WorkOS vs Together AI
Side-by-side comparison to help you choose.
| Feature | WorkOS | Together AI |
|---|---|---|
| Type | API | Model |
| UnfragileRank | 37/100 | 22/100 |
| Adoption | 1 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 13 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Enables SaaS applications to integrate enterprise SSO by accepting SAML assertions and OIDC authorization codes from 20+ identity providers (Okta, Azure AD, Google Workspace, etc.). WorkOS acts as a service provider that normalizes identity responses across heterogeneous enterprise directories, exchanging authorization codes for user profiles and access tokens via language-specific SDKs (Node.js, Python, Ruby, Go, PHP, Java, .NET). The implementation uses a per-connection pricing model where each enterprise customer's identity provider is registered as a distinct connection, allowing multi-tenant SaaS platforms to onboard customers without custom integration work.
Unique: Normalizes SAML/OIDC responses across 20+ heterogeneous identity providers into a unified user profile schema, eliminating per-provider integration code. Uses per-connection pricing model where each enterprise customer's identity provider is a billable unit, enabling SaaS platforms to scale enterprise sales without custom engineering per customer.
vs alternatives: Faster enterprise onboarding than building native SAML/OIDC support (weeks vs months) and cheaper than hiring dedicated identity engineers; more flexible than Auth0's rigid provider list because it supports custom SAML/OIDC endpoints with manual configuration.
Automatically synchronizes user and group data from enterprise HR systems and directories (Workday, SuccessFactors, BambooHR, etc.) into SaaS applications using the SCIM 2.0 protocol. WorkOS acts as a SCIM service provider that receives provisioning/de-provisioning events from customer directories via webhooks, normalizing user lifecycle events (create, update, suspend, delete) and group memberships into a consistent schema. The implementation uses event-driven architecture where directory changes trigger webhook deliveries in real-time, eliminating manual user management and keeping application user rosters synchronized with authoritative HR systems.
Unique: Implements SCIM 2.0 as a service provider (not just client), allowing enterprise HR systems to push user lifecycle events via webhooks in real-time. Uses normalized event schema that abstracts away differences between Workday, SuccessFactors, BambooHR, and other HR systems, enabling single integration point for SaaS platforms.
vs alternatives: Simpler than building custom SCIM integrations with each HR vendor (weeks per vendor vs days with WorkOS); more reliable than manual CSV imports because it's event-driven and continuous; cheaper than hiring dedicated identity engineers to maintain per-vendor connectors.
Enables users to authenticate without passwords by sending one-time magic links via email. When a user enters their email address, WorkOS generates a unique, time-limited link (typically valid for 15-30 minutes) and sends it via email. Clicking the link verifies email ownership and creates an authenticated session without requiring password entry. The implementation eliminates password management burden and reduces phishing attacks because users never enter credentials into the application.
Unique: Provides passwordless authentication via email magic links as part of AuthKit, eliminating password management burden. Magic links are time-limited and email-based, reducing phishing attacks compared to password-based authentication.
vs alternatives: Simpler user experience than password-based authentication; more secure than passwords because users never enter credentials; cheaper than SMS-based passwordless because it uses email (no SMS costs).
Enables users to authenticate using existing Microsoft or Google accounts via OAuth 2.0 protocol. WorkOS handles OAuth flow (authorization request, token exchange, user profile retrieval) transparently, allowing users to sign in with a single click. The implementation abstracts away OAuth complexity, supporting both Microsoft (Azure AD, Microsoft 365) and Google (Gmail, Google Workspace) without requiring application to implement separate OAuth clients for each provider.
Unique: Abstracts OAuth 2.0 complexity for Microsoft and Google, handling authorization flow, token exchange, and user profile retrieval transparently. Supports both personal (Gmail, personal Microsoft) and enterprise (Google Workspace, Azure AD) accounts from single integration.
vs alternatives: Simpler than implementing OAuth clients directly; more integrated than third-party social login services because it's part of AuthKit; supports both personal and enterprise accounts without separate configuration.
Enables users to add a second authentication factor (time-based one-time password via authenticator app, or SMS code) to their account. WorkOS handles MFA enrollment, challenge generation, and verification transparently during authentication flow. The implementation supports both TOTP (authenticator apps like Google Authenticator, Authy) and SMS-based codes, allowing users to choose their preferred MFA method. MFA can be optional (user-initiated) or mandatory (enforced by SaaS application or enterprise customer policy).
Unique: Provides MFA as part of AuthKit with support for both TOTP (authenticator apps) and SMS codes. Handles MFA enrollment, challenge generation, and verification transparently without requiring application code changes.
vs alternatives: Simpler than building custom MFA logic; more flexible than single-method MFA because it supports both TOTP and SMS; integrated with AuthKit so MFA is available for all authentication methods (passwordless, social, SSO).
