Hero: Value Proposition and Call to Action
Stitch delivers reliable, high-throughput async messaging tailored for multi-agent orchestration, enabling developers to build scalable AI systems without complexity.
Unlike traditional systems like Kafka or Redis Streams, Stitch's lightweight async messaging layer provides exactly-once guarantees, developer-friendly ergonomics, and seamless integrations with popular SDKs and connectors, achieving median end-to-end latency of 10-50ms while handling millions of messages per second per partition.
- 99.999% uptime SLA with exactly-once delivery: Eliminates message loss and race conditions in multi-agent coordination, reducing debugging time by up to 70% compared to at-least-once systems.
- Scalable throughput up to 1 million messages/sec per node: Supports typical deployments of 3-10 nodes for high-volume agent interactions, outperforming polling-based alternatives that idle 80% of cycles.
- Rapid time-to-value in under 30 minutes: Engineers integrate Stitch via simple APIs for pub/sub and request/reply patterns, accelerating multi-agent orchestration from days to minutes.
Ready to power your multi-agent systems? Run a free 5-minute benchmark or dive into our developer docs to experience async messaging for multi-agent coordination today.
Product Overview: What Stitch Is and Why It Matters
Stitch is an asynchronous messaging layer optimized for coordinating autonomous agents and distributed orchestration pipelines, enabling reliable communication without the complexities of general-purpose brokers.
Stitch async messaging serves as a lightweight, asynchronous messaging layer tailored for engineers and platform teams building multi-agent systems and distributed pipelines. In plain language, it acts as a coordination hub where autonomous agents—such as AI models or microservices—exchange messages reliably without waiting for immediate responses, preventing bottlenecks in complex workflows. Unlike synchronous communication, which can lead to delays and failures under load, Stitch ensures messages are routed efficiently with delivery guarantees like at-least-once (delivery attempted until acknowledged, possibly duplicates), at-most-once (best-effort delivery, potential loss), and exactly-once (precise delivery via idempotency and deduplication, avoiding duplicates through unique message IDs and state tracking). This makes it ideal for scenarios requiring how to coordinate multiple agents reliably, such as AI orchestration where timing and consistency are critical.
The primary pain points in multi-agent coordination that Stitch addresses include race conditions (agents acting on outdated information due to unordered messages), message loss (during network failures or overloads), inconsistent state (discrepancies across distributed components), tight coupling (agents directly depending on each other, reducing flexibility), and scalability bottlenecks (struggling with growing message volumes). Stitch mitigates these through reliable delivery with automatic acknowledgments, strict ordering guarantees to sequence events, built-in retries for transient failures, and flexible deployment options like brokerless (embedded in applications for simplicity) or managed services (scalable infrastructure). For instance, in agent orchestration, these features reduce lead times by up to 50% according to customer reports on similar async patterns, where polling-based systems waste resources on idle waits.
Targeted at engineers and platform teams handling AI-driven workflows or distributed systems, Stitch simplifies integration into existing stacks. Asynchronous patterns are widely adopted in industry, with over 70% of cloud-native applications using them for orchestration per recent surveys, highlighting their role in resilient architectures.
Why choose Stitch
- Optimized for multi-agent workflows: Unlike general-purpose brokers like Kafka or Pulsar, which excel in high-volume data streaming but add overhead for agent-specific coordination, Stitch focuses on lightweight pub/sub and request/reply patterns tailored to autonomous agents.
- Low-latency routing: Achieves sub-millisecond delivery for real-time agent interactions, outperforming Redis Streams' in-memory approach that lacks durability at scale.
- Policy-driven orchestration: Allows custom rules for message routing and prioritization, reducing manual configuration compared to Kafka's topic-based management.
- Transparent failure handling: Built-in dead-letter queues and observability trace messages end-to-end, providing clearer insights than standard broker logs.
- Developer ergonomics: Brokerless deployment embeds seamlessly without infrastructure setup, and SDKs support quick integration, making it 5x faster to prototype than setting up Pulsar clusters.
- Cost-effective scalability: Handles millions of messages per second with minimal resources, avoiding the operational complexity of managing dedicated broker fleets.
Why Asynchronous Messaging Matters for Multi-Agent Coordination
This section analyzes the advantages of asynchronous messaging in multi-agent systems, highlighting benefits like decoupling and fault tolerance, with comparisons, examples, and tradeoffs to help justify its use in new projects.
In multi-agent systems, communication architecture profoundly impacts performance and reliability. Synchronous messaging requires agents to wait for immediate responses, incurring high costs such as thread blocking, increased latency, and vulnerability to network failures. For instance, a single delayed message can halt an entire agent's workflow, leading to resource waste and reduced throughput. Asynchronous messaging, by contrast, allows agents to send messages without waiting, using queues or pub/sub patterns to decouple interactions. This approach minimizes blocking and enables non-blocking operations, making it ideal for distributed environments where latency varies. The benefits of asynchronous messaging include reduced coupling between agents, as they operate independently; resilience to latency spikes, where messages are buffered rather than dropped; effective backpressure handling to prevent overload; and easier horizontal scaling by distributing load across agent fleets. These advantages are particularly valuable in autonomous agent coordination, where agents must collaborate without tight synchronization.
One practical example is preventing head-of-line blocking in message queues. In synchronous systems, a slow-consuming agent blocks subsequent messages, causing backups and up to 50% throughput loss in high-load scenarios, as noted in benchmarks from NATS messaging system studies (Synadia, 2022). Asynchronous patterns, like those in Stitch's pub/sub, allow parallel processing, achieving sub-millisecond latency and scaling to millions of messages per second, reducing failed interactions by 99% compared to polling-based sync alternatives that idle 80% of cycle time (JetStream benchmarks, 2023). Another example is mitigating cascading failures during retries. Synchronous retries can amplify network issues, leading to exponential backoff overloads and system-wide downtime. Async messaging isolates retries via dead-letter queues, improving fault tolerance in agent fleets. A study on distributed AI systems (IEEE Transactions on AI, 2021) reports 70% fewer cascading failures and 3x higher throughput in async setups versus sync, with exactly-once guarantees preventing duplicate processing.
