Gpt Load
GPT-Load is a self-hosted AI gateway that pools API keys and Codex/Claude subscription accounts behind one base URL.
The reason to pick GPT-Load over a dozen other OpenAI-compatible proxies is the single scheduler that puts raw API keys and Codex/Claude OAuth accounts in the same pool with blacklisting. MIT license, one Go binary, UI embedded, SQLite by default. Two operational catches matter before you commit: losing encryption.key means losing every stored credential (no master-key rotation in this release), and version 2.0 cannot import 1.x data — plan a parallel run, not an upgrade.
Verified 19h ago · liveness 75/100 · cite: rightaichoice.com/tools/gpt-load
- Platform teams consolidating dozens of provider keys and Codex/Claude subscriptions behind one endpoint
- Developers who need OpenAI-, Anthropic- and Gemini-compatible clients working after a base URL and AccessKey swap
- Ops teams that need per-credential health, cooldown and blacklist visibility in one UI
- Organizations that must keep upstream credentials encrypted on their own infrastructure
- Teams that want a managed cloud gateway with no server to run or maintain
- Anyone without Docker or command-line comfort, since setup is Compose, a binary or a source build
- Teams that need billing-grade cost figures rather than estimates for capacity planning
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Skip GPT-Load if you want a managed gateway with nothing to run yourself, or if your deployment needs multi-instance scale-out rather than the documented single application instance.
GPT-Load is free and open source under the MIT License, so the cost is the infrastructure and time you run it on — a server or container host, plus MySQL or PostgreSQL if you outgrow the default managed SQLite, and the operational effort of a Compose or native-binary deployment. Compare with hosted gateway subscriptions, which bundle the server for you, and with LiteLLM if you want a Python proxy you also self-host.
In short
Gpt Load — GPT-Load is a self-hosted AI gateway that pools API keys and Codex/Claude subscription accounts behind one base URL. Best for Platform teams consolidating dozens of provider keys and Codex/Claude subscriptions behind one endpoint, Developers who need OpenAI-, Anthropic- and Gemini-compatible clients working after a base URL and AccessKey swap, Ops teams that need per-credential health, cooldown and blacklist visibility in one UI. Free to use.
What's new in Gpt Load
Checked 7 days agoAcross the latest 1 update: 1 changelog entry.
What people actually say about Gpt Load — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
14 mentions across 1 source (Lemmy) · researched Jul 3, 2026.
Average across the 1 source that answered — each source counts once, not each post.
- +Transparent proxy compatible with OpenAI API format.
- +Intelligent key rotation avoids rate limits automatically.
- +Multi-provider load balancing for high availability.
- +Open-source with MIT license - free and customizable.
- +Docker Compose makes single-node deployment quick.
- −Requires significant self-hosting setup (Docker, MySQL, Redis).
- −Limited to OpenAI, Gemini, and Claude providers only.
- −No built-in response caching – Redis configured separately.
- −Cluster mode adds complexity for horizontal scaling.
- −Lacks official support beyond GitHub issues.
