependency introduces another potential point of failure.
Self-hosted sync solves the immediate problem of pushing config across workers, but it creates a permanent maintenance obligation: the platform team owns the sync code, its failure modes, and its upgrades for as long as the gateways run.
Moving to managed gateways at scale
A managed gateway moves the distributed systems burden to the vendor. The platform team gets one place to set policy, one place to see telemetry, and no sync code to maintain. The team retains control over data location, capture settings, and policy. Tetrate holds SOC 2 Type II, ISO 27001, and GDPR compliance, with the full SOC 2 report available under NDA.
Calculating total cost of ownership
TCO for a LiteLLM alternative includes the license fee, engineering time spent maintaining sync, downtime from cross-region failures, the risk of ungoverned tool calls, and any per-token fees. A flat license makes the fee predictable. Engineering time is a variable TCO models can undercount.
Agent Router Enterprise
Tetrate Agent Router Enterprise routes AI agent traffic across providers and your own models, with smart routing, policy, cost controls, and audit on every request, in cloud, on-prem, or edge.
The architectural decision that matters
LiteLLM’s ceiling is architectural. Across regions or for high availability, LiteLLM forces a choice between a shared database or separate workers with separate state. The alternatives that matter separate the management plane from the data plane, govern agent tool calls as well as model calls, deploy where data residency requires, and license at a flat rate.
Tetrate Agent Router Enterprise is built on the Agent Router project (formerly Envoy AI Gateway), an open source data plane at the Agentic AI Foundation (AAIF) that runs on the Envoy proxy. Agent Router Enterprise runs gateways in any cloud, region, on-premises site, or edge location, and manages all of them from one management plane. The Envoy proxy behind it carries production traffic at Netflix, Lyft, and Airbnb.
Start a fast-track evaluation: first routed request under five minutes, nothing to install. Request a demo to spin up a dedicated managed instance, or request pricing for a detailed quote.
FAQs
What are the main LiteLLM competitors for enterprise teams?
The main LiteLLM competitors are self-built gateways, OpenRouter, Portkey, Kong, Bifrost, Cloudflare AI Gateway, and Tetrate Agent Router Enterprise. OpenRouter takes a share of inference spend, while Tetrate licenses at a flat rate. Kong is a general API gateway with AI features. Cloudflare AI Gateway runs on Cloudflare’s network, while Tetrate runs gateways in any cloud, region, on-premises site, or edge location.
Can I migrate from LiteLLM without rewriting applications?
Yes. Each app changes its base URL and key. Teams move one at a time while LiteLLM keeps running, and any app can roll back by switching its base URL back.
How do pricing models differ across LiteLLM alternatives?
OpenRouter takes a share of inference spend, tying vendor revenue to your usage, while Tetrate licenses the control layer with flat pricing by feature set, deployment footprint, and support tier. LiteLLM offers both an open source option and an Enterprise license for the Global Control Plane.
Which LiteLLM alternative supports multi-cloud deployment?
Tetrate Agent Router Enterprise runs data planes in any cloud, region, on-premises site, or edge location, managed from one management plane. AWS Bedrock and Azure API Management are limited to their respective clouds.
Do enterprise AI gateways govern agent tool calls or just model routing?
Tetrate Agent Router Enterprise governs both. The MCP Gateway provides a curated tool catalog with per-profile authentication and filtering, and step-up authorization built with Ory escalates high-risk actions to human approval.
Key terms glossary
PostgreSQL: The open source database LiteLLM uses to store keys, teams, budgets, and request logs. In LiteLLM’s multi-region setup, every region’s proxy instances point at one shared PostgreSQL database hosted in the primary region.
Data plane: The traffic-carrying layer of the gateway. In Agent Router Enterprise, each data plane runs on a local copy of the rules and keeps carrying traffic even if the management plane is unreachable.
Management plane: The administrative layer where policy is set once and distributed to every data plane. Tetrate hosts the management plane, and it is not in the data path.
MCP (Model Context Protocol): A protocol for connecting agents to tools. A gateway that governs MCP traffic can audit which tools an agent called, in addition to which model it used.
Step-up authorization: A policy mechanism that lets routine agent actions proceed automatically while higher-risk actions trigger human approval through the customer’s own identity system. Tetrate builds this with Ory.
Data residency: The requirement that prompts, responses, and logs stay inside a specific geographic or organizational boundary. Self-hosted data planes let regulated customers keep data inside their own network.
Learn more about Tetrate Agent Router Enterprise — enterprise AI agent routing with smart routing, policy, cost controls, and audit across every gateway.