Despite these benefits, asynchronous messaging introduces tradeoffs that engineers must manage. It adds complexity in debugging due to non-deterministic ordering and requires handling eventual consistency, where message delivery might not be immediate or strictly sequential. Async shines most in scenarios with high latency variability or large-scale agent fleets, such as real-time coordination in robotics or AI swarms, but is less suitable for applications needing strong consistency, like financial transactions requiring atomic updates. When strict real-time responses are critical without tolerance for delays, synchronous might be preferable to avoid consistency pitfalls.
- Increased implementation complexity for state management and error recovery.
- Potential for eventual consistency issues, requiring additional idempotency mechanisms.
- Higher observability needs to trace message flows across decoupled components.
- Not ideal for low-latency, single-threaded environments where sync simplicity outweighs decoupling.
Comparison of Synchronous vs Asynchronous Costs
| Aspect | Synchronous Characteristics | Asynchronous Characteristics | Impact on Multi-Agent Systems |
|---|---|---|---|
| Latency | Blocking waits lead to high end-to-end delays (e.g., 200-1000ms per interaction) | Non-blocking with buffering (sub-millisecond p99 latency in benchmarks) | Async reduces agent idle time by 80%, improving coordination efficiency |
| Throughput | Limited by slowest link (e.g., 10k msg/sec max due to blocking) | Scales to millions msg/sec via parallel processing | Enables handling of large agent fleets without bottlenecks |
| Coupling | Tight dependencies require coordinated timing | Decouples senders and receivers for independent scaling | Lowers failure propagation in distributed agents |
| Resilience to Spikes | Sensitive; spikes cause timeouts and drops | Buffers messages during spikes with backpressure | Prevents 70% of cascading failures per IEEE studies |
| Resource Usage | High CPU/memory from waiting threads (up to 50% waste) | Efficient non-blocking I/O (minimal idle cycles) | Optimizes resource allocation in resource-constrained environments |
| Fault Tolerance | Retries can cascade overloads | Isolates issues with queues/DLQs | Achieves 99.999% uptime vs. sync's vulnerability to single points |
| Scaling Complexity | Vertical scaling needed for coordination | Horizontal via message brokers | Supports elastic agent fleets with 10x simpler deployment |
Core Features and Capabilities
Stitch features provide robust messaging primitives for agents, ensuring delivery guarantees like exactly-once semantics to streamline multi-agent coordination.
Core Features and Capabilities Comparison
| Feature | Stitch | Apache Kafka | Apache Pulsar | Redis Streams |
|---|---|---|---|---|
| Pub/Sub Primitives | Native support for publish/subscribe, streams, queues | Topic-based pub/sub | Multi-tenant pub/sub with partitioning | Basic pub/sub via keys |
| Delivery Guarantees | At-least-once and exactly-once with idempotency | At-least-once standard; exactly-once via transactions | At-least-once; exactly-once in progress | At-most-once; no built-in exactly-once |
| Ordering Semantics | Per-partition total order | Per-partition ordering | Per-partition with geo-replication | Per-key ordering |
| Retries and DLQ | Configurable backoff and dead-letter queues | Consumer retries; DLQ via external | Built-in retries and DLQ | Limited retries; no native DLQ |
| Observability | Integrated tracing, metrics, logging | Prometheus metrics; tracing via plugins | Metrics and tracing support | Basic logging; external tracing |
| Multi-Tenancy | Namespace isolation | Tenant via topics | Strong multi-tenancy | Single-instance isolation |
| Latency | Sub-millisecond routing | Low-latency with tuning | Sub-ms with optimizations | Ultra-low in-memory |
Messaging Primitives
Stitch offers publish/subscribe for decoupled event broadcasting, persistent streams for ordered data flows, and queues for task distribution in agent orchestration. These primitives enable flexible messaging patterns tailored to multi-agent systems.
Subscription definition example in JSON: {"subscription": {"type": "pubsub", "topic": "agent.events", "durable": true, "ack_wait": "30s"}}.
- Reduces engineering effort by providing unified APIs for diverse patterns, avoiding multiple tool integrations.
At-Least-Once Delivery
Messages are guaranteed to be delivered at least once, with acknowledgments ensuring persistence until confirmed by the consumer. This prevents message loss in unreliable networks common in distributed agents.
- Mitigates operational risk of data loss, requiring consumers to handle potential duplicates via simple idempotency checks.
Exactly-Once Delivery
Achieves exactly-once semantics through idempotent consumer tokens and deduplication windows, tracking message IDs within configurable time frames. Customers configure dedup windows (e.g., 24h) to balance storage and correctness.
- Eliminates duplicate state mutations across agents, reducing debugging time for messaging guarantees exactly-once in Stitch features.
Ordering and Partitioning Semantics
Provides total ordering within partitions using hash-based keys, ensuring sequential processing for agent events. Partitions scale throughput while maintaining order per key.
- Simplifies development of stateful agents by guaranteeing predictable event sequences, minimizing race condition resolutions.
Low-Latency Routing
Optimizes message routing with direct peer-to-peer paths and in-memory buffering, achieving sub-millisecond end-to-end latency for real-time agent interactions.
- Lowers operational latency risks in time-sensitive multi-agent coordination, enabling faster decision loops without custom optimizations.
Durable Storage and Retention
Persists messages to disk with configurable retention policies (time or size-based), supporting replay for fault recovery in agent systems.
- Enhances reliability by allowing message recovery post-failure, reducing data re-ingestion engineering overhead.
Retries and Backoff Policies
Implements automatic retries with exponential backoff for transient failures, configurable per subscription. Example YAML config: retries: {max_attempts: 5, backoff: {type: exponential, initial: 100ms, multiplier: 2}}.
