Alternatives to Span-01
Compare Span-01 alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Span-01 in 2026. Compare features, ratings, user reviews, pricing, and more from Span-01 competitors and alternatives in order to make an informed decision for your business.
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1
Claude Sonnet 5
Anthropic
Claude Sonnet 5 is Anthropic's latest AI model, designed to deliver stronger agentic capabilities for coding, reasoning, tool use, and knowledge work while maintaining the efficiency of the Sonnet family. The model can independently plan tasks, use external tools such as browsers and terminals, and complete complex workflows that previously required larger AI models. Sonnet 5 significantly improves upon Claude Sonnet 4.6 with better reasoning, coding performance, reduced hallucinations, stronger safety behavior, and more effective autonomous task execution. It is available across Claude plans and through the Claude API with OpenAI-style developer access for application integration. Anthropic also introduced lower introductory API pricing, making Sonnet 5 a cost-effective option for developers building AI-powered products. By combining advanced agentic capabilities with improved safety and competitive pricing, Claude Sonnet 5 helps developers build more capable AI applications.Starting Price: $2 per 1M tokens (input) -
2
Claude Opus 4.8
Anthropic
Claude Opus 4.8 is a powerful AI model from Anthropic designed to deliver stronger coding, reasoning, agentic workflows, and advanced collaboration capabilities for developers, enterprises, and AI-powered productivity tasks. The model builds on Claude Opus 4.7 with improvements across coding benchmarks, practical knowledge work, alignment, and reliability while maintaining the same pricing structure. Claude Opus 4.8 introduces enhanced honesty and reasoning behavior, making it less likely to generate unsupported claims or overlook flaws during complex tasks such as software development and agent execution. The release also includes new features such as effort control settings, fast mode for lower-cost high-speed processing, and dynamic workflows in Claude Code that allow the system to coordinate hundreds of parallel subagents for large-scale tasks.Starting Price: $5 per 1M (input) -
3
GPT-5.5-Cyber
OpenAI
GPT-5.5-Cyber is an advanced cybersecurity-focused AI model designed for verified defenders working on authorized security research, vulnerability discovery, and remediation. The model pairs stronger cyber capabilities with more permissive behavior for specialized workflows that require deep analysis across complex software environments. It can help identify security-relevant components, trace vulnerable code paths, validate likely issues in controlled settings, develop and test patches, and prepare evidence for human review. GPT-5.5-Cyber is built to support the full remediation loop rather than simply generating more findings. The model shows stronger benchmark performance than GPT-5.5 on CyberGym, ExploitGym, and SEC-bench Pro, reflecting improvements in vulnerability reproduction, exploit reasoning, and long-horizon security tasks. GPT-5.5-Cyber is intended for advanced, authorized cybersecurity work with verification, monitoring, scoped controls, and review. -
4
Jev
TypeSafe AI
Jev is a System One Model from TypeSafe AI designed for fast, structured decision-making inside software and automated workflows. Unlike conventional large language models that generate free-form text token by token, Jev accepts unstructured state and returns predefined type-safe values with calibrated probabilities and confidence scores. The model uses a parallel sampling architecture and a training approach called Reinforcement Learning for Calibrated Decisions, which is optimized for producing reliable probabilistic decisions rather than conversational responses. Jev is intended for tasks such as classification, routing, scoring, extraction, branching logic, verification, guardrails, and other AI-powered workflows where structured outputs can plug directly into application code. TypeSafe reports end-to-end response times ranging from about 70 to 500 milliseconds and says Jev can be substantially faster and more efficient than frontier language models on System One-style workloads.Starting Price: Input: $0.042 / 1M tokens -
5
OpenAI Decisions API
OpenAI
The Decisions API turns text and images into typed decisions an application can use, evaluating inputs about 10x faster than the Responses API. Developers provide a model, shared input evidence, and one or more questions describing what should be evaluated, and the API returns structured answers that can be used to classify content, route requests, prioritize work, or make threshold-based decisions. It supports three question types: predicate checks whether a condition is true and returns a probability from 0 to 1; choice selects one value from a fixed set and returns option probabilities and confidence; and score evaluates an input against ordered rubric levels and returns a probability-weighted score. Inputs can be plain text or user messages containing both text and images, allowing applications to evaluate cases such as visible product damage, request intent, document type, issue severity, or answer relevance. -
6
Perplexity Decider
Perplexity
Perplexity Decider is a 27B decision model fine-tuned from Qwen3.8-27B for structured classification and multimodal decision tasks. Rather than generating unrestricted text, it takes an input state and a typed decision schema, then returns the selected result together with calibrated probabilities. The model supports choice decisions for selecting among predefined categories and noul decisions for estimating the probability of a yes-or-no condition. It can also accept images alongside text, allowing applications to make decisions from screenshots or other visual inputs. Example workflows include routing a support request to billing, technical support, or sales, and estimating whether a message expresses urgency. Perplexity reports an overall accuracy of 85.71% across 11 benchmarks, including FinancialPhraseBank, RAGTruth, JudgeBench, TabFact, ContractNLI, Circa, Belebele, and TruthfulQA binary.Starting Price: Free -
