Fastchat

What is fastchat

Fastchat is a conversational AI platform that provides both cloud and self-hosted infrastructure for building, running, and monitoring chat-based applications powered by large language models. The product packages a runtime for streaming model output, client SDKs and UIs, model orchestration (choose open models or hosted models), and operational features such as multi-tenant access, rate limiting, and usage analytics. Fastchat targets engineering teams, AI platform teams, and product managers who need to deploy chat assistants that integrate with internal data and third-party services.

Fastchat focuses on reducing the work required to go from prototype to production by offering a modular architecture: a model serving layer, a gateway for client connections, and connectors to vector databases and business systems. The platform is used for customer support assistants, internal knowledge bots, sales enablement assistants, and embedded chat capabilities inside SaaS products.

Fastchat supports common deployment patterns used in production: horizontal scaling of workers, GPU and CPU model backends, secure networking, and role-based access controls. It is designed to work with models hosted locally, on private cloud GPUs, and with API-backed models from established model providers.

Fastchat features

Fastchat bundles a set of features aimed at operationalizing chat applications and integrating LLMs into product workflows.

• Real-time streaming: Low-latency streaming of partial model outputs to client applications using WebSocket and server-sent events. This reduces perceived response time and supports progressive UI rendering.

• Model orchestration: Route requests to different models based on cost, latency, or capability. Deploy and manage a mix of open-source models and vendor-hosted endpoints.

• Client SDKs and UI components: Official JavaScript and Python SDKs plus prebuilt chat UI components that handle streaming, message hydration, and attachments. These components are designed for embedding into web apps and mobile applications.

• Vector store integrations: Connectors for popular vector databases and embedding providers to power retrieval-augmented generation workflows. This includes routines for document ingestion, chunking, and embedding refresh.

• Authentication and access control: Support for single sign-on, API keys, and role-based access control so teams can separate developer access, production access, and tenant boundaries.

• Observability and analytics: Usage dashboards with per-endpoint metrics, token consumption, latency breakdowns, and audit logs for compliance and cost monitoring.

• Conversation management: Tools for system and user prompting, context window management, conversation history pruning, and instruction templates for consistent behavior across agents.

• Plugin and webhook ecosystem: Ability to call external services during a conversation using secure plugin interfaces and outgoing webhooks, enabling actions like database queries, CRM updates, or calling internal microservices.

• Fine-tuning and adapters: Built-in workflows to attach fine-tuned adapters or LoRA weights to base models, plus versioning of model artifacts and rollback support.

What does fastchat do?

Fastchat provides the runtime and tooling to build interactive, stateful chat applications around large language models. It handles request routing, state and conversation management, low-latency streaming of model output to clients, and connectors to data sources used for retrieval-augmented generation. Engineers use Fastchat to avoid building chat plumbing from scratch and to maintain consistent behavior across different deployment environments.

Operationally, Fastchat automates routine production concerns: autoscaling model workers, enforcing rate limits, tracking token usage, and capturing logs and traces for each conversation. The platform lets teams define routing rules so inexpensive models are used for simple tasks while higher-capability models are reserved for complex queries.

From an integration perspective, Fastchat provides APIs and SDKs that make it straightforward to add chat UIs to web and mobile apps, connect to vector stores for knowledge retrieval, and call external systems during a conversation. This combination of runtime, integrations, and observability reduces time to production and simplifies ongoing maintenance.

Fastchat pricing

Fastchat offers these pricing plans:

• Free Plan: $0/month self-hosted community edition with core runtime and basic integrations
• Starter: $15/month per seat with hosted service limits, basic analytics, and email support
• Professional: $99/month per seat with expanded rate limits, advanced analytics, priority support, and SSO
• Enterprise: Contact Sales for custom pricing, dedicated infrastructure, and compliance requirements

The platform also exposes usage-based charges for hosted model inference and managed vector DB operations when using Fastchat's cloud-hosted model endpoints. Typical usage addons include per-request inference fees and per-GB storage and retrieval charges for vector indexes. Check Fastchat's pricing tiers at https://www.fastchat.com/pricing for the most up-to-date rates and enterprise options.