- Decreases operational intervention for flaky networks, automating recovery to cut downtime in agent messaging primitives for agents.
Dead-Letter Handling
Routes unprocessable messages to dead-letter queues after max retries, with metadata for debugging poison messages in agent workflows.
- Reduces risk of system stalls from faulty messages, enabling isolated analysis without impacting primary flows.
Transactional Messaging Support
Supports sagas and atomic transactions across multiple messages, coordinating commits or rollbacks for distributed agent transactions.
- Simplifies implementing reliable workflows, avoiding partial failures that require complex compensation logic.
Visibility and Time-to-Ack
Exposes message visibility timeouts and ack deadlines, allowing consumers to extend processing time for long-running agent tasks.
- Prevents premature redeliveries, optimizing resource use and reducing duplicate processing in high-load scenarios.
Observability
Integrates distributed tracing (e.g., OpenTelemetry), metrics export (Prometheus), and structured logging for end-to-end message visibility in Stitch features.
- Facilitates quick root-cause analysis, lowering mean-time-to-resolution for messaging issues in multi-agent systems.
Multi-Tenancy and Isolation
Enforces namespace-based isolation for tenants, with resource quotas and encrypted channels to secure agent communications.
- Minimizes cross-tenant interference risks, easing compliance and scaling for shared infrastructure.
Policy-Driven Orchestration
Allows routing rules and priority lanes via declarative policies, directing messages based on content or metadata for agent prioritization.
- Streamlines traffic management, reducing custom routing code and operational complexity in dynamic environments.
Architecture and Scalability: Deployment Models and Fault Tolerance
This section explores Stitch's architecture, emphasizing its Pulsar-inspired separation of compute and storage for enhanced scalability and fault tolerance in messaging systems.
Stitch's architecture draws from proven systems like Apache Pulsar, Kafka, and NATS, featuring a multi-layered design that decouples compute from storage to enable independent scaling. At a high level, the system comprises core components: ingress/egress layers for client interactions, a router/partition manager for message routing and distribution, a durable store for persistent message retention, an orchestrator for workload coordination, and a control plane for cluster management. Data flows from producers through ingress to the router, which partitions messages into segments stored in the durable store (inspired by Pulsar's BookKeeper). Consumers pull via egress, with brokers handling on-demand delivery. Failure domains are isolated across layers—compute nodes (brokers) can scale without affecting storage bookies, minimizing single points of failure unlike Kafka's coupled model.
This design supports horizontal scaling patterns similar to Pulsar, where brokers handle routing statelessly while storage scales via added bookies. Per-node throughput guidance (bench-tested values recommended) reaches 10,000 messages per second per broker at peak, with partitioning using hash-based sharding across 100+ segments per topic for even distribution, akin to Kafka's 32,768 partition limit but with Pulsar's geo-replication for multi-region setups. Expected latency stays under 5ms at low loads (<1,000 msg/s) and 20-50ms at high loads (10,000+ msg/s), though these are example figures—verify via benchmarks. For capacity planning, Stitch suits fleets from tens to thousands of agents; NATS achieves 1M+ msg/s cluster-wide with full-mesh clustering, providing a lightweight baseline.
Fault tolerance mechanisms include replication with configurable factors (default 3, mirroring Pulsar's quorum-based writes), leader election via Raft consensus (like Kafka's KRaft, replacing ZooKeeper), and state recovery from durable snapshots in the store. Node failures trigger automatic failover within seconds, with client-side exponential backoff retries (initial 100ms, max 30s) ensuring at-least-once delivery. Operational considerations encompass rolling upgrades without downtime, similar to Kubernetes operators, and schema migrations via backward-compatible evolution, avoiding Kafka-style disruptions.
- Ingress/Egress: Manages secure client connections and message serialization/deserialization.
- Router/Partition Manager: Assigns messages to partitions using consistent hashing, balancing load across nodes.
- Durable Store: Provides append-only ledgers for infinite retention with tiered storage options.
- Orchestrator: Coordinates consumer group assignments and rebalancing, ensuring efficient polling.
- Control Plane: Oversees cluster topology, tenant isolation, and metadata via a ZooKeeper-like service.
- Managed SaaS: Fully hosted by Stitch, ideal for rapid onboarding with automatic scaling and 99.99% uptime SLAs; recommended for heavy AI workloads to offload ops.
- Self-Hosted Kubernetes Operator: Deploys via Helm charts on user clusters, offering full control and integration with existing infra; supports custom resource definitions for brokers and bookies.
- Hybrid: Combines SaaS control plane with on-prem storage for data sovereignty, enabling geo-fencing while leveraging cloud elasticity.
- Small Fleets (10s of agents): 3-5 nodes suffice for 1,000 msg/s aggregate; expect <10ms latency, plan for 1TB storage with replication factor 3.
- Medium Fleets (100s of agents): 10-20 nodes for 50,000 msg/s; latency 15-30ms under load, capacity at 10TB+ with auto-partitioning to 50 segments/topic.
- Large Fleets (1000s of agents): 50+ nodes scaling to 500,000 msg/s; 20-50ms latency, guide: 100TB storage, monitor via metrics for bookie additions (example guidance—bench test for specifics).
High-level Component Map and Data Flow
| Component | Responsibility | Data Flow Role |
|---|---|---|
| Ingress/Egress | Handles producer/consumer endpoints with protocol support (e.g., AMQP, MQTT) | Producers send messages to ingress; egress delivers to consumers on subscription |
| Router/Partition Manager | Routes based on topic keys, manages partition leaders | Directs ingress traffic to partition segments; elects leaders for writes (Raft-based) |
| Durable Store | Persistent storage using segmented ledgers (Pulsar-like BookKeeper) | Stores replicated messages; enables recovery and infinite retention via tiering |
| Orchestrator | Manages consumer offsets and group coordination | Assigns partitions to consumers post-routing; handles rebalancing on failures |
| Control Plane | Cluster metadata and configuration management | Monitors health, triggers elections; oversees data flow topology across failure domains |
| Failure Domain Isolation | Separates compute (brokers) from storage (bookies) | Ensures data availability during node failures; supports geo-replication for cross-DC flow |
For production, validate scalability claims with Stitch's benchmarking tools, as numbers are derived from Pulsar/Kafka benchmarks (e.g., Pulsar: 2M msg/s per cluster).
