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Alternatives
Software like LLM API
What else does this job. Matched on what each project does, not on who links to whom.
Closest first
- LLM7.iollm7.ioLLM7.io is a platform that offers a single API endpoint to connect with various leading AI models. It enables developers and businesses to prototype, build, and scale AI applications efficiently without managing multiple provider integrations.
- LLM Gatewayllmgateway.ioLLM Gateway is an API layer for working with multiple LLM providers through one integration. It is described as a single API for 40+ providers, including OpenAI, Anthropic, and Google, and it routes requests across 200+ models while tracking costs in real time. The service is also presented as a way to avoid juggling provider API keys and dashboards, and to switch providers without changing application code. Several functions are called out on the product page. It offers a unified API interface that stays compatible with an existing OpenAI SDK setup by changing the base URL. It includes multi-provider support, performance monitoring to compare latency, cost, and quality, secure key management, cost-aware analytics, per-model and per-provider usage breakdowns, error and reliability monitoring, a project-level usage explorer, enterprise audit logs, and guardrails intended to prevent prompt injection, detect PII, and block malicious requests. The page also says requests are automatically routed to healthy providers in real time, with failover if one provider goes down. The product is shown as working with OpenAI, Anthropic, and Vercel AI SDKs, and the page says it works with any language or framework. It is offered as a cloud service or as a self-hosted deployment on the user’s own infrastructure. Pricing includes bring-your-own-keys use at no cost, pay-as-you-go credits with a 5% flat fee on top-ups, and a free self-hosted option. The self-hosted gateway is licensed under AGPLv3. The page also mentions an enterprise offering with SSO, managed or self-hosted deployment, volume pricing, and white-label options. LLM Gateway is presented as a tool for developers and teams building and operating LLM-based applications.
- Manifestmanifest.buildManifest is a platform that lets users connect their AI agents and applications to multiple LLM providers, including cloud APIs and local models. It enables routing, fallback, and cost control for LLM inference, helping developers optimize spend and avoid vendor lock-in. Designed for teams managing AI workloads.
- LLM Gatewaysllmgateways.comLLM Gateways is an AI security layer for LLM applications. It is built to stop prompt attacks before they reach a model, including prompt injection, jailbreaks, and system-prompt extraction. The service presents itself as a single API call with instant risk scores, blocking malicious prompts in milliseconds while also returning analytics and webhook notifications. The product supports a drop-in SDK or raw HTTP access. Examples are shown for Python, Node.js/TypeScript, and cURL, and the Python and Node.js examples wrap existing OpenAI clients. The request flow returns a risk score, an action such as block, and a list of threats; the page also shows a blocked-request exception in the SDK examples. Its detection stack is described as a three-layer engine made up of pattern rules, semantic similarity, and an LLM judge. LLM Gateways says it covers 78+ threat patterns and lists prompt injection, DAN jailbreaks, system-prompt extraction, PII leakage, and token smuggling among them. It also offers real-time dashboards, threat breakdowns by category, per-API-key statistics, scan history, HMAC-signed webhooks with configurable event types, email alerts when block rates spike, and per-key custom rules for blocklisting phrases or disabling detection categories. Each API key is rate limited to 100 requests per minute. The service is available free during beta, with all features included and no credit card required. The page identifies Apostrophe AI as the publisher and links to docs, privacy, and terms.
- LLM Hubllm-hub.appLLM Hub is an open-source AI application for Android, iOS, Mac, and browser that enables users to run over 15 AI models fully offline. It supports chat, image and video generation, translation, voice transcription, and code generation, all without cloud connectivity or account signup. Designed for privacy-focused users who want powerful AI capabilities on their own devices.
- LiteLLMlitellm.aiLiteLLM is an open-source gateway and proxy that enables developers to manage authentication, load balancing, and spend tracking across 100+ LLM providers using a unified OpenAI-compatible API. It streamlines integration and monitoring for AI applications.