Many teams combine a subscription for the hosted control plane with usage billing for model inference or choose the Free Plan to self-host the full stack and only pay for infrastructure (GPU, storage) in their cloud account. The Enterprise tier offers volume discounts and invoicing arrangements for customers with predictable monthly consumption.

Fastchat also offers volume and contract discounts for annual commitments and provides add-on professional services for integration, on-premises setup, and performance tuning. For pricing details specific to model hosting and token billing, review Fastchat's hosted model pricing in the documentation.

How much is fastchat per month

Fastchat starts at $0/month for the Free Plan when organizations choose to self-host the community runtime. For hosted service usage, Fastchat starts at $15/month per seat for the Starter plan that includes managed control plane access and basic tier limits.

Monthly costs increase with seat count, higher concurrency limits, and model inference consumption when using Fastchat's cloud-hosted models. Teams with moderate usage commonly see combined subscription and inference costs in the $99–$499/month range, while larger deployments using high-rate inference exceed that.

Monthly price behavior is influenced by the chosen model hosting option (self-hosted vs. Fastchat-hosted), the size of the models used, and the volume of requests per month. For a precise monthly estimate, consult Fastchat's usage calculators in the billing documentation.

How much is fastchat per year

Fastchat costs $180/year for a single seat on the Starter plan when billed annually at $15/month equivalent. The Professional plan is $99/month, which equates to $1,188/year per seat if billed monthly; annual discounts are typically available.

Self-hosted organizations can run Fastchat at effectively $0/year for the software license while incurring infrastructure costs for compute and storage. Enterprise contracts often include committed annual spend with negotiated discounts and service-level commitments.

For enterprises, annual pricing is quoted case-by-case and can include managed hosting, dedicated clusters, and onboarding services. Check Fastchat's enterprise options and contract models on Fastchat's pricing page.

How much is fastchat in general

Fastchat pricing ranges from $0 (self-hosted) to $499+/month per team seat depending on plan, hosting choice, and model inference consumption. Small teams often run on the Free Plan or Starter plan while production teams using higher-capability models choose Professional or Enterprise for support and compliance features.

Beyond subscription tiers, the bulk of variable cost in Fastchat deployments is usually model inference and vector store operations. Organizations that self-host models pay for GPU instances by the hour, while teams using Fastchat-hosted inference incur per-request or per-token charges.

To build an accurate budget, factor in subscription fees, expected token volume, storage for embeddings, and costs for security/compliance add-ons. Fastchat provides cost estimation guidance in its billing documentation.

What is fastchat used for

Fastchat is used to deploy interactive conversational agents that connect to internal knowledge bases, enterprise data sources, and external APIs. Common use cases include customer support assistants that handle common inquiries and escalate to humans, knowledge worker assistants that surface internal documents during workflows, and product-integrated chat features that enhance user experience in SaaS products.

Teams also use Fastchat for internal tooling: HR chatbots that answer policy questions, engineering assistants that search docs and code, and analytics assistants that run queries and summarize results. The retrieval-augmented generation pipeline makes Fastchat suitable for scenarios where up-to-date or proprietary information must be blended with LLM responses.

Because Fastchat supports both self-hosted and cloud-hosted models, it is frequently selected by organizations that need to meet data residency, compliance, or latency requirements. The platform’s plugin and webhook capabilities let it act as an action layer that can call internal systems, automate tasks, or create tickets in downstream systems when required.

Pros and cons of Fastchat

Pros:

• Fastchat supports a flexible deployment model: choose between self-hosting for full control or using the hosted control plane for simplified operations. This flexibility fits organizations with varying compliance and cost requirements.

• The platform integrates model orchestration, real-time streaming, and connector libraries that reduce development time for production chat applications. Built-in conversation management and analytics provide operational visibility.

• Fastchat's support for open-source models and adapters allows teams to control inference costs by selecting smaller models for routine tasks and reserving larger models for complex queries.

Cons:

• Self-hosting production-grade LLMs remains resource intensive; organizations that choose the Free Plan self-hosted route must manage GPU provisioning, model optimization, and capacity planning internally.

• Hosted inference charges for large-volume or low-latency use cases can be substantial; teams must closely monitor token and request usage to control cost.

• As with any platform that integrates with enterprise data, securing connectors, access controls, and managing data privacy requires engineering and governance work.

Overall, Fastchat favors teams that want control over model choice and deployment while providing managed features for teams that prefer a hosted control plane.