Provides a pre-built, white-label authentication interface (AuthKit) that SaaS applications can embed or redirect to, supporting passwordless authentication (magic links via email), social sign-in (Microsoft, Google), multi-factor authentication (MFA), and traditional password-based login. The UI is hosted by WorkOS and customizable via dashboard (logo, colors, branding) without requiring frontend code changes. AuthKit handles the full authentication flow including credential validation, MFA challenges, and session token generation, reducing SaaS teams' responsibility to building and securing authentication UI from scratch.
Unique: Provides fully hosted, white-label authentication UI that abstracts away credential handling, MFA logic, and social provider integrations. Uses per-active-user pricing model (free up to 1M, then $2,500/mo per 1M) rather than per-request, making it cost-predictable for platforms with stable user bases.
vs alternatives: Faster to deploy than Auth0 or Okta (hours vs weeks) because UI is pre-built and hosted; cheaper than hiring frontend engineers to build custom login forms; more flexible than Firebase Authentication because it supports enterprise SSO and passwordless in same product.
Enables SaaS applications to define custom roles and granular permissions, then assign them to users and groups provisioned via SSO or directory sync. WorkOS RBAC allows applications to create hierarchical role structures (e.g., Admin > Manager > Member) with custom permission sets, then enforce authorization decisions at the application layer using role and permission data returned in user profiles. The implementation uses a permission-based model where each role is a collection of named permissions (e.g., 'users:read', 'users:write', 'billing:admin'), allowing fine-grained access control without hardcoding authorization logic.
Unique: Integrates RBAC directly into user profiles returned by SSO/Directory Sync, eliminating need for separate authorization service. Uses permission-based model (not just role-based) allowing granular control at feature level without hardcoding authorization logic in application.
vs alternatives: Simpler than building custom authorization system or integrating separate service like Oso or Authz; more flexible than Auth0 roles because it supports custom permission hierarchies; integrated with directory sync so role changes propagate automatically when users are provisioned/deprovisioned.
Captures and stores all authentication, authorization, and user lifecycle events (logins, SSO attempts, directory sync actions, role changes, permission grants) with full audit trail including timestamp, actor, action, resource, and outcome. WorkOS streams audit logs to external SIEM systems (Splunk, Datadog, etc.) via dedicated connections, or allows export via API for compliance reporting. The implementation uses event-driven architecture where all identity operations generate immutable audit records, enabling forensic analysis and compliance audits (SOC 2, HIPAA, etc.).
Unique: Integrates audit logging directly into identity platform rather than requiring separate logging service. Uses per-event pricing model ($99/mo per million events stored) allowing cost-scaling with event volume; supports SIEM streaming ($125/mo per connection) for real-time security monitoring.
vs alternatives: More comprehensive than application-layer logging because it captures all identity operations at platform level; cheaper than building custom audit system or integrating separate logging service; integrated with SSO/Directory Sync so all events are automatically captured without application instrumentation.
+5 more capabilities
Provides unified REST API access to 50+ hosted models (text, vision, image generation, embeddings) with automatic load balancing and pay-per-token billing. Requests are routed to optimized inference clusters running custom CUDA kernels (FlashAttention-4, ATLAS) for 2× claimed speedup. No infrastructure provisioning required; models scale elastically based on demand.
Unique: Unified API gateway across 50+ heterogeneous models (text, vision, image, audio, embeddings) with custom CUDA kernel optimization (FlashAttention-4, ATLAS runtime learners) for 2× claimed speedup, eliminating need to manage separate endpoints per model provider
vs alternatives: Faster and cheaper than calling OpenAI/Anthropic directly for open-source models (Llama, Qwen, DeepSeek) due to custom kernel optimization; more model variety than single-provider APIs but less mature documentation than established platforms
Processes large token volumes (up to 30B tokens per model) asynchronously via batch jobs, applying custom kernel optimizations to reduce per-token cost by 50% vs. serverless. Batches are queued, scheduled during off-peak GPU availability, and results are returned via webhook or polling. Ideal for non-latency-sensitive workloads like data labeling, content generation, or model evaluation.
Unique: Dedicated batch queue with custom kernel scheduling that achieves 50% cost reduction by batching requests during off-peak GPU availability and applying FlashAttention-4/ATLAS optimizations at scale; supports up to 30B tokens per submission without per-token rate limiting
vs alternatives: Significantly cheaper than serverless for large-scale inference (50% claimed savings); more cost-effective than OpenAI Batch API for open-source models, but lacks documented completion SLA and integration patterns
WorkOS scores higher at 37/100 vs Together AI at 22/100. WorkOS leads on adoption, while Together AI is stronger on ecosystem. WorkOS also has a free tier, making it more accessible.
