Conversation API
Stateful AI chat API with built-in memory for app developers
If you want stateful chat without building a database layer, the Conversation API is a practical pick. The free Demo tier lets you validate the approach, and the $49 Sandbox adds BYOK. But you're locked into a managed platform—no on-prem, no fine-tuning outside Enterprise—so weigh that against the speed gain.
Verified 3d ago · liveness 80/100 · cite: rightaichoice.com/tools/conversation-api
- Low-code builders adding stateful chat without backend infrastructure
- Startup teams shipping AI features fast with minimal dev overhead
- Product managers owning AI features without developer dependency
- Individual builders prototyping conversational AI
- Teams needing on-premise deployment or full data control
- Users requiring custom fine-tuning of conversation models (Enterprise only)
- Applications needing strict data residency outside EU, US, or Asia Pacific
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Skip Amarsia Conversation API if you require on-premise deployment, need fine-tuning on your own data outside the Enterprise tier, must control data residency beyond Amarsia's US/EU/APAC regions, or have high-frequency low-latency real-time use cases.
Overage fees: $0.06 per run per agent on Sandbox and $0.045 per run per agent on Production, which can add up if your usage spikes.
Amarsia Conversation API pricing is competitive for low-code builders: free tier with 1,000 runs, $49 Sandbox, and $399 Production. Compared to Vercel AI SDK (usage-based) or LangChain (open-source), Amarsia bundles memory and infrastructure, but you pay for managed convenience. The credit system lets you start free, but heavy usage costs more than DIY.
In short
Conversation API — Stateful AI chat API with built-in memory for app developers. Best for Low-code builders adding stateful chat without backend infrastructure, Startup teams shipping AI features fast with minimal dev overhead, Product managers owning AI features without developer dependency. Free to start; paid plans from $49/mo.
What's new in Conversation API
Checked yesterdayAcross the latest 7 updates: 4 feature updates, 2 launches and 1 changelog entry.
Amarsia platform updates: Bedrock API key support, Tools 3.0, and new pricing plans
Bedrock API key support for BYOK workflows; Tools 3.0 adds pause/resume calling; sandbox and production plans introduced.
Amarsia releases test cases, contextual vector chunking, and web search improvements
Adds conversational simulation tests, LLM context headers per chunk, and web search map/depth improvements.
Amarsia launches evaluations, new pricing tiers, and security enhancements
Introduces side-by-side evaluations, Core/Scale plans, free sandbox, credits system, and security toggles.
Amarsia adds MCP server management and projects collaboration
Imports MCP servers as Actions, auto-creates Draft actions, and provides new collaboration projects.
Amarsia renames workflows to assistants, adds conversation API features
Major rename: 'workflows' to 'assistants'; conversation API gains name and history; AI consultant released.
Amarsia launches on Product Hunt, adds reasoning logs and AI Actions
Product Hunt #2 product of the day; new AI Actions for isolated tools; reasoning logs and security updates.
Amarsia adds GPT-5.1 and Claude 4.5 models, EU infrastructure
New premium models available; EU-Berlin infrastructure deployed with conversation API improvements.
What people actually say about Conversation API — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
34 mentions across 4 sources (Reddit, Hacker News, Product Hunt, Lemmy) · researched Jul 3, 2026.
- +Zero-setup persistence: no database or backend to manage
- +Simple REST API with JavaScript SDK and React hooks
- +Streaming API for real-time, chunk-by-chunk responses
- +Supports multiple models: OpenAI, Anthropic, Google Gemini
- +Automatic conversation history via conversation_id
- −Context window size limits and pruning not documented
- −No webhook support for real-time event triggers
- −Multilingual support unclear from community feedback
- −Limited community data beyond launch day buzz
- −No clear differentiation from self-hosted alternatives
- • Overages for API calls beyond plan limits
- • Additional storage costs for long conversation histories
- • Potential egress fees if using external LLM models
In users’ own words
“Hey everyone, I’m currently planning the development of an app that requires a voice conversation with the ai. It’s education based with the intention of helping the user communicate with an ‘ai client’. I found out GPT 4’s API does not have this included which is a shame because how it works on the app would be perfect for what I’m trying to achieve. Does anyone know of any other ai models that have this feature…”
Real posts from independent users, linked to the source — not testimonials we collected.