7
Perplexity Decisions API
Perplexity
Perplexity’s Decisions API is built for fast, structured decisions that software can use directly. Instead of generating free-form text, it reads natural-language text, JSON, or images as shared state and returns typed answers with probabilities. Developers can attach one or more named questions to the same input and choose among three question types. noul handles yes-or-no judgments and returns the probability of yes from 0 to 1. choice selects from developer-defined options and returns the most likely option, the probability of every option, and a confidence score. score evaluates content against an ordered rubric and returns a probability-weighted score, the full probability distribution, and confidence. The API is designed for tasks such as classification, routing, grading against a rubric, severity assessment, sentiment analysis, or any decision that can be compared against a threshold. -
8
Microsoft-Decision-1
Microsoft
Microsoft-Decision-1 is a fast decision-scoring model built for structured outputs that software can immediately act on. It is designed for routing, classification, prioritization, verification, and workflow control across applications, agents, and automated systems. The model is post-trained from Qwen3.5-9B for single-pass decision scoring and, when given a fixed set of answer options, returns a calibrated probability for each one. It supports yes/no, multiple-choice, rating, and rubric-based grading of AI responses and agent actions through a structured API. Microsoft evaluated the model across 36 benchmarks covering routing, ranking, long context, multilingual and out-of-distribution tasks, reasoning, and safety, and reports that it achieved the highest accuracy in that comparison while also delivering very low latency. The model is also designed for robustness, with testing focused on keeping decisions stable when prompts are paraphrased.Starting Price: $0.042 per 1M tokens -
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Laya AI
Laya AI
Laya AI is an open source family of non-autoregressive System 1 decision models built for fast, typed decisions instead of free-form text generation. Given a state and structured questions, Laya evaluates them in a single forward pass and returns outputs that application code can use directly. It supports three decision types: choice, which selects among named options and returns probabilities; score, which places an input on an ordered rubric such as urgency or severity; and noul, which estimates the probability that a proposition is true. Laya is designed for workloads such as agent and model routing, support ticket triage, guardrails, intent classification, risk and fraud scoring, workflow escalation, RAG filtering, moderation, and phishing detection. It can route requests by department, score urgency, flag refund or churn signals, detect policy or abuse conditions, and escalate uncertain cases to a larger model or human reviewer.Starting Price: Free -
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Liquid d1
Liquid AI
d1 is Liquid AI’s first decision model, built to answer structured questions about a situation with probabilities rather than generating text. It takes unstructured data such as text, images, or both, along with one or more questions, processes them in a single forward pass, and returns probabilities without generating output tokens. d1 supports three question types: noul for yes-or-no decisions, choice for selecting among labels with a probability for each option, and score for placing an input on an ordered scale using probability-weighted levels. Multiple questions can be evaluated against the same state in one request, making it useful for real-time decision workflows. Liquid AI demonstrates d1 across applications such as filtering support tickets, browsing code repositories, organizing documents into smart folders, operating websites, compacting agent context, inspecting industrial parts, and playing games from screenshots.Starting Price: Free -
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Tev1
Together AI
Tev1 is a fast decision and classification model built by Together AI and fine-tuned from Qwen3.5 4B. It is designed around a simple interface: provide a piece of state, a question, and 2 to 24 labeled options, and the model returns exactly one letter that software can map back to a semantic key. This structure supports production tasks such as routing support tickets, evaluating automated returns, applying policy rules, rating sentiment, categorizing documents, and inserting deterministic decision points into larger agent pipelines. The model has 4.7B parameters, a 32.8K-token context window, and runs with non-thinking generation for fast, predictable classification. It was trained on 37,840 examples spanning intent detection, boolean comprehension, sentiment, policy application, routing, news categories, and research-paper taxonomy. Recommended settings use temperature 0 and a very small output budget so responses remain stable and easy to parse.Starting Price: $0.04 per 1M tokens -
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SubQ Snap
Subquadratic
SubQ Snap is a long-context decision model for enterprise and agentic workflows, built to make structured decisions quickly over large amounts of information. It handles tasks such as choosing which files are relevant, selecting the next tool, checking whether a result passes, deciding whether a workflow should continue, and determining when a frontier model is needed. Instead of generating open-ended text, Snap takes context, a question, and a finite set of allowed answers, then returns a probability distribution over those answers. It supports up to 384K tokens of mixed text and image context, allowing it to reason over codebases, contracts, interaction histories, logs, screenshots, charts, diagrams, and scanned pages in a single call. Powered by Subquadratic Sparse Attention, Snap reduces the cost of reading long inputs while a decision head maps contextual representations directly to admissible answers without autoregressive generation.Starting Price: $0.05 per million input tokens -