Fastchat free trial

Fastchat provides a Free Plan suitable for evaluation and small-scale self-hosted deployments. The free tier includes the core runtime, community integrations, and developer documentation to get started without a subscription. It allows teams to validate architectures and prototype chat flows with limited scaling.

For hosted service evaluations, Fastchat typically offers time-limited trials of the hosted control plane and managed inference so teams can measure latency, reliability, and cost using their real workloads. Trials commonly include access to the management console, basic analytics, and sample connectors.

If you need extended evaluation support or a proof-of-concept with production-like data, Fastchat offers professional services and pilot programs as part of the Professional or Enterprise engagement. See Fastchat's documentation on trials and onboarding for details on how to request a hosted evaluation.

Is fastchat free

Yes, Fastchat offers a Free Plan for self-hosted use that provides the core runtime and developer tooling at no software charge. The free option is meant for development, experimentation, and small-scale deployments where organizations provide their own infrastructure.

The hosted control plane and managed inference options are paid and billed separately. Using Fastchat's cloud-hosted models or managed vector storage will incur additional usage-based fees.

Fastchat API

Fastchat exposes a RESTful API and WebSocket endpoints for real-time chat applications, plus official SDKs for JavaScript and Python to simplify integration. The API covers conversation lifecycle management, streaming responses, conversation history retrieval, and admin controls for model routing and tenant management.

Key API capabilities include:

• Token-streaming endpoints that allow clients to receive partial model outputs as they are generated.
• Conversation and session endpoints to create, update, and fetch conversation state and metadata.
• Admin APIs to register models, set routing rules, and manage quota and rate limits per tenant or API key.
• Webhook and plugin interfaces that enable secure calls to external services or internal microservices during the conversational flow.

Fastchat's API is designed for production usage: it includes retry semantics for transient errors, structured error codes for operational handling, and tracing headers to correlate requests with logs. For developers, Fastchat provides example integrations and SDK wrappers that abstract away low-level details. See Fastchat documentation for the complete API reference and example code snippets.

10 Fastchat alternatives

OpenAI — Large-scale model API provider with polished hosted models, broad ecosystem integrations, and predictable latency. Useful for teams who prefer a fully hosted model provider.

Anthropic — Model provider focused on safety and alignment with hosted APIs and enterprise support.

Cohere — Offers hosted models optimized for classification, generation, and embeddings with enterprise features for production usage.

Hugging Face — Model hub and Inference API providing access to many open-source models with hosted and self-hosting options.

Rasa — Open-source conversational AI focused on dialog management and connectors for task-oriented bots, good for structured workflows and on-premises deployments.

Botpress — Open-source conversational platform with an emphasis on visual bot building, on-premises deployment, and integrations to enterprise systems.

PagerDuty ChatOps — Not a direct model platform but an alternative for operational chat and incident workflows that integrates with AI tools.

Dialogflow — Google’s conversational platform for building voice and chat assistants with integrated NLU and enterprise support.

Microsoft Azure OpenAI Service — Bring OpenAI models into Azure with enterprise compliance, identity, and billing integration.

Kore.ai — Enterprise conversational AI platform geared toward large organizations with complex integration and compliance needs.

Paid alternatives to Fastchat

• OpenAI — Hosted model APIs with enterprise plans and a broad SDK ecosystem for production deployments. Ideal when teams want managed, high-quality models without self-hosting.

• Anthropic — Enterprise-grade hosted models with safety-focused features and cooperation on alignment-related configurations.

• Cohere — Commercial embeddings, generation APIs and enterprise support for secure deployments.

• Hugging Face Infinity — Paid inference and model optimization service for low-latency production use of open-source models.

• Microsoft Azure OpenAI Service — Enterprise integration within Azure with managed compliance and support.

• Dialogflow CX — Paid advanced conversational designer and orchestration for contact center and enterprise bots.

Open source alternatives to Fastchat

• Rasa — Open-source framework for building conversational assistants with strong dialog management and on-premises deployment options.

• Botpress — Open-source conversational platform with visual flows, modular architecture, and on-prem deployment.

• Haystack — Open-source framework for RAG (retrieval-augmented generation) use cases that includes connectors, pipelines, and model orchestration.