- • Infrastructure costs for Docker, MySQL, and Redis hosting
- • Time investment for setup and maintenance
Viability Score
How well maintained and how widely used is Gpt Load? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Self-hosted AI API gateway behind one base URL and one AccessKey
- 35 built-in channels: official APIs, cloud platforms, model services, subscriptions, gateways
- Shared scheduler pooling API keys and Codex/Claude/Antigravity/Grok OAuth accounts
- OpenAI Chat Completions endpoint at POST /v1/chat/completions
- OpenAI Responses pass-through at /v1/responses/** with stateful resources
- Native Anthropic Messages endpoint at POST /v1/messages for Claude Code
- Gemini /v1beta/models endpoints with generateContent and streaming variants
- Weighted rotation, session affinity, retries, cooldown and automatic blacklisting
- Per-credential health view with cooldown timers and blacklist status
- AccessKeys scoped to Groups and client protocols with rate or cost limits
- Management UI embedded inside the single Go binary
- SQLite default with external SQLite, MySQL and PostgreSQL via DATABASE_DSN
- Upstream credentials encrypted locally with encryption.key
- JEV AI Guardrails: review rules, warnings, blocking and cost
- Docker Compose, native binary (Linux/macOS/Windows) or source build deployment
About Gpt Load
GPT-Load is a self-hosted AI API gateway released under the MIT License as a single Go binary. You put the provider credentials you already own — raw API keys plus OAuth subscription accounts for Codex, Claude, Antigravity and Grok — into one shared pool, then hand your applications a base URL and an AccessKey. Setup is three steps: create a Group (upstream channel plus its credential pool), pick the models that Group exposes with weight, timeout and retry policy, then issue an AccessKey scoped to Groups and client protocols with rate or cost limits. Four client protocols go in: OpenAI Chat Completions at /v1/chat/completions, OpenAI Responses under /v1/responses/**, Anthropic Messages at POST /v1/messages, and Gemini /v1beta/models including generateContent and streaming variants. Behind them sit 35 built-in channels spanning official APIs (OpenAI, Anthropic, Gemini, xAI), cloud platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI), 19 model services such as DeepSeek, Moonshot AI, SiliconFlow, Zhipu AI, OpenRouter, Groq, Cerebras, Mistral and Cohere, the four subscription accounts, and gateway-compatible channels including New API, CLIProxyAPI, Sub2API and OpenAI Compatible. Whether a given channel works natively or through conversion is published in the capability matrix, which is the reference you should read before assuming a protocol works. One scheduler treats both credential types the same, applying weighted rotation, session affinity, retries, cooldown and automatic blacklisting so a limited or invalid credential gets isolated before it drags a whole route down. The management UI is embedded in the binary — no separate frontend deploy — covering channel health, route inspection, request logs, usage summaries and per-model cost estimates. Storage defaults to managed SQLite; set DATABASE_DSN to use external SQLite, MySQL or PostgreSQL. Upstream credentials are encrypted locally with encryption.key and never leave your infrastructure. Platform teams
Behind the Verdict
GPT-Load earns its place when your real problem is credential sprawl, not routing. If you have a folder of OpenAI keys, a couple of Anthropic ones and three Codex or Claude subscription logins that keep hitting rate limits, the shared pool plus weighted rotation and cooldown is the part that saves you. Doing that by hand with a load balancer means writing the health-check and blacklist logic yourself. In practice, deployment is the friction. It is Docker Compose, a native binary (Linux/macOS on amd64 and arm64, Windows on amd64 with an installer that registers a service) or a source build. If nobody on your team is comfortable with a shell, budget for the learning curve before you promise anyone a rollout. We'd reach for it when data control is a requirement. Credentials are encrypted locally with encryption.key and requests go straight to the upstreams you selected — no third party in the path. Backup discipline is non-negotiable: if that key is lost or replaced, encrypted credentials cannot be recovered, and this release doesn't support master-key rotation. The closest alternative depends on what you want to give up. LiteLLM does comparable multi-provider proxy work with its own config-driven model; if you'd rather not run any infrastructure, a managed router like OpenRouter removes the ops burden but puts your keys and traffic in someone else's hands. GPT-Load sits firmly on the self-hosted end of that spectrum. Upgrade planning is where people get burned. The current release is 2.0, 1.4.x docs are archived, and 2.0 cannot be upgraded in place or import 1.x data. The documented path is running it in parallel and following the Moving from 1.x guide — schedule that as a project, not a maintenance window. Finally, mind the stated target of a single application
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Real-world workflow fit
Concrete scenarios for the personas Gpt Load actually fits — and what changes day-one when you adopt it.
You create a Group against the OpenAI channel, paste in every OpenAI and Gemini key your team owns, select the models the Group exposes, and issue an AccessKey scoped to that Group with a cost limit. Your SDK apps then point at the single base URL and AccessKey.
Outcome: One endpoint handles scheduling, retries and cooldown, and the embedded dashboard shows per-credential health and per-model cost estimates without a second frontend to run.
You add your Claude and Codex subscription accounts via OAuth so they join the same scheduler as your raw API keys, then change the client's base URL and key to the gateway's AccessKey.
Outcome: Claude Code and Codex keep their existing protocols while a limited credential is cooled down or blacklisted before it takes down the route.