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Strands Decider
Strands Labs
Strands Decider is a small, fast decision model, or System One model, built for agentic AI workflows. Unlike an LLM that generates arbitrary text, it chooses between predefined options, answers yes-or-no questions, or rates inputs on an ordered scale. Each decision includes calibrated confidence, making it possible to send uncertain cases to a larger model or a human while handling routine decisions quickly. The first model in the family, strands-decider-2B, has 1.9 billion parameters and is based on a Qwen3.5-2B-Base torso with its language-modeling head replaced by a lightweight pointer head. It evaluates answers in one forward pass without text generation or a decoding loop. Supported decision types include choice, noul for probability-of-true questions, and score for ordered scales. The model is designed for tasks such as model routing, tool selection, tool-argument checking, request triage, grounding and safety guardrails, policy classification, and low-cost evaluation.Starting Price: Free -
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Solar Decide
Upstage
Solar Decide is Upstage’s structured decision model, serving as a System One endpoint on Solar Mini 4. Instead of generating prose, it takes a state together with one or more typed questions and returns a choice, score, or yes/no answer with calibrated probabilities taken directly from the model. Each question is handled in a single forward pass, making the model suited to fast classification, routing, policy checks, prioritization, and other structured decision tasks. The choice type selects among predefined options and returns the selected option, individual probabilities, and confidence. The score type evaluates an input against ordered criteria and returns a probability-weighted score, legend, probability distribution, and confidence. The noul type returns the probability that a yes-or-no proposition is true. Multiple questions can share the same state in one request, and the state can be provided as text or any JSON value serialized into the prompt.Starting Price: $0.1 per 1M tokens -
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Clef
Cloudflare
Clef is a 27B multimodal decision model that turns a state and a schema of typed questions into structured decisions. It can read state information as text, JSON, images, or video and return a probability for every allowed option of every question. The model supports a 65,536-token context window and vision inputs, allowing developers to evaluate both structured application data and visual content in the same decision workflow. Clef supports three typed question formats: noul for estimating the probability that a proposition is true, choice for selecting among predefined options and returning per-option probabilities, and score for evaluating an input against ordered criteria and producing a probability-weighted score. Applications can submit between 1 and 64 questions in a single request, with answers returned under the same question IDs. -
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Clef-flash
Cloudflare
Clef-flash is a fast 9B multimodal decision model that turns a state and a schema of typed questions into structured decisions. It can read state information as text, JSON, images, or video and returns a probability for every allowed option of every question. The model supports a 24,576-token context window and vision input, allowing developers to evaluate structured application data and visual content within the same workflow. Clef Flash supports three typed question formats: noul for estimating the probability that a proposition is true, choice for selecting among predefined options with per-option probabilities, and score for evaluating an input against ordered criteria and returning a probability-weighted score. Applications can submit from 1 to 64 questions in a single request, with answers returned under the same question IDs. Example uses include deciding whether a support request is urgent, routing it to the correct team, and scoring the severity of customer impact. -
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Semantic UI
Semantic
Semantic UI treats words and classes as exchangeable concepts. Classes use syntax from natural languages like noun/modifier relationships, word order, and plurality to link concepts intuitively. Semantic uses simple phrases called behaviors that trigger functionality. Any arbitrary decision in a component is included as a setting that developers can modify. Performance logging lets you track down bottlenecks without digging through stack traces. Semantic comes equipped with an intuitive inheritance system and high level theming variables that let you have complete design freedom. Definitions aren't limited to just buttons on a page. Semantic's components allow several distinct types of definitions: elements, collections, views, modules and behaviors which cover the gamut of interface design. -
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Openlayer
Openlayer
Openlayer is an enterprise AI governance platform that helps organizations discover, test, monitor, secure, and control AI systems from development through production. The platform brings AI evaluation, observability, governance, guardrails, compliance, gateway enforcement, and cost controls together in one place. Teams can evaluate LLMs, agents, RAG applications, and traditional ML systems using 175+ out-of-the-box evaluations across quality, safety, security, performance, cost, fairness, and compliance. In production, Openlayer provides end-to-end tracing and continuous monitoring of AI behavior, including prompts, responses, tool calls, agent workflows, latency, and cost. Real-time guardrails can detect and block issues such as prompt injection, jailbreak attempts, PII or PHI exposure, and other unsafe behavior before it reaches a model or end user. Openlayer Governance connects organizational AI policies to technical controls. Teams can maintain an AI inventory, assign own -
