Portkey Alternatives: The Best AI Gateway Replacements for 2026

At a Glance

What Is Portkey and Why Teams Are Looking for Alternatives

Portkey is an AI gateway and LLMOps platform that routes requests across multiple LLM providers, enforces fallbacks, caches responses, tracks costs, and logs every inference call for observability. Organizations deploy it as a single control plane sitting between their application code and providers such as OpenAI, Anthropic, and Google Gemini. It sits at the center of many production LLM stacks. The explainer on what is an AI gateway gives more context for this control-plane pattern.

Portkey's core feature set covers 4 operational concerns: request routing with load balancing, semantic and exact-match caching, real-time cost and token tracking, and governance guardrails for prompt management. These capabilities made it a common choice for engineering groups standardizing LLM infrastructure across multiple models.

In 2026, Portkey was acquired. That event may prompt some teams to re-evaluate vendor fit, alongside their existing deployment and governance requirements. After an acquisition, teams should confirm the current pricing terms, product roadmap, and open-source commitments rather than assuming those conditions have changed.

Some evaluate alternatives for reasons independent of the acquisition. Self-hosting requirements, stricter data-residency regulations, and the need for deeper integration with existing observability stacks each push engineers toward platforms with different architectural trade-offs. The sections below compare the strongest alternatives across those exact dimensions.

How to Choose an AI Gateway: Core Criteria That Matter

Choosing the right AI gateway comes down to 6 criteria: provider routing, latency, observability, caching, spend management, and oversight. Each one eliminates a different class of platform. The comparison of AI gateways for developers applies these criteria across managed and self-hosted options.

Those criteria are:

Provider routing and fallback determines whether the gateway redirects requests to a secondary model when a primary provider returns an error or exceeds its rate limit. Gateways without automatic fallback expose production applications to single-provider outages.

Latency and performance describe the overhead between the application, gateway, and model endpoint. Deployment proximity may reduce one network hop, but actual end-to-end latency depends on routing, model load, and region and should be measured on the target workload.

Observability and logging covers whether the platform captures token counts, request traces, error rates, and cost breakdowns per request. Teams running multiple models in parallel require per-model trace granularity, not aggregate dashboards alone.

Caching stores semantically or exactly matched responses so repeated prompts bypass the LLM entirely. Semantic caching reduces redundant inference calls on workloads with repetitive queries.

Cost control and budgets enforces per-user, per-team, or per-project spending limits at the gateway layer before requests reach the provider. Rate limiting and spending caps belong at the infrastructure layer, not inside application code.

Governance, SSO, and compliance covers role-based access control, audit logs, data-residency enforcement, and identity-provider integration. Regulated industries require these controls at the gateway level to satisfy internal security reviews and external compliance audits.

The self-hosted versus managed versus unified-API trade-off cuts across all six criteria. Self-hosting shifts operations to the engineering team and can increase control over gateway data handling; for every option, check deployment region, retention settings, network path, and any external model providers.

How We Evaluated These Portkey Alternatives

This shortlist compares published gateway documentation and pricing. It does not present controlled hands-on or latency benchmarks.

The review uses the same 6 criteria defined above: provider routing, latency, observability, caching, cost control, and governance. Deployment model is a cross-cutting choice. Product fit is assessed from public documentation; implementation details and latency should be validated in a proof of concept.

Feature claims tied to specific capabilities — such as which products support self-hosting, unified billing, or prompt-injection protection — are cross-referenced against each product's public documentation. Pricing structures are drawn from published pricing pages at the time of writing; exact figures appear in the comparison table with their sources.

The scope of this evaluation covers AI gateway and LLM proxy functionality only. Adjacent concerns such as fine-tuning pipelines, vector databases, and full MLOps platforms fall outside the evaluation boundary, even where a product (TrueFoundry, for example) offers those capabilities alongside its gateway.

The Best Portkey Alternatives Compared

Four full gateway/control-plane alternatives cover the main replacement spectrum: one open-source self-hosted proxy, one managed control plane, one multi-modal unified gateway, and one enterprise MLOps-grade system.

1. LiteLLM — Best Open-Source, Self-Hosted Portkey Alternative

LiteLLM is an open-source Portkey alternative for teams that want a self-hosted gateway and granular cost controls. It documents a unified OpenAI-compatible interface across 100+ providers (litellm.ai). Self-hosting can keep gateway components under the team's control, but data flows to upstream model providers and feature compatibility still need validation.

LiteLLM documents Docker deployment and per-key spend controls; setup time and behavior should be validated in the target environment.

