Spendline Alternatives

There are an estimated 25 million engineers in the world across dozens of distinct functions. As every company becomes a software company, engineers are using New Relic to gather real-time insights and trending data about the performance of their software so they can be more resilient and deliver exceptional customer experiences. Only New Relic provides an all-in-one platform that is built and sold as a unified experience. With New Relic, customers get access to a secure telemetry cloud for all metrics, events, logs, and traces; powerful full-stack analysis tools; and simple, transparent usage-based pricing with only 2 key metrics. New Relic has also curated one of the industry’s largest ecosystems of open source integrations, making it easy for every engineer to get started with observability and use New Relic alongside their other favorite applications.

FinOpsly is an AI Cost Governance platform. It brings AI, cloud, data platform and SaaS spend into one attribution, policy and control layer, so enterprises can price a workload before building it, attribute every dollar to an owner, hold spend inside budget under policy, and prove what landed in run-rate. Your AI invoice is not what your AI costs. One request draws on model tokens, retrieval, warehouse queries, GPU capacity and storage, and only the first shows up on the AI bill. FinOpsly resolves all of it, plus the seats in procurement and the compute in an untagged cloud account, to the same dimensions: owner, team, application, line of business, customer and tenant. An AI initiative's full cost becomes one figure, charged back through one hierarchy in one cycle. Workforce AI is the tools employees use: seats and per-user token draw across GitHub Copilot, Cursor, ChatGPT Enterprise and Microsoft 365 Copilot. Application AI is the AI your product ships: tokens, compute and data joined into cost-to-serve across OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex AI, SageMaker and Databricks. PLAN. Price a workload from its architecture before any resource exists, across model APIs, GPU capacity, data platform consumption and storage, with assumptions visible. Compare it across candidate models on your measured usage. EXPLAIN. Attribute spend to owner, team, application, line of business and business unit across 9+ hierarchy levels. Unified tagging reconciles providers that tag inconsistently, and AI-driven bulk labeling closes large key estates. Unattributed spend is reported in dollars. ACT. Budgets per project, team and API key, with daily burn-rate monitoring. Anomaly detection with root cause, routed to the owner. Waste detection using FinOpsly's own algorithms and ML models. Commitment planning across AWS, Azure and Google Cloud. Policy-driven parking of idle compute. PROVE. Chargeback across AI, cloud, data and SaaS in one cycle. Realized savings tracked into run-rate against a no-action baseline. Cost per call, cost per active user, and cost-to-serve per customer and tenant. proof: 100% attribution of AI spend; chargeback from 12.4 days to under one day across 9+ levels; 26% realized savings in AWS and 17%+ in Azure at a payments client. Built for CIOs, CTOs and platform leaders accountable for technology spend, FinOps and finance teams running chargeback, and engineering teams who need cost signal before they decide

Cloptima is an AI and cloud FinOps platform that brings LLM spend governance, multicloud cost intelligence, Kubernetes optimization, query analysis, and engineering cost controls into one operating model. Its AI gateway lets teams use their own OpenAI, Anthropic, Gemini, Vertex AI, and Amazon Bedrock credentials behind encrypted controls, then apply virtual keys, model policies, token limits, budgets, guardrails, and attribution before calls reach providers. Spend analytics break down usage by provider, model, team, application, environment, user, agent session, tool, workflow, and dimensions, while agent controls track retries, loops, tool calls, and runaway-cost risk. Exact and semantic response caching can reduce repeated usage, and intelligent routing can shift eligible traffic to cheaper or faster models with canary rollout and rollback if quality, latency, or errors regress.

Waterfall is a credit infrastructure for platforms building on large language models, designed to turn AI usage into a business model without requiring teams to build their own billing stack. It gives each user, agent, or team a stablecoin-backed credit wallet, then meters every model call by provider, model, token count, and cost. Requests can be routed through the Waterfall Gateway or integrated through TypeScript and Python SDKs, with usage attributed to the correct wallet in real time. Each API call settles atomically against the wallet as it happens, so credits decrease, and revenue is recognized per request instead of through delayed invoices and manual reconciliation. Waterfall supports more than 300 models across providers such as OpenAI, Anthropic, DeepSeek, and xAI, allowing products to use multiple AI services while maintaining one accounting layer.