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Qwen3-Max
Alibaba
Qwen3-Max is Alibaba’s latest trillion-parameter large language model, designed to push performance in agentic tasks, coding, reasoning, and long-context processing. It is built atop the Qwen3 family and benefits from the architectural, training, and inference advances introduced there; mixing thinker and non-thinker modes, a “thinking budget” mechanism, and support for dynamic mode switching based on complexity. The model reportedly processes extremely long inputs (hundreds of thousands of tokens), supports tool invocation, and exhibits strong performance on benchmarks in coding, multi-step reasoning, and agent benchmarks (e.g., Tau2-Bench). While its initial variant emphasizes instruction following (non-thinking mode), Alibaba plans to bring reasoning capabilities online to enable autonomous agent behavior. Qwen3-Max inherits multilingual support and extensive pretraining on trillions of tokens, and it is delivered via API interfaces compatible with OpenAI-style functions.Starting Price: Free -
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Creyos Health
Creyos Health
Creyos Health is a scientifically validated, clinician-trusted, and patient-friendly platform that combines proprietary online tasks with digitized behavioral health questionnaires. Healthcare providers receive a complete, research-backed solution for detecting cognitive impairment and behavioral health conditions that is flexible, engaging, accurate, and reimbursable. The platform is backed by over 30 years of scientific research and has been used in more than 400 peer-reviewed studies. Over 10,000 healthcare providers trust Creyos in their practices to evaluate patient cognition and behavioral health. Creyos offers a suite of 12 online cognitive tasks, each taking only 2-3 minutes to complete, designed to detect even mild cognitive impairment. These tasks assess key domains such as short-term memory, reasoning, concentration, and verbal ability. The platform also includes digitized behavioral health screeners that automatically tabulate results, providing quick insights. -
21
Store Visualizer
VTales graphics
The very high-definition image quality offered by Store Visualizer will meet both your aesthetic needs and your performance requirements. Get a realistic picture of a wide range of materials such as cardboard, plasticized and laminated surfaces, glass, aluminum, etc. View all types of shapes, from very simple shapes to highly complex ones. Realtime ray tracing & refraction and interactive liquid simulation and animation. Simulate lighting and shadowing for a highly realistic representation of your products. Add exceptional high-resolution graphics on packages (labels or cardboard boxes) and color reproduction. Easily focus on any object without losing image quality or detail. The consumer insights module allows you to understand and hyper-target your ideal consumer through eye-tracking, heat maps, and visual behavior analysis in a 3D virtual store that you create. Create or upload a planogram in Store Visualizer to create any 3D virtual store and display your product. -
22
GLM-5.1
Z.ai
GLM-5.1 is the latest iteration of Z.ai’s GLM series, designed as a frontier-level, agent-oriented AI model optimized for coding, reasoning, and long-horizon workflows. It builds on the GLM-5 architecture, which uses a Mixture-of-Experts (MoE) design to deliver high performance while keeping inference costs efficient, and is part of a broader push toward open-weight, developer-accessible models. A core focus of GLM-5.1 is enabling agentic behavior, meaning it can plan, execute, and iterate across multi-step tasks rather than simply responding to single prompts. It is specifically designed to handle complex workflows such as debugging code, navigating repositories, and executing chained operations with sustained context. Compared to earlier models, GLM-5.1 improves reliability in long interactions, maintaining coherence across extended sessions and reducing breakdowns in multi-step reasoning.Starting Price: Free -
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TraceRoot.AI
TraceRoot.AI
TraceRoot.AI is an open source, AI-native observability and debugging platform designed to help engineering teams resolve production issues faster. It consolidates telemetry into a single correlated execution tree that provides causal context for failures. AI agents operate over this structured view to summarize issues, pinpoint likely root causes, and even suggest actionable fixes or draft GitHub issues and pull requests. It offers interactive trace exploration with zoomable log clusters, span and latency views, and code-linked insights. Lightweight SDKs for Python and TypeScript enable seamless instrumentation using OpenTelemetry, with support for both self-hosted and cloud deployment. Human-in-the-loop interaction is central: developers can guide reasoning by selecting relevant spans or logs, then verify agent reasoning through traceable context.Starting Price: $49 per month -
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GLM-4.7-Flash
Z.ai
GLM-4.7 Flash is a lightweight variant of GLM-4.7, Z.ai’s flagship large language model designed for advanced coding, reasoning, and multi-step task execution with strong agentic performance and a very large context window. It is an MoE-based model optimized for efficient inference that balances performance and resource use, enabling deployment on local machines with moderate memory requirements while maintaining deep reasoning, coding, and agentic task abilities. GLM-4.7 itself advances over earlier generations with enhanced programming capabilities, stable multi-step reasoning, context preservation across turns, and improved tool-calling workflows, and supports very long context lengths (up to ~200 K tokens) for complex tasks that span large inputs or outputs. The Flash variant retains many of these strengths in a smaller footprint, offering competitive benchmark performance in coding and reasoning tasks for models in its size class.Starting Price: Free -
