Hopscotch vs Koreshield: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hopscotch and Koreshield — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Hopscotch
Hopscotch Labs
OpenAI-compatible LLM gateway giving one API and one balance for 500+ models, with fallbacks and spend limits per key, teammate and workspace.
Key features
- Unified OpenAI-Compatible API: Call 500+ models from Anthropic, OpenAI, Google and others by changing only the base URL, API key and model name in the OpenAI SDK.
- Routing Profiles and Fallbacks: Define an ordered list of backup models so requests fail over automatically when a provider errors or returns a 429.
- Spend Limits: Set resetting credit limits per API key, monthly limits per teammate and workspace, and a cap on how fast the account can spend.
- Activity Log and Usage Analytics: See every request with serving provider, outcome, tokens, cost and latency, export to CSV, and break spend down by model, key and teammate.
- Model Catalog with Upstream Pricing: Each model lists context window and per-million-token price, including multiple upstream providers for open-weight models.
- Side-by-Side Playground: Run one prompt on up to three models and compare answers and cost before changing code.
- Bring Your Own Keys: Use your own provider keys free with no monthly cap alongside Hopscotch credit.
Best for
- Multi-Provider Apps: Letting an application switch between Claude, GPT and Gemini without maintaining separate SDKs, accounts or bills.
- Resilient Production Traffic: Automatically falling back to another model when a primary provider is rate-limited or down.
- Team Budget Governance: Giving each developer or environment its own key with a spend ceiling so costs cannot run away.
- Model Evaluation: Comparing output quality and cost of several models on real prompts in the playground before switching.
- Cost Auditing: Exporting per-request logs to attribute LLM spend by model, provider, key and teammate.
Koreshield
Koreshield Labs Ltd.
Runtime trust layer that scans customer input, retrieved context and proposed tool calls in AI support workflows before they become trusted instructions.
Key features
- Customer Input Scanning: Inspects messages, attachments and externally controlled text before model execution so injected instructions never reach the model as trusted input.
- Retrieved Context Screening: Keeps poisoned tickets, CRM notes and RAG documents from inheriting system-level authority inside the prompt.
- Proposed Action Evaluation: Checks tool calls against trust, approval and authorization limits before they are executed.
- Detect and Enforce Modes: Run in detect mode to record threats without interrupting traffic, then enable enforcement only where fallback behavior is ready.
- Decision Evidence Trail: Records severity, decision and reason for every request, retained for 7 or 30 days depending on plan, for review and audits.
- Scan API with Key Management: Server-side API keys and endpoints such as /v1/rag/scan make integration with existing support stacks straightforward.
- Flexible Deployment: Use the hosted console or run the FastAPI + PostgreSQL service in your own infrastructure, with VPC, self-hosted and air-gapped options for Enterprise.
Best for
- Prompt Injection Defense: Blocking malicious instructions hidden in customer messages before an AI support agent acts on them.
- RAG Poisoning Protection: Preventing tampered knowledge-base articles or ticket histories from overriding system prompts.
- Agent Action Guardrails: Holding refunds, account changes or other tool calls for policy review when trust limits are exceeded.
- Safe Production Rollout: Measuring real threat and false-positive rates in detect mode before enforcing on live traffic.
- Security Audits: Exporting decision evidence to show auditors and security teams why each request was allowed or held.