AI Cost Board is an AI API observability and cost control platform that brings costs, requests, tokens, latency, errors, and usage from multiple model providers into one real-time dashboard. Applications route LLM traffic through a single proxy endpoint, while requests are forwarded to the connected provider and logged with model, token, status, timing, costs, input, output, and raw JSON context. In most cases, teams only replace the provider base URL and use an AI Cost Board project key, keeping the original request structure intact. It supports providers including OpenAI, Anthropic, and Google Gemini, with a consistent setup that standardizes usage data across integrations. Cost analytics break spending down by project, provider, model, and timeframe, showing trends, cost per request, success rates, and operational performance. Searchable request logs help developers inspect payloads, troubleshoot failures, compare models, and investigate slow or expensive calls.

SatGate is an agent authority and accountability Layer that governs what AI agents can access, spend, delegate, and execute before a request reaches an API, model, MCP tool, or paid external service. Deployed as an HTTP reverse proxy and MCP proxy, it applies scoped authority, per-agent budgets, route policies, and next-request revocation directly in the request path. Agents badge in once through existing Kubernetes, AWS, or OIDC identity, and SatGate Mint exchanges that identity for a cryptographically signed Macaroon containing limits for scope, budget, expiration, and delegation depth. Capabilities can only become more restrictive as they move through agent chains, preventing sub-agents from escalating beyond the authority they receive. Observe mode measures requests and attributes usage by agent, team, tool, route, and cost center without changing workflows; Control mode enforces hard budget caps before expensive or unauthorized work executes.

WrangleAI is an enterprise-grade platform that gives organizations visibility, control, and governance over their AI usage and spending. It acts as a “control plane” for generative-AI tools (like GPT-4, Claude, Gemini, and more), providing real-time usage tracking across providers, cost intelligence, infrastructure monitoring, and spend caps so companies can avoid runaway budgets. WrangleAI offers AI observability, helping teams understand which models are being used, by whom, and for what purposes, plus routing intelligence that can redirect workloads to more cost-effective models while maintaining output quality. It also includes governance features such as role-based access control and compliance support (e.g., for SOC 2 / ISO 27001 standards), enabling finance, engineering, and leadership teams to coordinate, enforce policies, and get actionable recommendations for optimizing AI spending and usage.

Toolspend is an AI-powered spend management platform designed to give organizations complete visibility into their AI and SaaS costs through a unified, automated dashboard. It connects directly to AI providers and financial data sources to reveal real usage patterns, show which teams drive consumption, and reconcile token metrics with actual billing. It goes beyond simple subscription tracking by analyzing usage behavior to identify underutilized licenses, duplicate tools across departments, and potential overpayments. It provides real-time monitoring, anomaly alerts for unusual spikes, and month-end forecasting so teams can anticipate costs before invoices arrive. It also delivers AI-driven recommendations such as switching to cheaper models or pausing idle resources, helping companies reduce waste and control budget growth.

ZenLLM is an AI cost optimization platform for engineering teams running LLM applications in production. It connects provider invoices to the application behavior behind them, showing which prompts, workflows, models, customers, retries, and request paths are driving spend. Teams send request-level telemetry through the ZenLLM SDK and can attach business context such as workflow, owner, customer, team, or product feature without storing prompt or response content. It monitors token usage, model selection, latency, errors, retries, and cost, then surfaces the waste patterns hidden by aggregate provider dashboards. It detects context accumulation when conversations or agents resend growing histories, premium-model overuse on low-risk work, retry loops that repeat expensive context, stale system prompts, routing mistakes, anomalies, and weak cost ownership.

Mavvrik is an AI and hybrid infrastructure cost management platform that gives finance, FinOps, IT, and engineering teams one control center for GenAI, autonomous agents, GPUs, cloud, on-premises systems, Kubernetes, data platforms, and SaaS. It unifies cost, usage, and telemetry signals from AWS, Azure, Google Cloud, Oracle, VMware, NVIDIA, OpenAI, Anthropic, Gemini, Snowflake, Databricks, and LiteLLM, creating a single source of truth across the technology stack. Teams can track every model call, agent interaction, GPU hour, workload, service, and resource, then allocate spending by customer, product, feature, project, application, environment, team, or cost center. Cost-to-serve and unit-economics analysis reveal margin drains, expensive workloads, and the true cost of delivering each offering. Real-time anomaly detection and alerts identify usage before it becomes a budget surprise, while predictive forecasting helps organizations model cloud, GPU, and AI expenses.