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Kimi K2 Thinking
Moonshot AI
Kimi K2 Thinking is an advanced open source reasoning model developed by Moonshot AI, designed specifically for long-horizon, multi-step workflows where the system interleaves chain-of-thought processes with tool invocation across hundreds of sequential tasks. The model uses a mixture-of-experts architecture with a total of 1 trillion parameters, yet only about 32 billion parameters are activated per inference pass, optimizing efficiency while maintaining vast capacity. It supports a context window of up to 256,000 tokens, enabling the handling of extremely long inputs and reasoning chains without losing coherence. Native INT4 quantization is built in, which reduces inference latency and memory usage without performance degradation. Kimi K2 Thinking is explicitly built for agentic workflows; it can autonomously call external tools, manage sequential logic steps (up to and typically between 200-300 tool calls in a single chain), and maintain consistent reasoning.Starting Price: Free -
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zope.interface
Python Software Foundation
This package is intended to be independently reusable in any Python project. It is maintained by the Zope Toolkit project. This package provides an implementation of “object interfaces” for Python. Interfaces are a mechanism for labeling objects as conforming to a given API or contract. So, this package can be considered as an implementation of the Design By Contract methodology support in Python. Interfaces are objects that specify (document) the external behavior of objects that “provide” them. An interface specifies behavior through informal documentation in a doc string, attribute definitions, and invariants, which are conditions that must hold for objects that provide the interface. Attribute definitions specify specific attributes. They define the attribute name and provide documentation and constraints of attribute values. Attribute definitions can take a number of forms.Starting Price: Free -
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SWE-1.6
Cognition
SWE-1.6 is an engineering–focused AI model developed by Cognition and integrated into the Devin (Windsurf) environment, designed to optimize both raw intelligence and what the company calls “model UX,” or the overall feel and efficiency of interacting with an AI agent. It represents a new iteration in the SWE model family, improving performance on benchmarks such as SWE-Bench Pro by over 10% compared to SWE-1.5 while maintaining similar underlying capabilities. It was trained from scratch to jointly improve reasoning quality and user experience, addressing issues observed in earlier versions such as overthinking simple problems, taking too many steps, looping in repetitive reasoning, and relying excessively on terminal commands instead of specialized tools. SWE-1.6 introduces behavioral improvements such as more frequent parallel tool usage, faster context retrieval, and reduced need for user input, resulting in smoother and more efficient workflows. -
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Arm DDT
Arm
Arm DDT is the number one server and HPC debugger in research, industry, and academia for software engineers and scientists developing C++, C, Fortran parallel and threaded applications on CPUs, GPUs, Intel, and Arm. Arm DDT is trusted as a powerful tool for the automatic detection of memory bugs and divergent behavior to achieve lightning-fast performance at all scales. Cross-platform for multiple servers and HPC architectures. Native parallel debugging of Python applications. Has market-leading memory debugging. Outstanding C++ debugging support. Complete Fortran debugging support. Has an offline mode for debugging non-interactively. Handles and visualizes huge data sets. Arm DDT is a powerful parallel debugger, available standalone or as part of the Arm Forge debug and profile suite. Its intuitive graphical interface provides automatic detection of memory bugs and divergent behavior at all scales. -
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SEAP
XTN Cognitive Security
SEAP® for Mobile is an SDK integrated into the customer’s app and doesn’t require any special permission. SEAP® for Web is JavaScript based, executed in the web browser application environment and does not require the installation of any agent. SEAP® detects malware activity both in mobile and web apps. Some examples of the malware threats monitored include man-in-the-browser and man-in-the-app-attacks, RAT, web injections, overlay attacks, SMS grabbing, memory tampering, and Injection attacks. SEAP® detects and reports technological threats in the device such as jailbreaking and rooting, reverse engineering attempts, binary tampering, repacking. Countermeasures to some of these device conditions can be activated in the app relying on the dedicated RASP APIs. SEAP® detects fraudulent activity taking control of existing user accounts, relying on behavioral biometrics checks and device identity authentication. -
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MiMo-V2-Flash
Xiaomi Technology
MiMo-V2-Flash is an open weight large language model developed by Xiaomi based on a Mixture-of-Experts (MoE) architecture that blends high performance with inference efficiency. It has 309 billion total parameters but activates only 15 billion active parameters per inference, letting it balance reasoning quality and computational efficiency while supporting extremely long context handling, for tasks like long-document understanding, code generation, and multi-step agent workflows. It incorporates a hybrid attention mechanism that interleaves sliding-window and global attention layers to reduce memory usage and maintain long-range comprehension, and it uses a Multi-Token Prediction (MTP) design that accelerates inference by processing batches of tokens in parallel. MiMo-V2-Flash delivers very fast generation speeds (up to ~150 tokens/second) and is optimized for agentic applications requiring sustained reasoning and multi-turn interactions.Starting Price: Free -