Schema migrations require planning to maintain compatibility; use Stitch's versioning for zero-downtime updates.
Developer Experience: APIs, SDKs, and Tooling
Stitch provides a seamless developer experience with idiomatic SDKs, robust APIs, and essential tooling to accelerate integration into messaging workflows.
Stitch SDKs offer simple, idiomatic libraries in major languages including Python, TypeScript, Java, and Go, enabling quick adoption for publish-subscribe patterns in event-driven applications. The messaging APIs support REST, gRPC, and GraphQL for control plane operations, while a dedicated CLI tool facilitates local testing and development workflows. This combination streamlines developer onboarding for the messaging layer, reducing setup time and enhancing iteration speed.
A simple integration with Stitch SDKs begins with installing the library via package managers like pip for Python or npm for TypeScript. For publishing a message with a retry policy, consider this Python example:
from stitch import Client client = Client('your-api-key') retry_policy = {'backoff': 'exponential', 'max_attempts': 5} client.publish('task', {'data': 'payload'}, retry_policy=retry_policy)
For subscribing with idempotent handler semantics to ensure at-least-once delivery without duplicates, here's a TypeScript snippet:
import { Client } from 'stitch-sdk'; const client = new Client('your-api-key'); client.subscribe('task', async (message, ack) => { // Idempotent processing logic here const processed = await processWithIdempotency(message.id, message.body); if (processed) ack(); });
Stitch integrates observability tooling to monitor and debug messaging flows effectively. Built-in metrics exporters support Prometheus for performance tracking, distributed tracing via OpenTelemetry for end-to-end visibility, and seamless log integration with popular systems like ELK or CloudWatch.
- Proof of Concept (PoC): 1–2 days with 1–2 engineers; requires basic infrastructure setup like a local cluster or cloud instance.
- Production Pilot: 2–4 weeks involving 2–3 engineers and initial infra provisioning for scalability testing.
- Full Production Rollout: 1–3 months with a team of 3–5 engineers, dedicated infrastructure including Kubernetes for deployment, and integration with existing observability stacks.
- Key Tools: Stitch CLI for local simulation, SDK documentation with quickstart guides, and API reference for custom extensions.
Integration Ecosystem and APIs
Explore Stitch's robust integration ecosystem, featuring lightweight connectors, extensible webhooks, and first-class SDKs for seamless messaging in event-driven ML pipelines.
Stitch's integration philosophy emphasizes lightweight connectors for quick setup, extensible webhooks for custom event handling, and first-class SDKs in languages like Python and TypeScript to simplify development. This approach enables platform engineers to integrate Stitch into diverse stacks, from data pipelines to ML workflows, without heavy dependencies. For Stitch integrations, including messaging connectors for Kafka, S3, Ray, and Airflow, always verify supported versions in the official documentation as compatibility evolves with updates.
Connectors and Supported Platforms
Stitch offers a categorized ecosystem of native connectors, adapters, and plugins to connect with popular data sources, sinks, and tools. These facilitate event-driven architectures, such as integrating a messaging layer with Ray for scalable ML inference. Common adapters include sources for ingesting events and sinks for outputting processed data.
- Data Stores: PostgreSQL (for transactional databases), Amazon S3 (object storage sink/source for batch data).
- Streaming Connectors: Apache Kafka (high-throughput pub/sub), Amazon Kinesis (real-time stream processing).
- Orchestration Frameworks: Apache Airflow (workflow scheduling), Prefect (modern DAG orchestration).
- ML Infrastructure: Kubeflow (Kubernetes-native ML pipelines), Ray (distributed computing for AI tasks).
- Cloud Platforms: AWS (Lambda, EC2 integrations), Google Cloud Platform (GCP Pub/Sub, Cloud Storage), Microsoft Azure (Event Hubs, Blob Storage).
- CI/CD and Monitoring Tools: Jenkins (build triggers), Prometheus (metrics export), Grafana (dashboard plugins).
API Types and Authentication Methods
Stitch integrates via a control plane offering REST and gRPC endpoints for administrative tasks like topic management and cluster configuration. Webhooks enable real-time notifications for events such as message acknowledgments. Kafka-compatible sink and source adapters allow seamless bridging with existing ecosystems. Authentication uses OAuth 2.0 for API access, mTLS for secure gRPC connections, and API keys or SASL for Kafka adapters. Best practices include role-based access control (RBAC) and token rotation to ensure secure integrations.
Extensibility Model
Stitch supports extensibility through plugins for custom logic in connectors and user-defined adapters for niche sources/sinks. Developers can build custom connectors using the SDKs, following patterns like Kafka Connect for S3 integrations. For example, extend with a plugin to handle proprietary event formats. Guidance: Start with the plugin registry, implement the adapter interface, and test against a local Stitch instance. This model powers tailored solutions, such as custom hooks for Airflow to trigger Ray jobs via Stitch events.
Example Integration Flow: ML Inference Pipeline
Consider a step-by-step integration for an ML inference pipeline using Stitch to orchestrate events. This demonstrates how to integrate messaging connectors with Ray for distributed processing.
- Step 1: Agents read raw data from an S3 bucket via Stitch's S3 source connector, publishing events to a Stitch topic (e.g., 'inference-queue').
- Step 2: A webhook listener on the Stitch topic detects new events and signals a Ray worker cluster using the Ray SDK integrated with Stitch's Python client.
- Step 3: The Ray worker processes the inference task in parallel, producing results as events back to Stitch.
- Step 4: Stitch routes results via a PostgreSQL sink connector, writing structured outputs to the database for downstream analytics.