AICosts.ai is a unified AI cost management platform that brings billing and usage data from more than 50 providers into one dashboard. Teams upload provider invoices and exports in PDF, CSV, or JSON format, or push usage events through the developer API, and the platform parses them into a normalized structure without requiring a proxy or changes to production requests. It supports services including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Vertex AI, Cohere, Groq, Hugging Face, Pinecone, RunwayML, Make, Zapier, and n8n. Daily views break spending down by platform, model, and billed unit, including tokens, operations, characters, and other provider-specific measures, helping users compare services and see where each bill comes from. Budgets can cover the full AI stack or a specific platform or feature, with email alerts when rolling 30-day spending crosses configured thresholds.

Track costs, usage, and latency for GPT applications with one line of code. Trusted by leading companies building with OpenAI. We will support Anthropic, Cohere, Google AI, and more coming soon. Stay on top of your costs, usage, and latency. Integrate models like GPT-4 with Helicone to track API requests and visualize results. Get an overview of your application with an in-built dashboard, tailor made for generative AI applications. View all of your requests in one place. Filter by time, users, and custom properties. Track spending on each model, user, or conversation. Use this data to optimize your API usage and reduce costs. Cache requests to save on latency and money, proactively track errors in your application, handle rate limits and reliability concerns with Helicone.

LLMeter is an open source AI cost monitoring platform that gives developers one dashboard for tracking spend across OpenAI, Anthropic, DeepSeek, OpenRouter, Mistral, and Azure OpenAI. Teams connect read-only provider keys and can see real costs, daily trends, model-level breakdowns, and optimization opportunities in about 30 seconds without installing an SDK, changing endpoints, or routing production traffic through a proxy. Because requests continue going directly to the model provider, LLMeter adds no latency, does not become a point of failure, and never sees prompts or completions. Budget alerts warn teams before spending crosses daily or monthly limits, while anomaly detection identifies unexpected usage spikes before they grow. The dashboard shows which providers, models, endpoints, customers, and environments are driving costs, and OpenRouter support extends visibility across more than 500 models.

FinOps LLM is an AI cost management and LLM observability platform for engineering teams running production GenAI. It makes token spend visible across OpenAI, Anthropic, Amazon Bedrock, Google Gemini, Azure, Groq, and other providers and reconciles internal usage data against provider invoices. Token-level costs can be filtered by provider, model, feature, team, customer, environment, and custom dimensions, giving every dollar a clear owner. Attribution and chargeback tools map usage to product surfaces and customer cohorts, support showback, and export data to NetSuite, QuickBooks, CSV, or APIs. Real-time anomaly detection monitors spend, latency, and quality against rolling feature baselines, sending alerts through Slack, PagerDuty, email, or webhooks when behavior changes. Optional budget enforcement and auto-throttling can stop runaway agents, retries, or model shifts before they become expensive.

Requesty is a cutting-edge platform designed to optimize AI workloads by intelligently routing requests to the most appropriate model based on the task at hand. With advanced features like automatic fallback mechanisms and queuing, Requesty ensures uninterrupted service delivery, even during model downtimes. The platform supports a wide range of models such as GPT-4, Claude 3.5, and DeepSeek, and offers AI application observability, allowing users to track model performance and optimize their usage. By reducing API costs and improving efficiency, Requesty empowers developers to build smarter, more reliable AI applications.

LLMetrics is LLM cost tracking software for teams shipping AI products, bringing model spend, token usage, feature attribution, and usage alerts into one live dashboard. It supports more than 100 models across OpenAI, Anthropic, Google Gemini, Mistral, Cohere, Together AI, Groq, and other providers, with pricing data synchronized daily. Teams tag each model call with a feature name, provider, model, input tokens, and output tokens, allowing them to see exactly whether a chatbot, summarizer, search feature, lesson generator, or other workflow is driving spend. Real-time updates and daily trend charts reveal how costs change after releases, prompt edits, traffic growth, or model swaps. Spend thresholds and spike-detection rules can alert teams through email or Slack when usage patterns look wrong, helping them catch runaway loops and unexpected cost increases before the provider invoice arrives.

AICostGuardian is an enterprise AI cost management platform that helps organizations track, optimize, and control spending across 25+ AI providers from one unified dashboard. It monitors every API call with millisecond precision, calculates cost instantly, and combines provider usage into cross-platform analytics, automated reports, forecasts, and dashboards. Teams can analyze spending trends, compare usage, identify optimization opportunities, and use machine-learning insights and smart recommendations to reduce unnecessary AI expenses. Predictive alerts and anomaly detection warn users about unusual usage spikes and approaching budget overruns, while configurable spending limits help keep consumption under control. Department-level cost allocation, team analytics, granular permissions, and role-based access make it easier to understand ownership and govern AI use across an organization.