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Ling Studio
Ant Group
Ling Studio is Ant Ling’s online environment for exploring the infinite possibilities of AI and testing the core capabilities of the Ling model family. It gives users a direct place to try Ant Ling models before building with them through API access, making it easier to experience multi-turn reasoning, long-context processing, multimodal generation, and model behavior in a practical chat workspace. It connects to Ant Ling’s high-performance model family for text, coding, reasoning, and multimodal tasks. Ling models are general-purpose LLMs built on Mixture of Experts architecture, balancing high parameter scale with low activation cost and supporting conversation, text generation, and content creation. Ring models specialize in deep reasoning and cognitive capabilities, with strong performance in math, programming, and comprehensive reasoning benchmarks. -
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DeepSeek-V3.2
DeepSeek
DeepSeek-V3.2 is a next-generation open large language model designed for efficient reasoning, complex problem solving, and advanced agentic behavior. It introduces DeepSeek Sparse Attention (DSA), a long-context attention mechanism that dramatically reduces computation while preserving performance. The model is trained with a scalable reinforcement learning framework, allowing it to achieve results competitive with GPT-5 and even surpass it in its Speciale variant. DeepSeek-V3.2 also includes a large-scale agent task synthesis pipeline that generates structured reasoning and tool-use demonstrations for post-training. The model features an updated chat template with new tool-calling logic and the optional developer role for agent workflows. With gold-medal performance in the IMO and IOI 2025 competitions, DeepSeek-V3.2 demonstrates elite reasoning capabilities for both research and applied AI scenarios.Starting Price: Free -
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Huawei Database Security Service (DBSS)
Huawei Cloud
Database Security Service (DBSS) uses machine learning and big data technologies to protect your databases on the cloud, intelligently auditing them and detecting risky behaviors like SQL injection. Purchase and get started with DBSS quickly without the need to manually install the service or adapt your database configurations. DBSS complies with the HIPAA, SOX, and PCI DSS, meeting your auditing requirements. Take advantage of algorithm models to detect SQL injection and abnormal behaviors, fast and accurate. DBSS works in bypass mode, having zero impact on your business. A wide range of policies are available to help you detect SQL injection and audit database behaviors. Monitor databases and detect anomalies in performance, data, and user actions. Audit reports are customized for different scenarios (pre-event and post-event) and roles (common users and administrators). Perform comprehensive database audit to comply with laws and regulation. -
34
Vivgrid
Vivgrid
Vivgrid is a development platform for AI agents that emphasizes observability, debugging, safety, and global deployment infrastructure. It gives you full visibility into agent behavior, logging prompts, memory fetches, tool usage, and reasoning chains, letting developers trace where things break or deviate. You can test, evaluate, and enforce safety policies (like refusal rules or filters), and incorporate human-in-the-loop checks before going live. Vivgrid supports the orchestration of multi-agent systems with stateful memory, routing tasks dynamically across agent workflows. On the deployment side, it operates a globally distributed inference network to ensure low-latency (sub-50 ms) execution and exposes metrics like latency, cost, and usage in real time. It aims to simplify shipping resilient AI systems by combining debugging, evaluation, safety, and deployment into one stack, so you're not stitching together observability, infrastructure, and orchestration.Starting Price: $25 per month -
35
BrightInfo
BrightInfo
BrightInfo's self-learning JARGON™ algorithm analyzes your website pages and learns your content assets. When visitors arrive to your website, JARGON™ tracks individual behavior, considers it against crowd behavior, and presents each visitor with the most relevant content from your existing content pool - all in real time, all fully automated. Most marketers are delighted when increasing their conversion rate, for instance from 5% to 6%. In this regard, BrightInfo insists on looking at the glass half-empty. More than 95% of visitors to a website – potential customers – leave without engaging. They don't find it relevant. Which is strange, since they've reached the website for a reason. Visitors arrive to your website from different channels and industries, they come with specific needs and at various stages of the buying cycle. Each of them behaves according to their own personality and attention span. It is hard to imagine that one website can fit them all. -
36
Phi-4-reasoning
Microsoft
Phi-4-reasoning is a 14-billion parameter transformer-based language model optimized for complex reasoning tasks, including math, coding, algorithmic problem solving, and planning. Trained via supervised fine-tuning of Phi-4 on carefully curated "teachable" prompts and reasoning demonstrations generated using o3-mini, it generates detailed reasoning chains that effectively leverage inference-time compute. Phi-4-reasoning incorporates outcome-based reinforcement learning to produce longer reasoning traces. It outperforms significantly larger open-weight models such as DeepSeek-R1-Distill-Llama-70B and approaches the performance levels of the full DeepSeek-R1 model across a wide range of reasoning tasks. Phi-4-reasoning is designed for environments with constrained computing or latency. Fine-tuned with synthetic data generated by DeepSeek-R1, it provides high-quality, step-by-step problem solving. -
37
Resistant AI
Resistant AI