• Hugging Face Transformers + Custom Runtime — Build your own chat stack using open models and a custom serving layer; suitable for teams with strong ML ops capabilities.

• DeepPavlov — Open-source conversational systems focused on task-oriented dialog and research use cases.

Frequently asked questions about Fastchat

What is Fastchat used for?

Fastchat is primarily used to deploy production-grade conversational AI and chat assistants. Teams use Fastchat to run streaming chat UIs, orchestrate multiple models, and integrate retrieval from knowledge bases. It supports both self-hosted architectures for compliance and a hosted control plane for operational convenience.

Does Fastchat provide real-time streaming?

Yes, Fastchat supports real-time streaming of model output. Clients can receive tokens as they are generated via WebSocket or streaming HTTP and render responses progressively to reduce user-perceived latency.

How much does Fastchat cost per user per month?

Fastchat starts at $15/month per seat for the Starter hosted plan; self-hosting the Free Plan is available at $0/month for the software itself. Actual monthly cost will depend on model inference usage and any additional managed services.

Is there a free version of Fastchat?

Yes, Fastchat offers a Free Plan that provides the community self-hosted runtime and developer tooling without a subscription fee. Hosted model inference and managed services are billed separately.

Can Fastchat integrate with vector databases?

Yes, Fastchat integrates with common vector stores and embedding providers. It includes connectors for ingestion, retrieval, and index management so you can build retrieval-augmented generation workflows.

Does Fastchat have an API for developers?

Yes, Fastchat exposes REST and WebSocket APIs plus SDKs. The APIs cover conversation lifecycle, streaming, admin controls, and plugin/webhook execution for calling external services.

Can Fastchat be self-hosted for on-premises use?

Yes, Fastchat can be self-hosted. Organizations can run Fastchat components on-premises or in private cloud to meet data residency and compliance requirements while controlling compute costs.

How secure is Fastchat for enterprise use?

Fastchat includes enterprise-grade security features such as SSO, API keys, role-based access controls, and audit logging; enterprise contracts also provide compliance assurances and deployment options tailored to regulatory needs.

Can I route requests to multiple models with Fastchat?

Yes, Fastchat supports model orchestration and routing rules. You can configure routing to use smaller models for basic tasks and route complex queries to higher-capability models or vendor-hosted endpoints.

What support and onboarding does Fastchat provide?

Fastchat provides tiered support based on plan level. The Starter plan includes email support and documentation, Professional adds priority support and onboarding assistance, and Enterprise packages include dedicated onboarding, SLA commitments, and optional professional services.

fastchat careers

Fastchat public careers listings typically include roles across engineering, product, and customer success focused on AI infrastructure, model ops, and integrations. Engineering roles emphasize experience with distributed systems, GPU-based model serving, and cloud platform automation. Product and design roles focus on conversational UX, developer experience, and analytics features.

Recruiting for Fastchat often looks for candidates with hands-on experience deploying LLMs, familiarity with vector search, and strong knowledge of API design and observability. For enterprise positions there are also roles in technical account management and solution architecture to support large customers.

Open positions and hiring processes are listed on the company careers page and typically include technical interviews, take-home exercises, and culture-fit interviews. Candidates interested in contributing to the open-source parts of the platform can also engage through the project's public repositories.

fastchat affiliate

Fastchat has an affiliate and partner program that is geared toward systems integrators, reseller partners, and cloud consultancies that help customers deploy conversational AI at scale. Partners receive technical enablement, co-marketing opportunities, and access to partner-only resources for onboarding customers.

Affiliate programs often vary by region and partner type; interested parties are encouraged to contact Fastchat's partner team to discuss referral fees, implementation credits, and partner certification paths. Partners that specialize in data engineering and MLOps are prioritized for integrations and joint go-to-market efforts.

Where to find fastchat reviews

You can find third-party reviews and user discussions about Fastchat on developer forums, AI community channels, and product review sites that focus on conversational AI and developer tooling. Common places to check include technical community threads, GitHub issues on the open-source components, and Q&A on engineering forums.

For curated testimonials and case studies from customers, review Fastchat's published case studies and documentation. To compare feature-by-feature and read user-reported pros and cons, search for Fastchat evaluations that focus on model orchestration, latency, cost, and developer experience.