Burnwise is an AI cost copilot that shows where an organization’s AI budget goes, why spending changes, and what actions can reduce it without sacrificing product quality. It tracks usage across LLMs, image generation, video, and audio from major providers through a single SDK and unified dashboard. Instead of stopping at aggregate token charts, Burnwise attributes costs to individual product features, users, sessions, teams, and agent workflows, helping teams understand the true cost of functions such as chat support, document analysis, summaries, or translation. Usage intelligence highlights cost-to-value mismatches, while anomaly alerts identify sudden spikes and runaway prompts in real time. Burnwise delivers a small set of prioritized decision cards with estimated savings, risk, and quality impact, covering actions such as switching models, enabling semantic caching, setting limits, or changing how a feature runs.

Keep track of your OpenAI usage and costs with AI Spend and never be surprised again. AI Spend offers user-friendly cost tracking with a dashboard and notifications that passively monitor your usage and costs. The analytics and charts provide insights that help you optimize your OpenAI usage and avoid billing surprises. Get daily, weekly, and monthly notifications with your spending. Discover which models and how many tokens you're using. Get clear insights into how much OpenAI is costing you.

Kimchi is a centralized gateway for managing SaaS and self-hosted AI models, built to help teams deploy, route, optimize, and scale LLM infrastructure without changing the developer workflow. It gives organizations one control layer for AI coding agents, open-source models, commercial models, and internal inference, allowing teams to combine lower-cost OSS models with higher-tier providers such as Claude, OpenAI, Gemini, and others when the task requires it. Kimchi focuses on reducing LLM cost while making development more autonomous, with fast model routing, coding-oriented inference, MCP integration, multi-agent workflows, interchangeable OSS and commercial models, and low-friction local setup. It supports running the Kimchi coding agent across teams, giving engineering organizations broader access to AI coding while preserving team-wide usage attribution, cost visibility, and operational control.

We give engineering, finance, and FinOps teams a single platform to track, attribute, and optimise LLM API spend across every provider. Costs are broken down by dimensions you define, matching how your business already reports its financials. Engineering teams get frictionless cost tracking without slowing anything down. CTOs get a single pane of glass to enforce model governance and prevent shadow usage. CFOs get finance-grade reporting for forecasting, budgeting, and chargebacks, attributed using their own reporting structure. FinOps teams get real-time, multi-provider cost data that slots straight into the workflows they already run for cloud. If your organisation uses LLM APIs and the board is asking "what are we spending and why?" we're the answer.

Amnic is a FinOps tool powered by context-aware AI agents that helps organizations gain clarity and control over their cloud spending. It automates cloud cost management by deploying role-specific agents that analyze usage, detect anomalies, and generate insights tailored to different stakeholders. Through its cloud cost observability capabilities, Amnic enables teams to visualize, analyze, and optimize infrastructure expenses, turning complex cloud bills into actionable intelligence. It provides fast cloud financial health checks, natural-language insights, and automated reporting that reduce the manual effort typically required for FinOps workflows. Built-in governance tools monitor budget drift, enforce tagging hygiene, and assign ownership, helping organizations maintain accountability across engineering and finance teams.

TokenAtlas is an AI FinOps and cost intelligence platform that helps teams understand, forecast, and optimize AI costs before they become expensive. Users describe a workload by entering the model, input and output token volumes, request counts, and growth assumptions, and TokenAtlas prices the scenario against a maintained catalog of published API rates. The cost modelling dashboard brings configured workloads into one view, while model comparison places provider and model options side by side using transparent assumptions. What-if scenario planning shows the cost impact of launching a new prompt, agent, model swap, retrieval pipeline, or traffic increase before it reaches production. Cost risk analysis identifies the workloads most sensitive to changes in volume, prompt size, or model choice, and benchmark comparisons show how a modeled model mix compares with typical AI product and infrastructure profiles.

StackSpend is a cloud and AI cost management platform that gives engineering, finance, and FinOps teams one daily view of the modern AI stack. It connects through read-only credentials to providers including AWS, Google Cloud, Azure, Snowflake, Vercel, ClickHouse Cloud, Elastic Cloud, OpenAI, Anthropic, Cursor, GitHub, Hugging Face, Grok, and Twilio, then automatically loads historical billing data and normalizes spend across services. Dashboards and explorers break costs down by provider, service, model, project, user, team, feature, and customer, helping teams understand AI COGS, cost per request, and product-level margins. Budgets and pace-to-forecast show where monthly spending is headed, while same-day anomaly detection catches unusual increases caused by traffic, prompt bugs, model changes, deployments, or individual users. Alerts and daily green, amber, or red spend signals can be delivered through Slack, Microsoft Teams, email, or webhooks.