Resistant AI, protects fintech AI and automation systems from manipulation and attack — without replacing their existing tech stack. We have two main products: - Resistant Documents. Document fraud detection software checks any document, from anywhere, for fraud and authenticity (in seconds). - Resistant Transactions. Transaction monitoring software upgrades existing rules-based TM systems, targeting advanced fincrime typologies with 80+ AI models. We analyze everything (from submitted documents to ongoing customer behaviors), uncovering and preventing: Document forgery Large-scale serial fraud Synthetic identities Account takeovers Money mules Laundering APP fraud With adaptive AI reasoning, we can even combat previously unknown financial threats! -
38
Flint AI
SandboxAQ
Flint AI is a local-first, framework-agnostic AgentOps CLI that helps developers determine whether an AI agent is reliable before it reaches production. One command, flintai scan, analyzes Python source code for security vulnerabilities, misconfigurations, risky tool access, missing guardrails, and quality issues, then uses AI reasoning to triage likely false positives. A second command, flintai eval, sends functional and adversarial prompts to a running agent and scores its responses across more than 35 built-in evaluations, including factual accuracy, instruction adherence, prompt injection resistance, jailbreak resilience, and other runtime behaviors. Each agent receives a reliability score, with findings mapped to OWASP Agentic Security Initiative risks ASI01 through ASI10 and severity scored using CVSS v4.0. Flint AI works with agent frameworks and SDKs including Claude Agents SDK, LangChain, CrewAI, Anthropic SDK, OpenAI SDK, MCP servers, and AutoGen.Starting Price: Free -
39
Intezer AI SOC
Intezer
Intezer AI SOC combines proven forensic capabilities with the adaptive reasoning of Agentic AI. The result is sub-minute triage across 100% of alerts, with less than 2% escalated for human review, a 98% verdict accuracy, and complete transparency. Intezer provides full coverage for all alert types including endpoint, network, email, identity, and cloud. Investigation outcomes are continuously fed into AI-driven detection engineering. Coverage is mapped and tracked against MITRE ATT&CK and new behavioral rules are deployed to address gaps in the detection posture. New alerting is funneled into Intezer AI SOC and creates a closed loop that continuously improves security posture over time. -
40
Build38
Build38
Build38 provides advanced mobile Runtime Application Self-Protection (RASP) technology to secure iOS and Android applications against reverse engineering, runtime manipulation, malware injection, and fraudulent attacks. By integrating the Build38 SDK, applications are transformed into self-protecting environments with built-in anti-tampering, anti-debugging, root/jailbreak detection, and runtime integrity monitoring. Our AI-driven threat intelligence continuously analyzes behavioral anomalies and emerging attack patterns, enabling real-time protection without requiring app store resubmission. Once deployed, applications receive ongoing security updates and centralized monitoring, ensuring long-term resilience against evolving mobile threats. Build38 helps organizations reduce fraud risk, protect sensitive business logic and APIs, and maintain compliance in highly regulated environments—without impacting user experience or development velocity.Starting Price: Free -
41
Alice
Alice
Alice (formerly ActiveFence) is a security, safety, and trust platform built to protect AI systems and online platforms in the GenAI era. Powered by the world’s largest adversarial intelligence dataset, Alice safeguards over 3 billion users across more than 120 languages. Its Rabbit Hole intelligence engine continuously analyzes billions of toxic and manipulative data samples to detect emerging threats in real time. The WonderSuite platform includes tools like WonderBuild for pre-launch stress testing, WonderFence for runtime guardrails, and WonderCheck for automated red-teaming. By defending against prompt injection, jailbreaks, governance gaps, and harmful AI behavior, Alice enables enterprises and foundation model labs to innovate with confidence. -
42
CrowdStrike Falcon AIDR
CrowdStrike
CrowdStrike Falcon AI Detection and Response (AIDR) is an enterprise security platform designed to protect the rapidly expanding AI attack surface by delivering real-time visibility, detection, and response across AI systems, users, and interactions. It provides unified visibility into how employees and AI agents use generative AI by mapping relationships between users, prompts, models, agents, and supporting infrastructure, while capturing detailed runtime logs for monitoring, compliance, and investigation. It continuously monitors AI activity across endpoints, cloud environments, and applications, enabling organizations to understand how data flows through AI systems and how agents operate within defined boundaries. AIDR detects and blocks AI-specific threats such as prompt injection, jailbreak attempts, malicious entities, harmful outputs, and unauthorized interactions, using behavioral analysis and integrated threat intelligence. -
43
Digna
digna GmbH
digna is a data quality and observability platform designed to monitor, analyze, and validate data directly within enterprise data environments. It combines anomaly detection, time-series analytics, and validation into a unified system that helps teams detect issues early and understand how data behaves over time. Core Capabilities * Data Anomaly Detection Identifies changes in data volume, distribution, and behavior using statistical methods and AI-driven models without relying on manually defined rules. * Time-Series Analytics Built-in analytical methods (regression, pattern detection, seasonality analysis) allow users to interpret trends and deviations directly within the platform. * Data Timeliness Monitoring Tracks expected data arrival times and identifies delays across pipelines and data flows. * Data Validation Supports rule-based validation with reusable templates and centralized definitions of allowed values. * Schema Change Tracking Detects structural changes in dat -
44
mFaaS
mTraction