You leave DATABASE_DSN empty to stay on managed SQLite, set encryption.key so upstream credentials are encrypted locally, and configure request redaction rules.
Outcome: Credentials never pass through a third party, redaction rules apply before requests leave your infrastructure, and you can move to MySQL or PostgreSQL later by setting a DSN.
Use Cases
- Route all AI API calls through a single endpoint with automatic key rotation.
- Balance traffic across multiple OpenAI and Gemini keys to avoid rate limits.
- Pool Codex, Claude, Antigravity and Grok subscription accounts alongside raw API keys.
- Monitor channel health, request logs, usage summaries and per-model cost estimates from the embedded dashboard.
- Let Claude Code, Codex or a Cherry Studio client work by changing only base URL and AccessKey.
- Keep upstream credentials encrypted on your own infrastructure with no third-party pass-through.
- Start on managed SQLite with no external database, then move to MySQL or PostgreSQL.
- Script Group and AccessKey management through the Management API.
Limitations
- GPT-Load is a gateway and ships no AI models of its own; every response comes from the upstream channels you configure, so the model set depends entirely on your own credentials.
- Production setups add MySQL or PostgreSQL plus encryption.key management, and per the existing profile losing encryption.key means stored credentials are unrecoverable.
- Versioning needs care: the evidence confirms 2.0 cannot be upgraded in place or import 1.x data, with 1.4.x documentation in a separate archive.
- Deployment requires Docker Compose, a native Go binary or a source build rather than a purely click-to-run install.
as of 2026-10-11
Verification history
We have re-verified Gpt Load 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Gpt Load tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source (MIT)
$0
Ideal for
Platform teams and developers who will run the gateway themselves and want to consolidate provider keys and Codex or Claude subscriptions on their own infrastructure.
What this tier adds
Starting tier: the whole gateway under the MIT License at no cost, with self-hosted deployment, 35 built-in channels, the shared scheduler and local credential encryption.
Where the pricing makes sense
The company stage and team size where Gpt Load's pricing actually pencils out — and where peers do it cheaper.
GPT-Load is free and open source under the MIT License, so the cost is the infrastructure and time you run it on — a server or container host, plus MySQL or PostgreSQL if you outgrow the default managed SQLite, and the operational effort of a Compose or native-binary deployment. Compare with hosted gateway subscriptions, which bundle the server for you, and with LiteLLM if you want a Python proxy you also self-host.
Setup time & first value
How long it actually takes to get something useful out of Gpt Load — broken out by persona, not the marketing-page minute.
Compose deployment to a generated management key is documented as roughly five minutes, and the Quick start targets a first successful request in about ten minutes. Expect longer if you are configuring several Groups, OAuth subscription accounts, or external MySQL/PostgreSQL alongside encryption.key management.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Gpt Load”, and we withheld 6: 6 did not mention Gpt Load. We are showing none, because we could not prove any of them are about Gpt Load.
Official links
Featured Head-to-Head Comparisons
Gpt Load vs Spider Cloud
If your project needs live web data for LLM context, RAG, or AI agents, Spider Cloud is the clear winner with its fast Rust engine, AI extraction, and Browser AI commands. If you instead struggle with managing multiple AI API keys, quotas, and provider failover, GPT Load's free self-hosted proxy is a powerful, complementary tool. They solve different problems—choose based on whether you need web scraping or API orchestration.
Gpt Load vs Voyage Ai
If you need high-accuracy embeddings for domain-specific RAG (finance, legal, code) and have enterprise budget, Voyage AI is the clear choice. If you're a developer or team wanting to manage multiple AI API keys, avoid rate limits, and need a self-hosted proxy at no cost, GPT-Load is ideal. They solve completely different problems; choose based on whether you need better retrieval or better API orchestration.
Gpt Load vs Temporal Ai
If you need durable execution that survives failures for AI agents or complex workflows, choose Temporal AI. If you simply want to proxy and load-balance multiple AI API keys with failover, GPT Load is the lightweight, free solution. Temporal is overkill for simple proxying; GPT Load lacks workflow state and recovery.
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