Routing rules are expressed in a YAML config file, which keeps infrastructure-as-code workflows intact. The admin UI is functional but sparse — organizations accustomed to Portkey's polished dashboard will notice the difference immediately. Observability depends on plugging in an external tool such as Prometheus or Langfuse; LiteLLM does not bundle a native trace viewer.

Self-hosting carries the full operational burden: upgrades, secret rotation, and scaling under load are the team's responsibility. For engineering-led organizations with existing Kubernetes or Docker Compose infrastructure, that tradeoff is acceptable. For those without DevOps capacity, it is a blocker.

2. Merge Gateway — Best Managed Control Plane with Unified Billing

Merge Gateway is a managed AI system that removes the operational overhead Portkey users carry when self-hosting. Its free plan provides credits spendable across the supported roster; paid traffic is billed at vendor token price plus a 5% Merge fee (docs.merge.dev).

A single invoice replaces separate accounts spanning OpenAI, Anthropic, and other vendors — a concrete reduction in finance-team overhead. Access controls and cost governance policies apply at the organization level, matching the control-plane model Portkey users expect.

Merge Gateway is managed by default; its Enterprise plan lists VPC or on-premises deployment, subject to contract terms. Coverage is narrower than LiteLLM's roster, so anyone running niche or self-hosted models will hit gaps. For those moving off Portkey primarily to consolidate billing and governance under a managed service, Merge Gateway is the direct replacement.

3. Eden AI — Best Multi-Modal Unified Gateway (LLM + Vision + OCR + Speech)

Eden AI is the only alternative in this set that unifies LLM routing with vision, OCR, and speech APIs under one system. Product groups that currently stitch together separate vendor accounts for text, image analysis, and transcription replace all of them through one integration point.

Eden AI covers document, language, and audio APIs under one account. Verify each endpoint's request format, provider support, and fallback behavior before treating it as a drop-in multimodal workflow.

The LLM-specific observability features are less mature than those in LiteLLM or TrueFoundry. Anyone whose primary concern is cost tracking and trace-level debugging will find the tooling thinner than Portkey's. Eden AI's value concentrates in breadth of modality coverage, not depth of LLM-specific instrumentation. It is the correct choice when the replacement scope extends beyond text models.

4. TrueFoundry AI Gateway — Best for Kubernetes-Native Private Deployment

TrueFoundry AI Gateway is a strong option for organizations that want to govern external vendors and internally hosted models through one system. It offers an OpenAI-compatible interface, so applications using compatible clients can generally migrate by updating their endpoint URL, credentials, and configuration. Teams should still test vendor-specific parameters and response behavior before switching production traffic.

TrueFoundry supports SaaS as well as private deployment options. For an on-premises setup, its installation guide describes a Helm-based process in a Kubernetes cluster. It can register self-hosted models alongside external providers, then apply routing, access controls, budgets, logging, and observability through the same interface. Its caching options include exact-match and semantic response caching.

The comparison here covers the gateway itself, not TrueFoundry's training or registry products. Its Developer plan starts at $0, but that figure should not be treated as the price of a private installation: VPC, on-premises, and air-gapped requirements need a plan-specific quote. An organization already operating Kubernetes may find this deployment model appealing; a team without that infrastructure can evaluate TrueFoundry's SaaS option instead.

Portkey also advertises private-cloud and self-hosted deployment. The practical decision therefore depends on the required deployment architecture and identity controls. It also hinges on model integrations, support terms, and a proof of concept in the organization's environment — not on an assumption that only one product can meet enterprise security requirements.

Pricing note: Fees, vendor charges, and private-infrastructure costs are separate. Catalog totals also do not guarantee access to every model on every plan.

Portkey Alternatives Compared: Pricing, Provider Coverage & Features

Pricing and product capabilities differ across these four tools in ways that affect real workload expense. "Caching" distinguishes gateway response reuse from provider-side prompt caching; the two do not offer the same behavior. Model counts describe each vendor's advertised catalog, not the number available on every plan.

Pricing note: Gateway fees, model charges, cloud infrastructure, and any enterprise contract expenses are different line items. The starting prices above describe the gateway plans, not the total expense of running a workload. Check GPT Proto pricing against the actual models and usage levels in your shortlist.

Open-Source and Free Portkey Alternatives

LiteLLM offers an open-source self-hosted edition; TrueFoundry offers private deployment, which is not the same as an open-source license.

LiteLLM is licensed under MIT and its code is publicly available on GitHub. Engineering-led groups deploy LiteLLM on their own infrastructure to gain granular cost controls, access controls, and a unified OpenAI-compatible API across more than 100 providers. The operational burden — patching, scaling, observability wiring — falls entirely on the deploying organization.