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Together AI develops and deploys custom CUDA kernels (FlashAttention-4, ATLAS runtime learners, speculative decoding variants) that optimize inference and training performance. FlashAttention-4 claims 1.3× speedup vs. cuDNN on NVIDIA Blackwell. ATLAS claims 4× faster LLM inference. Kernels are transparently applied to all hosted models without user configuration.
Unique: Proprietary custom CUDA kernel stack (FlashAttention-4, ATLAS, speculative decoding) transparently applied to all hosted models, claiming 2× general speedup and 1.3× FlashAttention-4 speedup on NVIDIA Blackwell; eliminates need for manual kernel selection or tuning
vs alternatives: Automatic kernel optimization without user configuration vs. manual kernel selection in vLLM or TensorRT; claims faster than stock cuDNN implementations but lacks peer-reviewed benchmarks vs. competing optimization frameworks
Provides cloud storage for model weights, training data, and inference artifacts with zero egress fees when used within Together's ecosystem. Eliminates data transfer costs for models deployed to Together's inference endpoints. Storage pricing and capacity limits not documented.
Unique: Integrated managed storage with explicit zero egress fees for artifacts used within Together's inference/fine-tuning ecosystem, eliminating data transfer costs for model deployment workflows
vs alternatives: Zero egress within Together ecosystem vs. AWS S3 or GCP Cloud Storage where egress fees apply; less feature-rich than general-purpose cloud storage but optimized for ML artifact management
Provisions dedicated GPU infrastructure for single-tenant model deployment, isolating inference workloads from shared serverless clusters. Models run on reserved GPUs with guaranteed availability and no noisy-neighbor interference. Supports custom container images and optimized kernel stacks (FlashAttention-4, ATLAS). Pricing model and hardware specs not documented.
Unique: Single-tenant GPU reservation with custom kernel stack (FlashAttention-4, ATLAS) and containerized deployment support, eliminating noisy-neighbor interference and enabling proprietary model hosting; purpose-built for production inference with guaranteed resource isolation
vs alternatives: More cost-effective than AWS SageMaker or Azure ML for dedicated inference due to custom kernel optimization; less mature than established platforms but offers tighter integration with Together's optimization stack
Enables supervised fine-tuning of open-source models (Llama, Qwen, Gemma, etc.) with recent upgrades supporting larger models and longer context windows. Fine-tuning methodology (LoRA, QLoRA, full) not documented. Trained models are deployed to serverless or dedicated inference endpoints. Claims to improve accuracy, reduce hallucinations, and enable behavior control.
Unique: Recent platform upgrades support larger models and longer context windows for fine-tuning (specific improvements unspecified), with integrated deployment to serverless/dedicated endpoints; methodology and hyperparameter controls not documented but claims domain-specific accuracy improvements and hallucination reduction
vs alternatives: Tighter integration with Together's inference stack than standalone fine-tuning services; less documented than OpenAI's fine-tuning API but potentially cheaper for open-source models
Hosts multiple image generation models (FLUX.2 pro/dev/flex/max, FLUX.1 schnell, Stable Diffusion 3/XL, Qwen Image 2.0, Google Imagen 4.0, ByteDance Seedream, Ideogram 3.0) via serverless API. Requests specify model, prompt, and quality/style parameters; outputs are image URLs. Pricing ranges $0.0019–$0.06 per image depending on model and resolution.
Unique: Unified API access to 10+ image generation models (FLUX variants, Stable Diffusion, Qwen Image, Google Imagen, ByteDance Seedream, Ideogram) with per-image pricing ($0.0019–$0.06) and custom kernel optimization for faster generation; eliminates need to manage separate endpoints per model provider
vs alternatives: More model variety than Replicate or Hugging Face Inference API; cheaper per-image pricing for FLUX.1 schnell ($0.0027) vs. Replicate ($0.004); less mature API documentation than Stability AI's official API
Hosts vision-capable models (Kimi K2.6, K2.5, Qwen3.5-Vision 9B, Gemma 4 31B) that accept text prompts + image inputs and return text analysis/descriptions. Models process images via URL or embedded format (unspecified). Supports visual question answering, document analysis, scene understanding, and multimodal reasoning.
Unique: Unified API for multiple vision models (Kimi, Qwen, Gemma) with custom kernel optimization for faster image processing; supports multimodal reasoning combining text and image inputs without separate vision/language model calls
vs alternatives: More model variety than OpenAI's vision API; potentially cheaper for open-source vision models (Qwen3.5-Vision) vs. GPT-4V; less mature documentation than established vision platforms
+4 more capabilities