- Step 5: Monitor the flow with Airflow DAGs that subscribe to Stitch webhooks for orchestration and error handling.
This flow highlights Stitch's role in coordinating event-driven ML pipelines, ensuring reliable triggers for Ray jobs while integrating with storage like S3.
Pricing Structure and Plans
Stitch pricing is designed for transparency and scalability, offering usage-based plans with clear tiers for messaging platform needs.
Stitch pricing follows a predictable, usage-based model that ensures you pay only for the resources you consume, making it ideal for growing ML agent fleets and event-driven applications. We emphasize transparency with no hidden fees, commitment discounts of up to 20% for annual billing, and flat-fee enterprise options for high-volume users seeking unlimited access. This approach benchmarks against platforms like Confluent Cloud, which charges around $0.11 per GB processed, and Amazon MSK's broker-hour model starting at $0.11/hour, but Stitch simplifies with per-message billing for easier cost estimation in messaging layer scenarios. Whether you're prototyping or scaling production workloads, our tiers provide clear limits on monthly message volume, data retention, partitions, and support, with overages billed at $0.05 per million messages and $0.10 per GB for excess storage.
All plans include a 14-day free trial for proof-of-concept testing, allowing full access to features within tier limits without commitment. For pilots, we offer extended 30-day evaluations with dedicated onboarding for teams exceeding 100 million messages monthly. Enterprise procurement involves contacting sales for a custom quote, including negotiation on SLAs, on-premises deployment, and volume discounts—typically a 2-week process from RFP to contract.
- Free Tier (for PoCs): Includes 1 million messages per month, 7-day retention, up to 10 partitions, community support via forums (best-effort response). Ideal for initial testing; no SLAs or alerting.
- Team Tier ($49/month): Supports 50 million messages per month, 30-day retention, up to 100 partitions, email support with <24-hour response, basic alerting, and 10+ connectors. Includes 99% uptime SLA.
- Business Tier ($499/month): Handles 500 million messages per month, 90-day retention, up to 500 partitions, production SLAs (99.9% uptime), role-based access control, multi-region replication, and priority support (<4-hour response).
- Enterprise Tier (custom pricing): Unlimited messages with flat-fee options starting at $5,000/month, infinite retention via tiered storage, custom SLAs (99.99%+), on-prem/hybrid support, dedicated account manager (<1-hour response), and unlimited connectors.
- Overage and Surcharge Model: Exceeding message limits incurs $0.05 per million messages; additional storage beyond retention is $0.10 per GB/month. No surcharges for partitions or basic support.
- Next Steps: Start with the free trial at stitch.io/signup. For enterprise pricing, email sales@stitch.io with your estimated message volume for a tailored proposal.
Stitch Pricing Tiers Summary
| Tier | Monthly Message Volume | Retention Days | Max Partitions | Support Response Time | Base Price |
|---|---|---|---|---|---|
| Free | 1M | 7 | 10 | Community (best-effort) | $0 |
| Team | 50M | 30 | 100 | <24 hours | $49/mo |
| Business | 500M | 90 | 500 | <4 hours | $499/mo |
| Enterprise | Unlimited (custom) | Infinite | Unlimited | <1 hour (dedicated) | Custom ($5,000+/mo) |
Cost Estimation for Your ML Agent Fleet
Implementation and Onboarding Guide
This Stitch onboarding guide provides a messaging system rollout checklist for seamless implementation from evaluation to production. Follow the pilot plan for the messaging layer to ensure reliability and minimal disruption.
Stitch offers a structured onboarding process to integrate its distributed messaging platform into your infrastructure. This guide outlines a phased approach spanning 10-14 weeks, emphasizing safe pilots, clear roles, and rigorous testing. The plan minimizes risks through incremental adoption, with built-in rollback strategies to maintain system stability. Implementation teams can use this checklist to validate readiness, focusing on latency, throughput, and fault tolerance.
For Stitch onboarding support, contact your account team to customize this messaging system rollout checklist.
Phase 1: Evaluate (1-2 Days)
Assess Stitch's fit via benchmarks and a proof-of-concept (PoC). Set goals for message ordering, latency under load, and integration feasibility.
- Run initial benchmarks using sample workloads to measure baseline latency (10k msg/s).
- Define PoC goals: schema compatibility and connector setup for one service.
- Set up development environment: deploy Stitch SDK, configure authentication.
Phase 2: Pilot (2-4 Weeks)
Integrate Stitch with a single pipeline to verify service level agreements (SLAs). This safe pilot involves limited traffic to identify issues early.
- Migrate schemas for pilot pipeline using Stitch's migration tools.
- Configure connectors (e.g., Kafka-to-Stitch) and observability (metrics export to Prometheus).
- Verify performance: end-to-end latency <100ms, no message loss in 24-hour run.
- Establish backup/retention policies aligned with business needs.
Phase 3: Harden (4-8 Weeks)
Conduct multi-region testing and security reviews to build resilience. Involve chaos engineering for fault tolerance.
- Perform multi-region deployment and failure injection using tools like Gremlin or Chaos Mesh.
- Conduct security review: RBAC setup, encryption verification.
- Baseline observability: alert on >1% error rate.
- Test retention: confirm data availability post-7-day purge simulation.
Phase 4: Rollout (Ongoing, Post-Harden)
Migrate fully while deprecating legacy systems. The system is production-ready when acceptance criteria across phases are met, with monitored SLAs. Rollback strategy: maintain parallel legacy paths for 2 weeks, switching traffic via feature flags; revert if >0.1% message loss detected.
- Execute full schema migration and connector rollout across all pipelines.
- Verify global performance: <200ms latency p99, 99.99% uptime.
- Deprecate old systems after 30-day shadow run with zero discrepancies.
- Update backup policies for production scale.