- Awan LLMawanllm.comAwan LLM is an API platform for power users and developers that provides LLM inference with unlimited tokens up to a model’s context limit. It describes its service as unrestricted and cost-effective, and says it uses a monthly payment model rather than charging per token. The service highlights several named use cases: asking an AI assistant for help, running AI agents, roleplay, data processing, code completion, and building AI-powered applications. Its copy says users can send and receive unlimited tokens, use models without constraints or censorship, and process large amounts of data without limits. It also says code completion is available and that the platform is intended to make AI applications more profitable by removing token costs. Awan LLM says it provides Meta Llama 3.1 8B and 70B models, and notes that users can sign up for an account and then use the API endpoints through a Quick-Start page. The FAQ states that the company owns its own datacenters and GPUs, does not log prompts or generations, and applies request rate limits that are explained in its Models and Pricing page. It also says support is available by email at contact.awanllm@gmail.com or through the contact button on the site, and that model requests can be sent if a desired model is not listed. The site includes links for Pricing, Docs, Privacy Policy, and Terms and Conditions, and states that Awan LLM offers a free start. It also says the API can be used instead of self-hosting LLMs, with lower cost than renting GPUs in the cloud or paying electricity to run one’s own GPUs.
- Llmpricesllmprices.devLLMPrices.dev is a web-based tool that allows users to compare and track the pricing of large language models (LLMs) from various providers in real time. It helps AI developers and researchers make informed decisions by providing up-to-date price data and model information.
- Mesh LLMmeshllm.cloudMesh LLM is a distributed model inference tool for running large language models across multiple nodes. It is described as a way to run bigger models without buying bigger GPUs, by splitting large models into smaller pieces so memory-bound GPUs can handle smaller layer slices on right-sized nodes. The service supports two mesh modes. In router mode, the model field selects a host and requests are routed by model. In split mode, one large model is divided into layer ranges that are processed as a pipeline across several machines. The page also says that tokens can stream back to the client, and shows an OpenAI-compatible API with a local base URL, so existing tools can point at localhost while Mesh handles routing or splitting. Mesh LLM is presented for use across a homelab, a business, or the internet. The examples and interface reference use with tools such as goose, vscode, opencode, and pi.dev, as well as any OpenAI client. It also includes a mesh planner for partitioning layers and a live model catalog drawn from Hugging Face, where models can be browsed and copied as org/repo quantized references for single-machine or multi-machine use. Beyond inference, Mesh LLM includes a pluggable architecture for distributed agents. Plugins declare what they provide in a manifest, and the runtime starts them, routes calls, and exposes capabilities over MCP, HTTP, inference, and mesh events. The page also mentions blobstore state persistence, blackboard agent coordination, configuration settings, metrics, data, tools, inference backends, model serving, HTTP routes, REST endpoints, and mesh events channels and gossip. Installation is shown with a shell command, curl -fsSL https://meshllm.cloud/install.sh | bash, followed by mesh-llm setup. The page does not state pricing or licensing information.
- LLM Councilllmcouncil.onlineLLM Council is a web platform that enables users to connect multiple AI models using their own API keys and have them deliberate on a question. Each model critiques others' responses, and a chairman model synthesizes the most reliable answer. It is designed for AI researchers and enthusiasts seeking robust, multi-model insights.
- llmpypi.orgllm is an open-source command-line utility and Python library that enables developers to interact with large language models from providers like OpenAI, Anthropic, and Google Gemini, as well as local models. It supports prompt management, model selection, and streaming responses, making it easy to work with LLMs in scripts or development workflows.
- LLLMlllm.oneLLLM, short for Low-Level Language Models, is a protocol and service layer for reusable agentic tactics. Its core idea is a Tactic: a typed unit of work that can run in process, stream results, sit behind a FastAPI service, and be described as a PsiHub package resource. The stated aim is to give different runtimes and surrounding tools a stable boundary for agentic work. The system centers on a small contract rather than on model execution itself. LLLM is not a model runtime; execution details such as tools, provider settings, tracing, eval hooks, and workflow state are handled by Pydantic AI, native LLLM objects, plain Python, or future adapters. The documentation says LLLM owns Tactic, TacticInfo, CallContext, and TacticEvent, along with local, async, streaming, proxy, and sandbox wrappers at the tactic boundary. It also provides FastAPI service adapters and remote tactic clients, exposing /run, /stream, and /info with stable envelopes. LLLM also includes metadata helpers that export tactics to PsiHub package resources. Those resources are described as discoverable, configurable, and composable, and the page shows ref resolution for local or remote tactic bindings. The text also notes that package storage, validation, cards, agent cards, config templates, semantic channels, event logs, artifacts, snapshots, local stores, service launch decisions, and operational orchestration stay outside LLLM’s responsibility. The tool is presented for apps, workers, robots, coding agents, package tools, and similar callers that need to understand the same tactic contract without caring whether the underlying implementation is an offline test agent, a Pydantic AI agent with tools, a native prompt/dialog workflow, or a remote HTTP service. No pricing or license information is stated in the provided material.