Edgee is an AI gateway that sits between your application and large language model providers, acting as an edge intelligence layer that compresses prompts before they reach the model to reduce token usage, lower costs, and improve latency without changing your existing code. Applications call Edgee through a single OpenAI-compatible API, and Edgee applies edge-level policies such as intelligent token compression, routing, privacy controls, retries, caching, and cost governance before forwarding requests to the selected provider, including OpenAI, Anthropic, Gemini, xAI, and Mistral. Its token compression engine removes redundant input tokens while preserving semantic intent and context, achieving up to 50% input token reduction, which is especially valuable for long contexts, RAG pipelines, and multi-turn agents. Edgee enables tagging requests with custom metadata to track usage and spending by feature, team, project, or environment, and provides cost alerts when spending spikes.

LLM Gateway is a fully open source, unified API gateway that lets you route, manage, and analyze requests to any large language model provider, OpenAI, Anthropic, Gemini Enterprise Agent Platform, and more, using a single, OpenAI-compatible endpoint. It offers multi-provider support with seamless migration and integration, dynamic model orchestration that routes each request to the optimal engine, and comprehensive usage analytics to track requests, token consumption, response times, and costs in real time. Built-in performance monitoring lets you compare models’ accuracy and cost-effectiveness, while secure key management centralizes API credentials under role-based controls. You can deploy LLM Gateway on your own infrastructure under the MIT license or use the hosted service as a progressive web app, and simple integration means you only need to change your API base URL, your existing code in any language or framework (cURL, Python, TypeScript, Go, etc.)

Kunavo is a hosted AI API gateway for developers and teams that need one account, API key, and prepaid balance to access models from multiple providers. It provides OpenAI-compatible Chat Completions and a native Anthropic Messages endpoint, with model identifiers and supported features described in its live catalog and API documentation. Depending on model and route, the service handles text, image, video, and audio requests. Developers connect supported SDKs, coding agents, workflow tools, and other clients by setting a base URL, key, and model identifier. A dashboard records request usage and costs; API keys can have per-key spending limits and IP allowlists. Billing is usage-based against prepaid credit, with published rates varying by model and modality and no recurring subscription. Use cases include adding model inference to applications, coding workflows, automations, and AI-powered customer-support or data-processing features.

Cloudflare AI Gateway is an intelligent control plane for AI applications, built to connect to any model, dynamically route requests, and manage usage, billing, and logs from one unified gateway. It gives teams visibility and control over AI apps by connecting applications to AI Gateway, gathering insights on how people are using the application through analytics and logging, and controlling how the application scales with caching, rate limiting, request retries, model fallback, and more. AI Gateway helps reduce cost and latency by caching responses and reducing redundant API calls, so frequent requests can be served directly from Cloudflare’s cache instead of the original model provider. It improves reliability with dynamic controls that configure how and when model provider APIs are called based on attributes, fallbacks, latency, cost, or availability, with routing rules that can be adjusted from the dashboard or API without redeployments or downtime.

Insights by Promevo is a read-only reporting and governance platform for organizations using Gemini Enterprise and Google Cloud. It brings together reporting on Gemini license activity, custom AI agents, cloud access, and AI and cloud costs. Teams can review license use and identify idle seats; inventory custom agents and examine their models, tools, health, and errors; and attribute token or model spending to teams and projects. FinOps dashboards show cloud spending trends and budget-versus-actuals, while access reports map users to Google Cloud assets to help identify permissions that may be broader than needed. The product connects through read-only APIs and is designed not to change customer environments or read or store prompt content. Promevo offers Insights alongside gPanel.

​LiteLLM is a versatile platform designed to streamline interactions with over 100 Large Language Models (LLMs) through a unified interface. It offers both a Proxy Server (LLM Gateway) and a Python SDK, enabling developers to integrate various LLMs seamlessly into their applications. The Proxy Server facilitates centralized management, allowing for load balancing, cost tracking across projects, and consistent input/output formatting compatible with OpenAI standards. This setup supports multiple providers. It ensures robust observability by generating unique call IDs for each request, aiding in precise tracking and logging across systems. Developers can leverage pre-defined callbacks to log data using various tools. For enterprise users, LiteLLM offers advanced features like Single Sign-On (SSO), user management, and professional support through dedicated channels like Discord and Slack.