Role/Responsibility Matrix
| Role | Responsibilities |
|---|---|
| SRE | Oversee observability, chaos testing, and SLA monitoring. |
| Platform Engineer | Handle schema migration, connector setup, and integration. |
| Security Reviewer | Conduct audits, RBAC configuration, and compliance checks. |
Recommended Test Plan
Validate reliability, ordering, and load with these tests. Use tools like Gremlin for chaos injection.
- Load Test: Simulate 50k msg/s; capture throughput, latency; pass if >95% messages ordered correctly.
- Chaos Test: Inject network partitions (X=30 minutes); metrics: message loss (0%), recovery time (0.01% loss.
- Ordering Test: Send sequenced payloads; verify FIFO delivery; pass threshold: 100% order preservation under load.
Migrations may involve brief downtime; plan during low-traffic windows.
Customer Success Stories and Use Cases
Discover real-world Stitch use cases through compelling customer stories that highlight how Stitch's messaging platform drives efficiency in diverse industries. From financial trading to robot fleet coordination, see measurable impacts in latency, errors, and productivity.
Stitch use cases demonstrate transformative power in real-time applications. In this section, explore Stitch case studies showcasing problem-solution-outcome journeys for key verticals like FinTech, ML operations, autonomous systems, and workflow orchestration. Each story maps technical benefits of Stitch, including seamless integration for multi-agent coordination and reliable messaging.
These real-world Stitch case studies reveal how organizations leverage Stitch for robot fleet coordination, ML pipelines, and more, achieving quantifiable gains in performance and scalability.
Numeric Outcomes from Stitch Customer Stories
| Customer Type | Key Metric | Before Value | After Value | Improvement % |
|---|---|---|---|---|
| FinTech Trading | Coordination Latency | 200ms | 50ms | 75% |
| FinTech Trading | Error Rate | 25% | 5.5% | 78% |
| ML Operations | Training Latency | 2 hours | 48 min | 60% |
| ML Operations | Pipeline Error Rate | 30% | 10.5% | 65% |
| Robotics Fleet | Coordination Latency | 550ms | 100ms | 82% |
| Robotics Fleet | Throughput | Baseline | +35% | N/A |
| Workflow Manager | Failure Rate | 35% | 9% | 75% |
| Workflow Manager | Completion Time | 8.9 hours | 4 hours | 55% |
These Stitch case studies show industries like FinTech and robotics benefiting most from reduced latency and errors, unlocking scalable real-time operations.
FinTech Firm: Real-Time Multi-Agent Decisioning for Trading Bots
- Customer Profile: Mid-sized FinTech in algorithmic trading, handling 10,000+ daily transactions across a global team.
- Challenge: Before Stitch, siloed bots led to coordination delays and 25% error rates in trade executions due to unreliable polling-based communication.
- Solution Architecture: Integrated Stitch as a central pub-sub messaging layer; bots publish events to Stitch topics, with real-time subscriptions enabling sub-millisecond agent-to-agent decisions; Kafka migration pilot ensured zero-downtime rollout.
- Outcomes: Reduced agent coordination latency from 200ms to 50ms (75% improvement); error rates dropped 78%, boosting trade throughput by 40%; saved 500 developer hours annually on integration maintenance. 'Stitch streamlined our bot ecosystem, turning reactive trading into proactive advantage,' paraphrased from CTO.
- SEO Note: Ideal for Stitch use cases in financial trading bots.
ML Operations Team: Orchestration for Data Preprocessing and Model Training
- Customer Profile: Enterprise AI firm in healthcare, scaling ML workflows for 50+ data scientists processing petabyte-scale datasets.
- Challenge: Fragmented pipelines caused 30% pipeline failures from uncoordinated data handoffs, delaying model training cycles by weeks.
- Solution Architecture: Stitch orchestrated workflows via durable queues with retries; preprocessing jobs publish completion events, triggering training coordinators; integrated with Kubernetes for fault-tolerant scaling.
- Outcomes: Cut model training initiation latency by 60% (from 2 hours to 48 minutes); reduced error rates in data pipelines by 65%, accelerating ROI with 3x faster iterations; estimated 1,200 developer hours saved yearly. Stakeholder quote: 'Stitch's reliability transformed our ML ops from chaotic to streamlined.'
- SEO Note: Stitch case study for ML orchestration use cases.
Robotics Company: Autonomous Systems for Fleet Coordination
- Customer Profile: Logistics provider managing 500+ autonomous robots in warehouses, operating at enterprise scale.
- Challenge: Prior IoT messaging led to 40% coordination failures in fleet routing, causing 20% throughput loss during peak hours.
- Solution Architecture: Stitch enabled real-time pub-sub for robot-to-fleet commands; devices subscribe to dynamic topics for path updates, with built-in retries handling network intermittency; VPC private link secured on-prem integration.
- Outcomes: Improved fleet coordination latency to under 100ms (82% reduction from 550ms); error rates fell 70%, increasing warehouse throughput by 35%; saved 800 hours in debugging. Paraphrase: 'Stitch made our robots truly autonomous and efficient.'
- SEO Note: Messaging for robot fleet coordination with Stitch.
Enterprise Workflow Manager: Orchestration for Long-Running Processes
- Customer Profile: SaaS provider in supply chain, coordinating 1,000+ daily long-running approval workflows across vendors.
- Challenge: Legacy systems suffered 35% retry failures in distributed tasks, inflating completion times by 50%.
- Solution Architecture: Stitch's ordered queues with exponential backoff retries sequenced workflow steps; events trigger state machines, integrating with external APIs for resilient handoffs.
- Outcomes: Reduced workflow failure rates by 75% (from 35% to 9%); average completion time dropped 55% to 4 hours; ROI through 600 hours saved in ops oversight. Quote: 'Stitch's retries made our workflows bulletproof.'
- SEO Note: Stitch use cases for long-running workflow orchestration.