- LLM Armorllmarmor.devLLM Armor is a static analysis scanner for Python AI applications that checks for OWASP LLM Top 10 vulnerabilities. It is aimed at finding issues before attackers do, and it is presented as a fast, free tool that runs in seconds. The scanner is described as having no agents, no API calls, and no runtime overhead. Its OWASP coverage includes 7 of the 10 categories: LLM01, LLM02, LLM05, LLM06, LLM07, LLM08, and LLM10, with 2 marked strong and 5 partial. The page says LLM01 and LLM08 have strong detection, while the other listed categories have partial coverage. An example shown on the site illustrates a prompt injection finding, and the coverage section also mentions detection of leaked API keys, eval/exec and other unsafe sinks, dangerous @tool functions, hardcoded prompts, dynamic dispatch, disabled approval gates, and missing max_tokens on LLM API calls. LLM Armor is built for Python and is said to work with OpenAI, Anthropic, LangChain, CrewAI, Smolagents, Google ADK, Semantic Kernel, MCP, and more. It is delivered as a CLI, with installation instructions that use pip and a scan command. The site also says it can be run in GitHub Actions, GitLab CI, and other CI/CD systems, and that it outputs structured exit codes and SARIF for pipeline gating. Documentation, a CLI reference, coverage details, a compare section, and a blog are also listed. It is fully open source under the MIT license. The site says there is no paid tier, no API key requirement, and no vendor lock-in, and it invites users to audit, fork, and extend the code. The product compares itself on-site with garak, Promptfoo, and Lakera, describing LLMArmor as purpose-built for OWASP LLM Top 10 static analysis.
- AnythingLLManythingllm.comAnythingLLM is an AI application for working with documents and AI agents in one interface. It is described as local and private by default, with desktop use centered on running AI tasks without requiring setup or code. The product supports chatting with documents, using AI agents, and working with custom models. It can run a preferred LLM locally, connect to local or cloud LLM engines, or use enterprise models from OpenAI, Azure, AWS, and more. It also supports text-only and multi-modal LLMs, with images or audio mentioned as usable in the same interface. Document support includes PDFs, Word documents, CSV files, codebases, and importing documents from online locations. The site also says it includes built-in defaults for the LLM, embedder, vector database, storage, and agents, and that nothing is shared unless allowed. AnythingLLM Desktop is available for MacOS, Windows, and Linux, and is presented as a one-click install. The desktop version is not SaaS and does not require a signup to use the full suite of tools locally. The same page also mentions hosted and self-hosted options for team use, with multi-user access, full isolation between tenants, and admin control over what users can do and see. It is open source, free to use, and MIT licensed. The page further describes a built-in developer API, plus a growing ecosystem of plugins and integrations, custom agents, data connectors, and data loaders.
- Cloudidr LLM Opscloudidr.comCloudidr LLM Ops is an AI FinOps gateway for controlling and attributing LLM API spend. It sits between applications and LLM providers, and the page says it gives engineering and finance teams request-level cost attribution, hard budget controls that block overspend, and intelligent model routing to cut LLM costs by 75–90%. The service supports routing across AWS Bedrock, OpenAI, Anthropic, and Gemini. Its interface highlights an optimization flow with a complexity scorer that uses 36 factors on a 0–400 point scale and routes prompts across model tiers such as Claude Haiku, Claude Sonnet, and Claude Opus. The page also describes quality fallback behavior in which a low score escalates to the next tier automatically. Savings and spend reporting are shown in the interface, including monthly, last-month, and all-time savings figures. Cloudidr says users can pay their LLM provider directly, without a middleman, and that there is no lock-in because setup or tear down can be done in 60 seconds. It is available as SaaS or can be deployed fully in an AWS VPC for data-sensitive workloads, with prompts never leaving the customer’s infrastructure. The page also advertises a free starter plan with no card needed and says cancellation is possible at any time. A separate enterprise callout refers to full VPC deployment and the data and control plane being deployed entirely in the customer’s AWS VPC. The page also mentions AWS Marketplace.
- LLM Radarllmradar.aiLLM Radar is a free web-based platform that helps users stay up to date with the latest large language models (LLMs). It provides notifications and a comprehensive list of new LLMs, making it easy for AI researchers and enthusiasts to discover and track new models as they are released.
Ranked by how close each one sits to LLM API in the index, not by popularity. Back to LLM API →