Launch production-ready apps with the LMOps stack for monitoring, model management, and more. Replace your OpenAI or other provider APIs with the Portkey endpoint. Manage prompts, engines, parameters, and versions in Portkey. Switch, test, and upgrade models with confidence! View your app performance & user level aggregate metics to optimise usage and API costs Keep your user data secure from attacks and inadvertent exposure. Get proactive alerts when things go bad. A/B test your models in the real world and deploy the best performers. We built apps on top of LLM APIs for the past 2 and a half years and realised that while building a PoC took a weekend, taking it to production & managing it was a pain! We're building Portkey to help you succeed in deploying large language models APIs in your applications. Regardless of you trying Portkey, we're always happy to help!

CloudEagle.ai is an AI-powered SaaS Management, AI Governance, and Identity Governance platform that helps organizations discover, govern, and optimize every SaaS and AI application across the enterprise, including applications outside traditional SSO or IT visibility. CloudEagle enables teams to detect Shadow IT and Shadow AI, automate provisioning and deprovisioning beyond the IDP, manage security posture, track NHIs, govern AI application usage, run continuous access reviews, optimize licenses, and streamline renewals using real usage and benchmarking insights. With 500+ integrations and AI-driven contract and usage intelligence, CloudEagle centralizes application access, spend, contracts, renewals, and compliance visibility into a single governance platform.

Tokonomics is an AI cost metering proxy that sits between your app and any LLM provider. One URL change gives you real-time cost tracking, budget alerts, and hard spending caps across OpenAI, Anthropic, DeepSeek, Google Gemini, Mistral, Groq, and more. How it works: Replace your LLM base URL with Tokonomics, keep your existing code. Every API call is logged with token counts, cost (8-decimal USD precision), latency, and custom tags for per-team or per-feature attribution. Key features: - Budget alerts via email, Slack, or Teams at configurable thresholds - Hard spending caps that block requests when monthly budget is exceeded - Analytics dashboard with spend-by-model, daily trends, and cost optimization reports - BYOK (Bring Your Own Keys) with AES-256 encryption - Rate limiting per API key - Works with any language or HTTP client (PHP, Python, Node.js, Go, Ruby)

Concentrate AI is the LLM gateway for fast-growing teams, one API for every major LLM provider, with routing, spend, logs, and controls in one place. It helps teams securely access, use, and manage AI through a single API, so every request can find the smarter, faster, cheaper model for the workflow or task. Teams can access 130+ models, benchmark speed, quality, and cost, and route each workload to the best fit without wiring separate provider APIs into every environment. Support bots, coding agents, internal tools, chat, and batch jobs do not need the same model or the same route, so Concentrate lets teams pick a model slug, limit allowed providers, sort by live latency, use fallbacks, and reroute traffic when a provider slows down, errors, or hits a rate limit. It also gives engineering, finance, security, and leadership a shared view of AI usage with request-level logs, models, provider, duration, token counts, spend, error rates, alerts, and exports.

Braintrust is an AI observability and evaluation platform designed to help teams build, monitor, and improve AI systems in production. It enables users to capture and inspect real-time traces of AI interactions, including prompts, responses, and tool usage. The platform allows teams to measure performance using automated and human evaluations to ensure output quality. Braintrust helps identify issues such as hallucinations, regressions, and performance drops before they impact users. It supports prompt and model comparisons, making it easier to optimize AI workflows over time. With scalable trace ingestion and real-time monitoring, teams gain full visibility into how their AI systems behave. The platform integrates with multiple programming languages and tools, allowing developers to work within their existing tech stack. Overall, Braintrust provides a comprehensive solution for maintaining and improving AI quality at scale.

Kilo Gateway is a universal AI inference gateway that routes LLM requests to any provider through one standardized endpoint, giving developers access to hundreds of hosted and open models without rewriting their applications for each provider. It provides unified access to models from Anthropic, OpenAI, Mistral, and other providers, while also supporting bring-your-own-key configurations that let teams connect existing provider credentials through centralized infrastructure. The gateway is compatible with standard AI SDKs, making it possible to switch providers while keeping the same integration surface. Its infrastructure handles routing complexity and load balancing across direct providers and external gateways to improve availability and resilience. Auto Model can route each request to the best available model while keeping routing decisions, model behavior, and usage visible and controllable.