Viability Score
How well maintained and how widely used is Conversation API? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Automatic conversation storage with conversation_id
- Named conversations and message history retrieval
- Metadata attachment to chat threads
- Streaming API for real-time responses
- Multi-model routing: OpenAI GPT, Anthropic Claude, Google Gemini, DeepSeek
- Amazon Bedrock API key support for BYOK workflows
- Release meta support for tracking assistant versions
- Tools 3.0 pause/resume tool calling for debugging
- JavaScript SDK (@amarsia/sdk) and React hooks (@amarsia/react)
- Testing playground for iteration
- Security toggles: authentication and IP allowlist
- Monitoring dashboard for interaction analytics
- Knowledge base with RAG and drag-and-drop upload
- URL scraping for knowledge bases
- Web search tool
About Conversation API
The Amarsia Conversation API removes the backend plumbing from adding stateful AI chat to your apps. Instead of managing a database, handling sessions, and wiring model calls, you make one API call to get automatic conversation storage, a conversation_id, and memory that persists across turns. Built for low-code builders, startup teams, and product managers, it lets you deploy conversational features in days without deep AI expertise. The API handles the entire chat lifecycle: naming conversations, retrieving message history, and attaching metadata to threads keeps sessions organized and debuggable. A streaming API delivers real-time responses, and a model registry routes calls across OpenAI GPT, Anthropic Claude, Google Gemini, DeepSeek, and Amazon Bedrock for bring-your-own-key (BYOK) setups. Recent updates added release meta support for tracking assistant versions in production and pause/resume tool calling via Tools 3.0 for interactive debugging. An SDK (@amarsia/sdk), React hooks (@amarsia/react), and a testing playground streamline integration. Security toggles enforce authentication and IP allowlists, while a monitoring dashboard shows interaction analytics. The API is part of the broader Amarsia platform, including assistants, knowledge bases with RAG, AI actions, and MCP server management, so it scales from a simple chat widget to complex agentic builds. Compared to Vercel AI SDK or LangChain, Amarsia manages memory itself—you don't own the persistence layer. The trade-off: you commit to a managed platform hosted in EU, US, or Asia Pacific regions, with tiered pricing based on credits and runs. For teams wanting minimal infrastructure overhead, this is a fast track to production.
Behind the Verdict
We've seen plenty of chat APIs, and most make you assemble the pieces yourself. The Conversation API stands apart because it treats memory as a core feature, not an afterthought. You get a conversation_id that automatically persists state, so you skip the database schema and session management entirely. For a startup shipping an MVP or a low-code team embedding chat into a product, that's a meaningful head start. Where it bites: it's fully managed. You can't bring your own database or deploy on-premise unless you spring for Enterprise. Data residency is limited to EU, US, and Asia Pacific, and fine-tuning is an Enterprise-only perk. If your compliance requirements demand specific control, this might not be your answer. Pricing is transparent: Demo at $0/mo, Sandbox at $49/mo, Production at $399/mo, Enterprise custom. The Sandbox tier's $0.06 overage per run can sneak up on you if you're hitting the 3,000-run limit regularly. The Production tier's pooled 20,000 runs and priority support are better for serious usage. The model pricing is credit-based, with light models like Haiku 4.5 costing 0.25 credits per run, while premium ones like Opus 4.6 with long context cost 3. Credits never expire, which is a nice touch. If you're used to fixed per-token pricing, this abstraction takes some getting used to. Compared to Vercel AI SDK, which gives you primitives and expects you to handle persistence yourself, Amarsia's 'it just works' approach saves real dev time. But you lose the flexibility of swapping in your own database or infrastructure. For teams happy to trading control for speed, this is a solid trade. Just read the data retention policy—four weeks after activity—before committing to production.
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Real-world workflow fit
Concrete scenarios for the personas Conversation API actually fits — and what changes day-one when you adopt it.
Building a customer support chatbot for a small SaaS—wants persistent chat history without managing a database.
Outcome: Integrate the Conversation API with the SDK, get conversation_id for each user, and have memory across sessions in a day. Deploy on the free tier and scale to Sandbox when volume grows.
Needs to launch an AI FAQ assistant with document retrieval for the support site.
Outcome: Use the knowledge base with URL scraping to upload docs, and the Conversation API for chat. Deploy in a weekend, monitor via the dashboard, and refine with test cases.
A client wants a personalized concierge bot that remembers user preferences.
Outcome: Use the Conversation API's automatic memory and naming to manage threads per client. With React hooks, integrate quickly and bill the client on the Production plan with pooled runs.
Use Cases
- Add persistent chat history to a customer support widget without building a database
- Build a conversational AI assistant that remembers user preferences across sessions
- Prototype a ChatGPT-like interface for internal knowledge retrieval from uploaded documents
- Integrate stateful AI chat into a low-code platform or no-code tool like Bubble
- Create a tax research assistant for an accounting firm using private knowledge bases
- Develop a multi-turn AI agent that maintains context across complex workflows
Models Under the Hood
as of 2026-09-02
Limitations
- The free tier includes 150 monthly free credits on the Demo plan.