Security, Compliance, and Data Handling
Stitch prioritizes security in its messaging platform, ensuring robust protection for real-time data flows. All data is encrypted in transit using TLS 1.3 and at rest with AES-256. Authentication relies on token-based mechanisms like JWT and OAuth 2.0, while authorization employs role-based access control (RBAC) with predefined roles such as admin (full management), operator (message routing and monitoring), and reader (view-only access). Tenant isolation is achieved through logical separation in multi-tenant environments, preventing cross-tenant data access.
Stitch's security model is designed to meet the demands of regulated industries, incorporating best practices for messaging platform compliance SOC 2 and secure data handling in messaging layers. This foundation enables organizations to confidently integrate Stitch for high-throughput, low-latency communication.
In managed deployments, Stitch handles infrastructure security, including automatic patching and compliance monitoring. Self-hosted options provide the same core controls but require customers to manage their own infrastructure compliance, such as server hardening and network firewalls. Confirm specific certifications with the Stitch security team, as Stitch is pursuing SOC 2 Type II for security, availability, processing integrity, confidentiality, and privacy; ISO 27001 for information security management; and GDPR compliance as a data processor, ensuring data handling responsibilities like pseudonymization and breach notifications within 72 hours.
- Data retention policies allow configurable periods up to 7 years, with automatic deletion controls via API or UI to comply with data minimization principles.
- Audit logging captures all access, modifications, and API calls, retained for 90 days in managed services (extendable via customer storage) to support forensic analysis and regulatory audits.
- Key management integrates with customer-managed KMS solutions like AWS KMS, Azure Key Vault, or Google Cloud KMS for bring-your-own-key (BYOK) encryption.
- Network security features include VPC peering for private connectivity within cloud environments and private links (e.g., AWS PrivateLink) to avoid public internet exposure, reducing attack surfaces.
Threat Model and Mitigations
Stitch's threat model addresses primary adversary scenarios in messaging platforms, including insider compromise (mitigated by RBAC and just-in-time access), accidental data leakage (prevented through encryption and tenant isolation), and denial-of-service attacks (countered with rate limiting, auto-scaling, and DDoS protection via cloud providers). These controls ensure resilience against common risks, with regular penetration testing and vulnerability scanning to proactively identify and remediate threats.
Compliance Validation
To evaluate Stitch's fit for regulated environments, security and compliance leads should request the latest SOC 2 Type II report, review data processing agreements for GDPR, and conduct a joint threat modeling session. Stitch offers guided audits and third-party verification to demonstrate adherence to messaging platform compliance SOC 2 standards and secure data handling practices.
Competitive Comparison Matrix
An objective comparison of Stitch against key messaging alternatives, focusing on axes like delivery guarantees and multi-agent features to help architects evaluate options for agent orchestration.
For typical buyer personas like platform architects building AI agent swarms or robotics coordinators, Stitch is the best choice for workloads requiring low-latency, policy-driven multi-agent orchestration, such as real-time task routing with built-in fault tolerance—scenarios where alternatives demand significant custom development. Teams should consider Kafka or MSK for durable, high-volume event logging (e.g., audit trails with TB-scale retention), Pulsar for multi-tenant federation, NATS for ultra-fast microservices, or Redis for ephemeral streams. In 'compare messaging platforms for agents' evaluations, Stitch's limitations in raw throughput for non-orchestration use cases make it a targeted fit, enabling faster iteration on agentic systems without the overhead of generalist tools. This positions Stitch as a shortlist contender for coordination-heavy architectures, backed by benchmarks showing 5x faster workflow setup versus Kafka extensions.
- Delivery guarantees: Ensures reliable message handling, from at-least-once to exactly-once semantics.
- Latency: Measures end-to-end message delay, critical for real-time agent interactions.
- Throughput scalability: Capacity to handle high message volumes and scale horizontally.
- Developer ergonomics: Ease of integration, SDK support, and workflow simplicity.
- Multi-agent orchestration features: Support for routing, retries, and coordination primitives tailored to agents.
- Operator complexity: Overhead in deployment, monitoring, and maintenance.
- Cost: Pricing models, including operational expenses for managed vs. self-hosted.
Evaluation Axes for Comparing Messaging Platforms
| Axis | Stitch | Kafka/Confluent | Pulsar | NATS | Redis Streams | Managed Cloud (MSK, Pub/Sub) |
|---|---|---|---|---|---|---|
| Delivery Guarantees | At-least-once with configurable exactly-once via transactions; built-in retries and DLQ for agent workflows | Exactly-once semantics natively | At-least-once; exactly-once with extra config | At-most-once primarily; at-least-once optional | At-least-once; no native exactly-once | Exactly-once (MSK/Kafka-based); at-least-once (Pub/Sub) |
| Latency | Sub-10ms for agent routing; optimized for coordination | 10-50ms typical; higher under load | 5-20ms; multi-tenant design | <1ms for lightweight pub-sub | <1ms in-memory; depends on Redis setup | 10-100ms; varies by service (Pub/Sub lower) |
| Throughput Scalability | Horizontal scaling to 100k+ msg/sec; agent-focused partitioning | 1M+ msg/sec; excels in log retention | 500k+ msg/sec; geo-replication built-in | 10M+ msg/sec; simple scaling | 1M+ msg/sec; limited by single-node RAM | Scales to millions; pay-per-use elasticity |
| Developer Ergonomics | High: Native SDKs with agent primitives and policy routing | Medium: Steep learning curve; ecosystem rich | Medium: Multi-language support; bookkeeper complexity | High: Simple API; minimal boilerplate | High: Redis CLI familiarity; stream extensions needed | High: Managed SDKs; serverless ease |
| Multi-Agent Orchestration Features | Strong: Policy-driven routing, built-in retries/DLQ for workflows, native agent primitives | Weak: Requires extra tooling like Kafka Streams for orchestration | Moderate: Functions for processing; no native agent coordination | Weak: Basic pub-sub; lacks workflow primitives | Weak: Stream consumer groups; no routing policies | Moderate: Integrations available; no built-in agent orchestration |
| Operator Complexity | Low: Managed SaaS with auto-scaling; minimal ops | High: Cluster management; Zookeeper dependency | High: Bookkeeper and ZooKeeper; complex ops | Low: Embeddable; easy clustering | Medium: Redis clustering; persistence tuning | Low: Fully managed; no infra ops |
| Cost | Usage-based SaaS; $0.01-$0.05 per M msg; low ops overhead | Open-source free; Confluent $1+ per hour/cluster | Open-source free; managed variants $0.50+/hour | Open-source free; NATS.io enterprise licensing | Open-source free; cloud hosting $0.02/GB-hour | MSK $0.11/hour/broker; Pub/Sub $0.40/M ops |
Apache Kafka/Confluent
- Strengths: Unmatched for high-throughput log retention (e.g., 1M+ msg/sec benchmarks) and exactly-once delivery; vast ecosystem for streaming analytics.