Jectora is a hosted AI gateway that provides a single OpenAI-compatible API for sending requests to models from multiple providers. Developers can use the OpenAI JavaScript or Python SDK, change the configured base URL and model ID, and call supported chat, response, and embedding endpoints. The model catalog lists available models, capabilities, usage prices, and service-health information. Jectora routes requests among available model providers and offers API-key controls for model access, spending, expiration, and source IP. Organization features let teams manage shared access, usage, balances, and billing. The service uses prepaid, pay-as-you-go credits, with charges based on the selected model and its metered input, output, or media units. It is intended for developers and organizations building AI-enabled applications that need a common API and centralized model usage management.

Lunar.dev is an AI gateway and API consumption management platform that gives engineering teams a single, unified control plane to monitor, govern, secure, and optimize all outbound API and AI agent traffic, including calls to large language models, Model Context Protocol tools, and third-party services, across distributed applications and workflows. It provides real-time visibility into usage, latency, errors, and costs so teams can observe every model, API, and agent interaction live, and apply policy enforcement such as role-based access control, rate limiting, quotas, and cost guards to maintain security and compliance while preventing overuse or unexpected bills. Lunar.dev's AI Gateway centralizes control of outbound API traffic with identity-aware routing, traffic inspection, data redaction, and governance, while its MCPX gateway consolidates multiple MCP servers under one secure endpoint with full observability and permission management for AI tools.

Bifrost is a high-performance AI gateway that unifies access to 20+ providers OpenAI, Anthropic, AWS, Bedrock, Google Vertex, Azure, and more, through a unified API. Deploy in seconds with zero configuration and get automatic failover, load balancing, semantic caching, and enterprise-grade governance. In sustained benchmarks at 5,000 requests per second, Bifrost adds only 11 µs of overhead per request.

Tikway is a hosted AI model gateway and API for developers building applications that use multiple model providers. It provides a unified API key and supports request formats compatible with OpenAI, Anthropic, and Gemini protocols, allowing clients to call listed models through Tikway. Developers can browse the model catalog to compare model pricing, capabilities, and supported parameters, and use online documentation for setup and request examples. The service uses usage-based billing, with charges varying by the selected model and usage. Tikway is intended for independent developers, makers, and teams integrating generative AI into applications. Model availability, capabilities, and supported parameters vary by model; developers can consult the catalog before choosing an endpoint.

ML Junction gives developers a consistent API for working with multiple AI model providers. It supports OpenAI and Anthropic SDK compatibility, capability-aware routing, retries and fallback, and request-level cost and privacy controls. Its per-request receipts explain which route was selected, the alternatives considered, the policies enforced and the final cost. ML Junction also supports model evaluations, agent observability, and a native LangChain integration. Pricing is usage-based on prepaid credits: provider rates at cost plus one routing fee, or bring your own provider keys. It is designed for teams building AI applications.

PortaAIM is an inference service delivery platform that combines PortaBilling with an AI gateway to authorize, meter, and settle AI usage in real time. It checks the customer, pricing rules, and balance before a request runs, reserves funds against the worst-case cost, then meters actual usage in tokens, images, audio, tool calls, or other units and releases any unused balance. Pricing, tokens, credits, and bundles are configured without code changes, and resellers are settled within the same system. PortaAIM routes requests to the lowest-cost capable model, offers a white-label customer portal, and runs across multiple sites for continuity. It supports full API and MCP access, and includes guardrail modules for frameworks such as the EU AI Act. Available as perpetual license or pay-as-you-go, self-hosted or hosted by PortaOne — built on 25+ years of real-time billing experience for nearly 500 operators worldwide.

APIXO is a performance-first AI API platform that delivers enterprise-grade stability at a lower cost, with unified routing, automatic failover, and transparent usage reporting. What APIXO is APIXO gives teams one API to access a wide range of AI models while keeping reliability and cost under control. It automatically routes requests to the best-value provider using health, latency, and pricing signals, so developers can focus on product delivery instead of infrastructure tuning. Why it matters Modern AI stacks are fragmented, expensive, and operationally fragile. APIXO reduces integration overhead and cost volatility while improving reliability—making it easier to ship AI features that stay fast and available as usage grows. Key capabilities Unified schema across models for simpler integration Automatic failover to keep services running during provider issues Usage reports for clear cost visibility and accountability

Kosmoy is an AI governance and management platform for enterprise AI in production. It connects four layers of control: inventory, monitoring, governance and action control. Register AI systems, models, agents and MCP servers with owners and risk assessments. Monitor usage, costs and behaviour across applications. Apply access controls, guardrails, routing and logging to LLM, MCP and agent-to-agent traffic through an AI Gateway. Use Action Capsule to isolate autonomous agent actions with runtime controls and a kill switch. Deploy in your own Kubernetes environment, in the cloud or on premises. Built for AI platform, security, risk and compliance teams that need shared operational evidence and control.