- Data automatically deleted four weeks after latest activity in each agentic run.
- Data residency options are US, EU, or Asia Pacific.
- Enterprise plan details and pricing require contacting sales.
as of 2026-08-24
Verification history
We have re-verified Conversation API 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Conversation API tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Demo
$0/mo
Ideal for
Exploring Amarsia for the first time, testing one assistant with up to 1,000 runs/month.
What this tier adds
Free entry point with 1 agentic assistant, 1,000 runs, 5 actions, 150 credits; no knowledge base, BYOK, or evaluations.
Sandbox
$49/mo
Ideal for
Early-stage teams validating an AI feature with BYOK support and test cases.
What this tier adds
Adds BYOK, evaluations & test cases, email support, 3,000 runs, 25 actions, 500 credits; overage $0.06/run.
Production
$399/mo
Ideal for
Teams running multiple assistants in production needing pooled runs and priority support.
What this tier adds
Adds 3 assistants, 20,000 pooled runs, 100 actions, priority 24h support, 2,000 credits; overage $0.045/run.
Enterprise
Custom quote
Ideal for
Organizations requiring custom infrastructure, fine-tuning, and dedicated support.
What this tier adds
Custom assistants, committed-use runs, knowledge base, fine-tuning, dedicated hosting, success manager.
Where the pricing makes sense
The company stage and team size where Conversation API's pricing actually pencils out — and where peers do it cheaper.
Amarsia Conversation API pricing is competitive for low-code builders: free tier with 1,000 runs, $49 Sandbox, and $399 Production. Compared to Vercel AI SDK (usage-based) or LangChain (open-source), Amarsia bundles memory and infrastructure, but you pay for managed convenience. The credit system lets you start free, but heavy usage costs more than DIY.
Setup time & first value
How long it actually takes to get something useful out of Conversation API — broken out by persona, not the marketing-page minute.
For a developer familiar with APIs, first value in under an hour: create an assistant, generate an API key, and call the endpoint. No-code users can attach the widget in minutes. Integration with the SDK takes a day; full production setup with evaluations and monitoring takes a few days.
Switching to or from Conversation API
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Vercel AI SDK: Replace your AI SDK calls with Amarsia API to get built-in persistence—no need to set up a database or manage conversation state.
- →From LangChain: Migrate your chain definitions to Amarsia assistants via the dashboard, and use the SDK to invoke conversations with memory.
- →From Dialogflow: Export your intents and re-implement as Amarsia assistants; the API handles session state automatically.
- ↗To Vercel AI SDK: You'll need to handle persistence yourself—add a database and manage threads manually.
- ↗To LangChain: Use LangChain's memory modules to replicate stateful chat, but you'll own the whole stack.
- ↗To a custom solution: Export conversation logs and build your own backend with your choice of LLM providers.
Integrations
Resources & Guides
- Documentationamarsia.com
Docs · Conversation API
Full product docs from amarsia.com
- Conceptsamarsia.com
Concepts · Conversation API
Core ideas explained from amarsia.com
- Documentationamarsia.com
Api Reference · Conversation API
Full product docs from amarsia.com
- Documentationamarsia.com
Features · Conversation API
Full product docs from amarsia.com
- Documentationamarsia.com
Integrations · Conversation API
Full product docs from amarsia.com
- Resourceamarsia.com
Changelog · Conversation API
Helpful link from amarsia.com
Tutorials & Learning
Official links
Tools that pair well with Conversation API
Common stack mates teams adopt alongside Conversation API, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Conversation Api vs Spider Cloud
Choose Spider Cloud if you need to feed live web data into AI agents or RAG pipelines—its Rust engine and AI extraction are purpose-built for structured scraping at scale. Choose Conversation API if you want to drop a stateful chat interface into your app with zero backend setup, auto-stored conversation history, and free unlimited usage. They solve different problems: one fetches external data, the other manages conversational memory.
Conversation Api vs Voyage Ai
If you're building domain-specific search or RAG with high accuracy on finance/legal documents, Voyage AI's specialized embedding models and rerankers are unmatched. But for quickly adding stateful chat to an app without backend setup, Conversation API's free sandbox and persistent memory are a no-brainer. Choose based on your primary need: retrieval accuracy vs conversational turnkey.
Conversation Api vs Temporal Ai
Choose Temporal AI if you are building production-grade AI agents or complex microservice workflows that demand durability, automatic retries, and full execution visibility. Choose Conversation API if you need to add stateful AI chat to your app with minimal backend effort and zero infrastructure management, ideal for rapid prototyping and low-code teams. Temporal offers more power and flexibility but requires significant setup; Conversation API trades depth for simplicity and speed.
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