Detect Mobile Ad Fraud. mFaaS's sophisticated machine learning algorithms identify ad fraud in real-time across 15+ reason codes. Get a quick overview of your mobile traffic and potential fraudulent sources through exceptional behavioral and pattern recognition capabilities. Customizable dashboards and deeply sliced visuals for key actionable insights. Our core technology backed by 7+ patent applications ensure multi-level quality checks across campaigns. mFaaS is a mobile ad fraud detection platform that identifies fraudulent traffic across 15+ fraud types in real-time. -
45
PageIndex
PageIndex
PageIndex is a human-like document AI platform for understanding long, complex documents with precise, verifiable answers grounded directly in the source. It uses vectorless, reasoning-based retrieval instead of embeddings, chunking, or vector databases, transforming each document into a tree-structured index that mirrors how people navigate sections, subsections, pages, and content. An LLM then reasons over that structure to decide where to look for relevant information, producing context-aware retrieval that is traceable and explainable. Users can upload reports, filings, research papers, technical manuals, legal documents, medical files, textbooks, and business plans, then ask questions with line-level citations that can be reviewed and verified. PageIndex supports ultra-long documents spanning thousands of pages and understands text, tables, charts, figures, and images.Starting Price: Free -
46
HunyuanOCR
Tencent
Tencent Hunyuan is a large-scale, multimodal AI model family developed by Tencent that spans text, image, video, and 3D modalities, designed for general-purpose AI tasks like content generation, visual reasoning, and business automation. Its model lineup includes variants optimized for natural language understanding, multimodal vision-language comprehension (e.g., image & video understanding), text-to-image creation, video generation, and 3D content generation. Hunyuan models leverage a mixture-of-experts architecture and other innovations (like hybrid “mamba-transformer” designs) to deliver strong performance on reasoning, long-context understanding, cross-modal tasks, and efficient inference. For example, the vision-language model Hunyuan-Vision-1.5 supports “thinking-on-image”, enabling deep multimodal understanding and reasoning on images, video frames, diagrams, or spatial data. -
47
Olmo 3
Ai2
Olmo 3 is a fully open model family spanning 7 billion and 32 billion parameter variants that delivers not only high-performing base, reasoning, instruction, and reinforcement-learning models, but also exposure of the entire model flow, including raw training data, intermediate checkpoints, training code, long-context support (65,536 token window), and provenance tooling. Starting with the Dolma 3 dataset (≈9 trillion tokens) and its disciplined mix of web text, scientific PDFs, code, and long-form documents, the pre-training, mid-training, and long-context phases shape the base models, which are then post-trained via supervised fine-tuning, direct preference optimisation, and RL with verifiable rewards to yield the Think and Instruct variants. The 32 B Think model is described as the strongest fully open reasoning model to date, competitively close to closed-weight peers in math, code, and complex reasoning.Starting Price: Free -
48
Patronus AI
Patronus AI
Patronus AI is an automated AI evaluation, security, and optimization platform for LLM applications and agentic systems. It helps teams confidently deploy AI products at scale by generating test suites, running experiments, logging traces, comparing outputs, monitoring production interactions, and evaluating model performance in real time. It provides industry-leading evaluators for RAG hallucinations, context quality, image relevance, answer correctness, prompt injection, sensitive data leakage, toxicity, bias, and other safety or reliability risks. Patronus Evaluators can score AI outputs on specific dimensions, and teams can also create custom evaluators for use-case-specific criteria. Its platform combines dashboards, APIs, plug-and-play evaluations, logs, traces, side-by-side comparisons, visualizations, analytics, and real-time alerts to help teams detect mistakes, benchmark models, improve prompts, and understand system behavior over time. -
49
Darktrace / NETWORK
Darktrace
Darktrace / NETWORK is an AI-powered network detection and response solution built to prevent, detect, investigate, and contain known and previously unseen threats across modern environments. Its Self-Learning AI runs directly on an organization’s data, learns normal behavior for every device, identity, connection, and attack path, and identifies unusual activity without depending on signatures, historical attack data, or globally trained models. It provides visibility across on-premises, virtual, cloud, and hybrid networks, including remote endpoints, OT devices, ZTNA, and both encrypted and decrypted traffic. It continually tunes detection to improve accuracy and reduce alert fatigue without manual rule maintenance. Cyber AI Analyst performs end-to-end investigations at scale, forms hypotheses, reaches conclusions, prioritizes incidents by potential business impact, and correlates events across network, endpoint, cloud, identity, OT, email, and remote systems. -
50
Atatus
NamLabs Technologies
Atatus is a next-gen observability platform for modern, cloud-native systems, built for developers, DevOps engineers, and SREs who require deep visibility across applications and infrastructure. Trusted by organizations of all sizes and across diverse industries, Atatus provides a unified SaaS platform for comprehensive system monitoring. The platform combines metrics, logs, and traces to deliver full-stack observability across microservices, containers, and cloud environments. With distributed tracing, code-level performance analysis, and intelligent error detection, teams can pinpoint issues faster, understand system behavior, and minimize downtime. Atatus also delivers real-user experience and behavioral insights, helping teams connect application performance with how digital services are actually consumed.Starting Price: $49.00/month