Cheaper Inference is an OpenAI-compatible API gateway that provides access to AI models from multiple providers through a single API key, without requiring users to change their request format. Developers can switch by replacing the provider base URL and API key while keeping the same model, messages, tools, streaming settings, and response handling. It supports text and image models, vision-capable chat requests, streaming, prompt caching, reasoning controls, and temporary image uploads for larger vision payloads. Models are selected per request, and the catalog can be filtered by type, vision, reasoning, streaming, or provider. Automatic retries handle network and provider failures, while eligible fallback routes can be tried before a request fails. Every request is visible in History, giving teams a record of request volume, token usage, and operational activity.

FastRouter is a unified API gateway that enables AI applications to access many large language, image, and audio models (like GPT-5, Claude 4 Opus, Gemini 2.5 Pro, Grok 4, etc.) through a single OpenAI-compatible endpoint. It features automatic routing, which dynamically picks the optimal model per request based on factors like cost, latency, and output quality. It supports massive scale (no imposed QPS limits) and ensures high availability via instant failover across model providers. FastRouter also includes cost control and governance tools to set budgets, rate limits, and model permissions per API key or project, and it delivers real-time analytics on token usage, request counts, and spending trends. The integration process is minimal; you simply swap your OpenAI base URL to FastRouter’s endpoint and configure preferences in the dashboard; the routing, optimization, and failover functions then run transparently.

Shifter is a residential proxy network for collecting public web data at a lower cost than major providers. It has operated since 2012. In our published benchmarks we tested Shifter against the biggest names in residential proxies, using the same method for everyone. Shifter costs less than half of what most of them charge per GB, gives you a comparable or bigger IP pool, and has lower latencies, often by 50% or more. The network covers 205M+ residential IPs in 195+ countries through one gateway endpoint over HTTP, HTTPS or SOCKS5. Country, state, city and ASN targeting is set in the proxy username. Sessions rotate on every request or stay on one IP for a set time, concurrency is uncapped, and usage is billed per GB. A dashboard shows live bandwidth and requests, team workspaces, a usage API and guides for Python, Node.js, Go, PHP, Selenium, Playwright and Puppeteer.

Router is an LLM gateway built to reduce inference costs by matching each request to the lowest-cost model that still meets performance needs. It provides one endpoint and one API key for accessing multiple closed and open-source AI models from providers such as OpenAI, Anthropic, Grok, Fireworks, and others, helping developers avoid wiring applications to providers one at a time. Requests go through Router first, where usage, model, provider, and cost can be tracked before eligible workloads are routed to a more efficient option when quality will not be affected. Router Strategies let developers define cost and performance priorities for different types of requests or use benchmarked defaults based on real production workloads. It responds to live latency, availability, failures, and rate limits, and eligible requests can be moved to another available model when a provider cannot serve them.

CloudQuell is a cost management platform built for teams whose spend no longer sits in one place. It ingests AWS billing data daily through a scoped read-only cross-account IAM role, and connects OpenAI, Anthropic, and Snowflake from the Integrations page. On top of that it supports cost centers, allocation rules, tags, and multi-account cost views, so spend can be attributed to the team or product that caused it. Anomaly detection, budgets, and alert delivery flag problems as they develop, and ranked savings recommendations show where the money is. Every tier receives a weekly accrued-cost recap email.

Cloudgov.ai is an agentic AI FinOps platform for continuous cost and policy governance across cloud, multicloud, data, container, and AI environments. It brings AWS, Azure, Google Cloud, Oracle Cloud, Snowflake, Databricks, Kubernetes, OpenAI, Anthropic, and Gemini into one control plane, giving teams a live view of cost, allocation, policy, and risk. Continuous Multicloud Observability connects accounts, analyzes historical spending, filters costs by region, account, and service, and forecasts future spend from history. AI-driven insights identify waste and optimization opportunities, while anomaly detection highlights unexpected spending surges and their financial impact. Ready-to-use Infrastructure as Code remediation snippets help engineering teams apply recommended changes, and Jira integration turns insights and anomalies into assignable work.