TrueFoundry AI Gateway is also hostable in-house and targets enterprises with existing Kubernetes and MLOps maturity. Groups without Kubernetes expertise face a steep setup curve before the gateway is production-ready.

Free-tier managed options exist as a lower-maintenance entry point. There are 2 patterns worth distinguishing:

Self-hosted open-source (LiteLLM): software licensing can be free, while infrastructure and provider usage remain billable. TrueFoundry private deployment has separate commercial terms.

Free-tier managed (Merge Gateway's free tier, Eden AI's free tier): no infrastructure to run, but data leaves the environment and usage caps apply.

Capacity determines the right choice. Does the organization have the engineering bandwidth to operate infrastructure? Self-managed open-source fits those that do; free-tier managed fits those that do not and accept the provider's data handling terms.

For a deeper breakdown of self-hosted AI gateway architectures and deployment patterns, the standalone open-source AI gateway comparison covers LiteLLM configuration, proxy setup, and observability integrations in full.

Enterprise-Grade Portkey Alternatives: Governance, Compliance & SSO

TrueFoundry AI Gateway and Merge Gateway are the strongest fits for enterprise groups that require oversight guardrails, compliance controls, SSO, and unified billing at scale.

TrueFoundry AI Gateway targets organizations with Kubernetes and MLOps maturity and can run on customer-controlled infrastructure. Whether the full workflow meets data-residency requirements also depends on upstream model endpoints, logs, and retention settings. Its observability can integrate with an existing MLOps stack; verify identity and permission boundaries in the actual deployment.

Merge Gateway addresses the compliance posture from a different angle. It operates as a managed plane, so the security surface is owned and audited by the vendor rather than the customer's platform staff. Cost guardrails and spend attribution land in a single dashboard rather than across three separate invoices.

For teams reviewing Portkey, Merge Gateway's managed offering can reduce self-hosting work; its Enterprise plan also lists private deployment options subject to contract terms.

SSO support and role-based access control are present in both platforms. Teams with strict compliance mandates — SOC 2, HIPAA, or internal data-classification policies — weigh self-hosted deployment (TrueFoundry) against managed-but-audited deployment (Merge Gateway). The choice often hinges on whether their security staff accepts vendor-controlled infrastructure.

Which Portkey Alternative Should You Choose? Picks by Use Case

Picking the right Portkey alternative comes down to one primary factor: where your organization sits on the self-hosted-versus-managed spectrum and what AI modalities need routing.

There are 5 use-case picks:

1. Startups Needing Fast Integration

GPT Proto may fit startups seeking managed multi-model API access without operating a gateway. For basic OpenAI-compatible calls, migration can begin with a new base URL and API key, followed by model-ID and feature checks.

2. Engineering-Led Teams Wanting Self-Hosting

LiteLLM is the pick for engineering-led groups that want an open-source, independently hosted Portkey replacement with granular cost and access controls. Those with Kubernetes familiarity can deploy it on their own servers and retain full data ownership.

3. Multi-Modal Product Teams

Eden AI serves product groups that require a unified gateway spanning LLMs plus vision, OCR, and speech APIs — modalities that single-purpose LLM gateways do not cover.

4. Enterprise Governance and Compliance

TrueFoundry AI Gateway targets enterprises with Kubernetes and MLOps maturity that want routing, observability, and model deployment unified on their own setup. Those whose security policies reject vendor-controlled hosting choose TrueFoundry over managed options.

5. Teams Moving Off Portkey Into a Managed Control Plane

Merge Gateway fits organizations that want unified billing, cost oversight, and a strong security posture without operating their own gateway setup.

Where GPT Proto Fits as a Multi-Model API Gateway

GPT Proto occupies the low-friction end of the AI gateway spectrum. It provides a single API endpoint that routes requests across multiple LLM providers, removing the setup overhead of managing separate SDKs and credentials per model. Teams can consolidate basic model access, while still validating provider-specific parameters and tool behavior. Browse the GPT Proto model catalog to verify which providers and models are currently listed.

For basic compatible calls, integration can start by changing the base URL and API key. Check the current GPT Proto model catalog, use the target model ID, and validate parameters, responses, and any media-specific endpoints before migrating production traffic.

There are 3 situations where GPT Proto may fit: early-stage products that need model flexibility before committing to a single provider, small engineering groups that lack bandwidth to operate self-hosted infrastructure, and projects where simpler multi-model integration matters more than operating a gateway.

GPT Proto is not the right fit for teams whose primary requirement is on-premises deployment. Teams requiring granular per-user budgets or enterprise SSO should verify GPT Proto's current feature support. Those needs align more directly with the self-hosted and enterprise options covered in the sections above.