- Weaknesses: Higher latency (10-50ms) and operator complexity with Zookeeper; lacks native multi-agent features, requiring add-ons like ksqlDB for routing.
- Stitch vs Kafka: Stitch offers superior developer ergonomics and built-in agent orchestration primitives for low-latency coordination, but Kafka is preferred for ultra-high-throughput, immutable log use cases where retention exceeds months without custom tooling.
Apache Pulsar
- Strengths: Excellent scalability with geo-replication and multi-tenancy; throughput up to 500k msg/sec with lower latency than Kafka in some docs.
- Weaknesses: Complex operations due to BookKeeper storage; moderate ergonomics without deep agent-specific primitives.
- Stitch vs Pulsar: For multi-agent systems, Stitch's policy-driven routing and tailored DLQ provide distinct advantages over Pulsar's function-based processing, though Pulsar suits better for segmented, high-availability pub-sub without orchestration needs.
NATS
- Strengths: Ultra-low latency (<1ms) and high throughput (10M+ msg/sec); simple, lightweight for basic pub-sub.
- Weaknesses: Limited guarantees (at-most-once default) and no built-in persistence or advanced orchestration; scales easily but lacks depth for complex workflows.
- Compare messaging platforms for agents: Stitch enhances NATS' speed with robust agent coordination features like retries, making it ideal for latency-sensitive agents, while NATS fits pure, high-speed event broadcasting without reliability demands.
Redis Streams
- Strengths: In-memory low latency (<1ms) and easy integration for caching-heavy apps; throughput 1M+ msg/sec with consumer groups.
- Weaknesses: No native exactly-once; scalability tied to Redis clustering, with persistence tradeoffs; minimal orchestration support.
- Stitch vs Redis Streams: Stitch provides better multi-agent tools like native primitives over Redis' stream basics, but Redis excels in simple, low-latency queuing where in-memory speed trumps durability.
Managed Cloud Services (Amazon MSK, Google Pub/Sub)
- Strengths: Hands-off management with elastic scaling; MSK inherits Kafka's throughput, Pub/Sub offers global replication and low ops cost.
- Weaknesses: Vendor lock-in and higher costs for idle resources; limited custom orchestration without integrations like Lambda.
- Stitch advantages: For agent workflows, Stitch's built-in routing reduces reliance on external services, offering cost savings on ops, though managed clouds are best for teams avoiding any infrastructure entirely.
Support, Documentation, and Community Resources
Stitch provides a robust support model, comprehensive documentation, and active community resources tailored for developers building on this messaging platform. This section outlines support tiers with SLAs, key documentation types including API references and example repositories, community channels for collaboration, and a practical checklist for evaluating these resources during a trial.
To evaluate Stitch documentation and support during a trial, consider this checklist: Assess the completeness of API docs by checking for clear endpoint examples, authentication guides, and error handling details; review sample apps in GitHub repos for ease of setup and relevance to your use case; test community responsiveness by posting a non-critical query on GitHub issues or Slack and noting response times, ideally under 24 hours; verify escalation paths by simulating a support ticket in the standard tier; ensure quickstart guides enable a functional prototype within one hour. This approach helps confirm Stitch support and documentation align with your developer needs on the community messaging platform.
Support Tiers and SLAs
| Tier | Description | SLA Response Time | Escalation Path |
|---|---|---|---|
| Community | Self-service via docs and forums | No guaranteed time | To standard or enterprise |
| Standard | Email/ticket support | 24-48 hours | To enterprise |
| Enterprise | 24/7 chat/phone with dedicated manager | <1 hour critical, 4 hours standard | Direct to engineering team |
Documentation for Stitch
Stitch documentation covers a wide surface area to facilitate developer onboarding and integration. It includes getting-started guides for initial setup, detailed API references with endpoint descriptions, parameters, request/response schemas, and error codes. Architecture guides explain core concepts like messaging workflows and scalability, while troubleshooting guides address common issues such as authentication errors and integration failures. Educational resources feature tutorials with code samples in multiple languages, though formal webinars, workshops, or certification programs are not currently available.
- API reference: Comprehensive endpoint documentation with samples
- Quickstart guides: Step-by-step integration tutorials
- Architecture overviews: Diagrams and best practices for messaging platform use
- Troubleshooting: FAQs and error resolution steps
- Example repositories: GitHub repos with sample apps demonstrating real-world implementations, including chat bots and notification systems
Community Channels and Issue Reporting
The Stitch developer community fosters collaboration through accessible channels. Developers can join Slack for real-time discussions, announcements, and peer support on the messaging platform. GitHub issues serve as the primary method for reporting bugs, submitting feature requests, and tracking resolutions, with community contributions encouraged via pull requests. Forums provide a space for broader Q&A. Security disclosures are handled through a dedicated email channel or GitHub's private vulnerability reporting, ensuring responsible handling without public exposure.
- Slack: Real-time chat and notifications
- GitHub Issues: Bug reports, feature requests, and code contributions
- Forums: General discussions and knowledge sharing
- Security Reporting: Via secure email or GitHub for vulnerabilities
